no code implementations • ECCV 2020 • Guangyi Chen, Yuhao Lu, Jiwen Lu, Jie Zhou
Experimental results demonstrate that our DCML method explores credible and valuable training data and improves the performance of unsupervised domain adaptation.
no code implementations • ECCV 2020 • Ziwei Wang, Quan Zheng, Jiwen Lu, Jie zhou
n this paper, we propose a Deep Hashing method with Active Pairwise Supervision(DH-APS).
no code implementations • ECCV 2020 • Wenzhao Zheng, Jiwen Lu, Jie zhou
We employ a metric model and a layout encoder to map the RGB images and the ground-truth layouts to the embedding space, respectively, and a layout decoder to map the embeddings to the corresponding layouts, where the whole framework is trained in an end-to-end manner.
no code implementations • ECCV 2020 • Guangyi Chen, Yongming Rao, Jiwen Lu, Jie zhou
Specifically, we disentangle the video representation into the temporal coherence and motion parts and randomly change the scale of the temporal motion features as the adversarial noise.
no code implementations • ECCV 2020 • Liangliang Ren, Yangyang Song, Jiwen Lu, Jie zhou
Unlike most existing works that define room layout on a 2D image, we model the layout in 3D as a configuration of the camera and the room.
no code implementations • ECCV 2020 • Yu Zheng, Danyang Zhang, Sinan Xie, Jiwen Lu, Jie zhou
In this paper, we propose a Rotation-robust Intersection over Union ($ extit{RIoU}$) for 3D object detection, which aims to jointly learn the overlap of rotated bounding boxes.
1 code implementation • 1 Jun 2024 • Yixuan Zhu, Wenliang Zhao, Ao Li, Yansong Tang, Jie zhou, Jiwen Lu
Image enhancement holds extensive applications in real-world scenarios due to complex environments and limitations of imaging devices.
1 code implementation • 30 May 2024 • Lening Wang, Wenzhao Zheng, Yilong Ren, Han Jiang, Zhiyong Cui, Haiyang Yu, Jiwen Lu
To address this, we propose a diffusion-based 4D occupancy generation model, OccSora, to simulate the development of the 3D world for autonomous driving.
1 code implementation • 27 May 2024 • Yuanhui Huang, Wenzhao Zheng, Yunpeng Zhang, Jie zhou, Jiwen Lu
To address this, we propose an object-centric representation to describe 3D scenes with sparse 3D semantic Gaussians where each Gaussian represents a flexible region of interest and its semantic features.
1 code implementation • 27 May 2024 • Shuai Zeng, Wenzhao Zheng, Jiwen Lu, Haibin Yan
While conventional methods focus on generating pseudo-labels for unlabeled samples as supplements for training, the structural nature of 3D point cloud data facilitates the composition of objects and backgrounds to synthesize realistic scenes.
1 code implementation • 6 May 2024 • Zheng Zhu, XiaoFeng Wang, Wangbo Zhao, Chen Min, Nianchen Deng, Min Dou, Yuqi Wang, Botian Shi, Kai Wang, Chi Zhang, Yang You, Zhaoxiang Zhang, Dawei Zhao, Liang Xiao, Jian Zhao, Jiwen Lu, Guan Huang
General world models represent a crucial pathway toward achieving Artificial General Intelligence (AGI), serving as the cornerstone for various applications ranging from virtual environments to decision-making systems.
1 code implementation • 23 Apr 2024 • Shuofeng Sun, Yongming Rao, Jiwen Lu, Haibin Yan
However, we contend that such implicit high-dimensional structure modeling approch inadequately represents the local geometric structure of point clouds due to the absence of explicit structural information.
1 code implementation • 22 Apr 2024 • Shiyi Zhang, Sule Bai, Guangyi Chen, Lei Chen, Jiwen Lu, Junle Wang, Yansong Tang
NAE is a more challenging task because it requires both narrative flexibility and evaluation rigor.
3 code implementations • CVPR 2023 • Shiyi Zhang, Wenxun Dai, Sujia Wang, Xiangwei Shen, Jiwen Lu, Jie zhou, Yansong Tang
Action quality assessment (AQA) has become an emerging topic since it can be extensively applied in numerous scenarios.
1 code implementation • 1 Apr 2024 • Yixuan Zhu, Ao Li, Yansong Tang, Wenliang Zhao, Jie zhou, Jiwen Lu
The recovery of occluded human meshes presents challenges for current methods due to the difficulty in extracting effective image features under severe occlusion.
1 code implementation • 23 Mar 2024 • Hancheng Ye, Chong Yu, Peng Ye, Renqiu Xia, Yansong Tang, Jiwen Lu, Tao Chen, Bo Zhang
Recent Vision Transformer Compression (VTC) works mainly follow a two-stage scheme, where the importance score of each model unit is first evaluated or preset in each submodule, followed by the sparsity score evaluation according to the target sparsity constraint.
1 code implementation • 19 Mar 2024 • Zuyan Liu, Yuhao Dong, Yongming Rao, Jie zhou, Jiwen Lu
In the realm of vision-language understanding, the proficiency of models in interpreting and reasoning over visual content has become a cornerstone for numerous applications.
Ranked #57 on Visual Question Answering on MM-Vet
no code implementations • 16 Mar 2024 • Zhiheng Li, Muheng Li, Jixuan Fan, Lei Chen, Yansong Tang, Jie zhou, Jiwen Lu
Scale arbitrary super-resolution based on implicit image function gains increasing popularity since it can better represent the visual world in a continuous manner.
no code implementations • 13 Mar 2024 • Guanxing Lu, Shiyi Zhang, Ziwei Wang, Changliu Liu, Jiwen Lu, Yansong Tang
Then, we build a Gaussian world model to parameterize the distribution in our dynamic Gaussian Splatting framework, which provides informative supervision in the interactive environment via future scene reconstruction.
no code implementations • 11 Mar 2024 • Xiuwei Xu, Chong Xia, Ziwei Wang, Linqing Zhao, Yueqi Duan, Jie zhou, Jiwen Lu
To this end, we propose an adapter-based plug-and-play module for the backbone of 3D scene perception model, which constructs memory to cache and aggregate the extracted RGB-D features to empower offline models with temporal learning ability.
1 code implementation • 5 Mar 2024 • JianJian Cao, Peng Ye, Shengze Li, Chong Yu, Yansong Tang, Jiwen Lu, Tao Chen
To this end, we propose a novel framework named Multimodal Alignment-Guided Dynamic Token Pruning (MADTP) for accelerating various VLTs.
1 code implementation • 19 Jan 2024 • Borui Zhang, Wenzhao Zheng, Jie zhou, Jiwen Lu
Rigorousness and clarity are both essential for interpretations of DNNs to engender human trust.
no code implementations • 18 Jan 2024 • XiaoFeng Wang, Zheng Zhu, Guan Huang, Boyuan Wang, Xinze Chen, Jiwen Lu
World models play a crucial role in understanding and predicting the dynamics of the world, which is essential for video generation.
1 code implementation • 14 Dec 2023 • Chubin Zhang, Juncheng Yan, Yi Wei, Jiaxin Li, Li Liu, Yansong Tang, Yueqi Duan, Jiwen Lu
As a fundamental task of vision-based perception, 3D occupancy prediction reconstructs 3D structures of surrounding environments.
no code implementations • 12 Dec 2023 • Guanxing Lu, Ziwei Wang, Changliu Liu, Jiwen Lu, Yansong Tang
Embodied Instruction Following (EIF) requires agents to complete human instruction by interacting objects in complicated surrounding environments.
1 code implementation • 1 Dec 2023 • Xiaoke Huang, JianFeng Wang, Yansong Tang, Zheng Zhang, Han Hu, Jiwen Lu, Lijuan Wang, Zicheng Liu
We propose a method to efficiently equip the Segment Anything Model (SAM) with the ability to generate regional captions.
1 code implementation • 27 Nov 2023 • Wenzhao Zheng, Weiliang Chen, Yuanhui Huang, Borui Zhang, Yueqi Duan, Jiwen Lu
In this paper, we explore a new framework of learning a world model, OccWorld, in the 3D Occupancy space to simultaneously predict the movement of the ego car and the evolution of the surrounding scenes.
1 code implementation • 21 Nov 2023 • Yuanhui Huang, Wenzhao Zheng, Borui Zhang, Jie zhou, Jiwen Lu
Our SelfOcc outperforms the previous best method SceneRF by 58. 7% using a single frame as input on SemanticKITTI and is the first self-supervised work that produces reasonable 3D occupancy for surround cameras on nuScenes.
1 code implementation • 2 Nov 2023 • Borui Zhang, Baotong Tian, Wenzhao Zheng, Jie zhou, Jiwen Lu
Shapley values have emerged as a widely accepted and trustworthy tool, grounded in theoretical axioms, for addressing challenges posed by black-box models like deep neural networks.
1 code implementation • NeurIPS 2023 • Yinan Liang, Ziwei Wang, Xiuwei Xu, Yansong Tang, Jie zhou, Jiwen Lu
Due to the high price and heavy energy consumption of GPUs, deploying deep models on IoT devices such as microcontrollers makes significant contributions for ecological AI.
no code implementations • 9 Oct 2023 • Zhenyu Wu, Xiuwei Xu, Ziwei Wang, Chong Xia, Linqing Zhao, Jiwen Lu, Haibin Yan
Existing methods only consider fixed frames of input data for a single detector, such as monocular RGB-D images or point clouds reconstructed from dense multi-view RGB-D images.
1 code implementation • ICCV 2023 • Zhiheng Li, Wenjia Geng, Muheng Li, Lei Chen, Yansong Tang, Jiwen Lu, Jie zhou
By this means, our model explores all sorts of reliable sub-relations within an action sequence in the condensed action space.
1 code implementation • ICCV 2023 • Junlong Li, Bingyao Yu, Yongming Rao, Jie zhou, Jiwen Lu
The core of our method consists of a global instance assignment strategy and a spatio-temporal enhancement module, which improve the temporal consistency of the features from two aspects.
no code implementations • 18 Sep 2023 • XiaoFeng Wang, Zheng Zhu, Guan Huang, Xinze Chen, Jiagang Zhu, Jiwen Lu
The established world model holds immense potential for the generation of high-quality driving videos, and driving policies for safe maneuvering.
2 code implementations • 11 Sep 2023 • Chengkun Wang, Wenzhao Zheng, Zheng Zhu, Jie zhou, Jiwen Lu
This paper proposes an introspective deep metric learning (IDML) framework for uncertainty-aware comparisons of images.
1 code implementation • 31 Aug 2023 • Sicheng Zuo, Wenzhao Zheng, Yuanhui Huang, Jie zhou, Jiwen Lu
To address this, we propose a cylindrical tri-perspective view to represent point clouds effectively and comprehensively and a PointOcc model to process them efficiently.
1 code implementation • ICCV 2023 • Ziyi Wang, Xumin Yu, Yongming Rao, Jie zhou, Jiwen Lu
In this paper, we propose a novel 3D-to-2D generative pre-training method that is adaptable to any point cloud model.
Ranked #6 on 3D Part Segmentation on ShapeNet-Part
1 code implementation • 4 Jul 2023 • Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan
Equipping embodied agents with commonsense is important for robots to successfully complete complex human instructions in general environments.
1 code implementation • 30 May 2023 • Changyuan Wang, Ziwei Wang, Xiuwei Xu, Yansong Tang, Jie zhou, Jiwen Lu
On the contrary, we design group-wise quantization functions for activation discretization in different timesteps and sample the optimal timestep for informative calibration image generation, so that our quantized diffusion model can reduce the discretization errors with negligible computational overhead.
1 code implementation • 5 May 2023 • Xiuwei Xu, Zhihao Sun, Ziwei Wang, Hongmin Liu, Jie zhou, Jiwen Lu
Specifically, we theoretically derive a dynamic spatial pruning (DSP) strategy to prune the redundant spatial representation of 3D scene in a cascade manner according to the distribution of objects.
1 code implementation • 28 Apr 2023 • Yuejian Wu, Linqing Zhao, Jiwen Lu, Haibin Yan
In addition to the shape and location constraints, we design a quality-aware classification loss to adaptively supervise each positive proposal so that the discriminative power can be further boosted.
1 code implementation • 13 Apr 2023 • Ziwei Wang, Jiwen Lu, Han Xiao, Shengyu Liu, Jie zhou
On the contrary, we obtain the optimal efficient networks by directly optimizing the compression policy with an accurate performance predictor, where the ultrafast automated model compression for various computational cost constraint is achieved without complex compression policy search and evaluation.
1 code implementation • 11 Apr 2023 • Hui Li, Tianyang Xu, Xiao-Jun Wu, Jiwen Lu, Josef Kittler
In particular we adopt a learnable representation approach to the fusion task, in which the construction of the fusion network architecture is guided by the optimisation algorithm producing the learnable model.
no code implementations • CVPR 2023 • Xiuwei Xu, Ziwei Wang, Jie zhou, Jiwen Lu
In this paper, we propose binary sparse convolutional networks called BSC-Net for efficient point cloud analysis.
1 code implementation • 23 Mar 2023 • Xiaoke Huang, Yiji Cheng, Yansong Tang, Xiu Li, Jie zhou, Jiwen Lu
Moreover, only minutes of optimization is enough for plausible reconstruction results.
2 code implementations • ICCV 2023 • Yi Wei, Linqing Zhao, Wenzhao Zheng, Zheng Zhu, Jie zhou, Jiwen Lu
Towards a more comprehensive perception of a 3D scene, in this paper, we propose a SurroundOcc method to predict the 3D occupancy with multi-camera images.
1 code implementation • ICCV 2023 • XiaoFeng Wang, Zheng Zhu, Wenbo Xu, Yunpeng Zhang, Yi Wei, Xu Chi, Yun Ye, Dalong Du, Jiwen Lu, Xingang Wang
Towards a comprehensive benchmarking of surrounding perception algorithms, we propose OpenOccupancy, which is the first surrounding semantic occupancy perception benchmark.
2 code implementations • ICCV 2023 • Wenliang Zhao, Yongming Rao, Zuyan Liu, Benlin Liu, Jie zhou, Jiwen Lu
In this paper, we propose VPD (Visual Perception with a pre-trained Diffusion model), a new framework that exploits the semantic information of a pre-trained text-to-image diffusion model in visual perception tasks.
Ranked #7 on Referring Expression Segmentation on RefCoCo val
no code implementations • 23 Feb 2023 • Zhenyu Wu, Ziwei Wang, Jiwen Lu, Haibin Yan
Then we fuse the feature maps representing the visual information of multi-view RGB images and the pixel affinity learned from the clutter point cloud, where the acquired instance segmentation masks of multi-view RGB images are projected to partition the clutter point cloud.
2 code implementations • CVPR 2023 • Yuanhui Huang, Wenzhao Zheng, Yunpeng Zhang, Jie zhou, Jiwen Lu
To lift image features to the 3D TPV space, we further propose a transformer-based TPV encoder (TPVFormer) to obtain the TPV features effectively.
Ranked #1 on Prediction Of Occupancy Grid Maps on nuScenes
1 code implementation • 11 Jan 2023 • Xumin Yu, Yongming Rao, Ziyi Wang, Jiwen Lu, Jie zhou
In this paper, we present a new method that reformulates point cloud completion as a set-to-set translation problem and design a new model, called PoinTr, which adopts a Transformer encoder-decoder architecture for point cloud completion.
Ranked #2 on Point Cloud Completion on ShapeNet
1 code implementation • CVPR 2023 • Shuai Shen, Wenliang Zhao, Zibin Meng, Wanhua Li, Zheng Zhu, Jie zhou, Jiwen Lu
In this way, the proposed DiffTalk is capable of producing high-quality talking head videos in synchronization with the source audio, and more importantly, it can be naturally generalized across different identities without any further fine-tuning.
1 code implementation • CVPR 2023 • Wenliang Zhao, Yongming Rao, Weikang Shi, Zuyan Liu, Jie zhou, Jiwen Lu
Unlike previous work that relies on carefully designed network architectures and loss functions to fuse the information from the source and target faces, we reformulate the face swapping as a conditional inpainting task, performed by a powerful diffusion model guided by the desired face attributes (e. g., identity and landmarks).
1 code implementation • CVPR 2023 • Chengkun Wang, Wenzhao Zheng, Junlong Li, Jie zhou, Jiwen Lu
Learning a generalizable and comprehensive similarity metric to depict the semantic discrepancies between images is the foundation of many computer vision tasks.
no code implementations • ICCV 2023 • Shuai Shen, Wanhua Li, Xiaobing Wang, Dafeng Zhang, Zhezhu Jin, Jie zhou, Jiwen Lu
Furthermore, we develop a neighbor-aware proxy generator that fuses the features describing various attributes into a proxy feature to build a bridge among different sub-clusters and reduce the intra-class variance.
1 code implementation • 18 Dec 2022 • Borui Zhang, Wenzhao Zheng, Jie zhou, Jiwen Lu
Deep learning has revolutionized human society, yet the black-box nature of deep neural networks hinders further application to reliability-demanded industries.
1 code implementation • CVPR 2023 • Yansong Tang, Jinpeng Liu, Aoyang Liu, Bin Yang, Wenxun Dai, Yongming Rao, Jiwen Lu, Jie zhou, Xiu Li
With the continuously thriving popularity around the world, fitness activity analytic has become an emerging research topic in computer vision.
1 code implementation • CVPR 2023 • Muheng Li, Yueqi Duan, Jie zhou, Jiwen Lu
With the rising industrial attention to 3D virtual modeling technology, generating novel 3D content based on specified conditions (e. g. text) has become a hot issue.
1 code implementation • 17 Nov 2022 • Haojun Jiang, Jianke Zhang, Rui Huang, Chunjiang Ge, Zanlin Ni, Jiwen Lu, Jie zhou, Shiji Song, Gao Huang
However, as pre-trained models are scaling up, fully fine-tuning them on text-video retrieval datasets has a high risk of overfitting.
no code implementations • 17 Nov 2022 • Sichao Huang, Ziwei Wang, Jie zhou, Jiwen Lu
We compare our approach with existing robotic packing methods for irregular objects in a physics simulator.
1 code implementation • 15 Nov 2022 • Chengkun Wang, Wenzhao Zheng, Xian Sun, Jiwen Lu, Jie zhou
We propose to learn a global probabilistic distribution for each pixel in the patch and a probabilistic metric to model the distance between distributions.
1 code implementation • 15 Oct 2022 • An Tao, Yueqi Duan, Yingqi Wang, Jiwen Lu, Jie zhou
To address this issue, we propose a Leaded Gradient Method (LGM) and show the significant effects of the lagged gradient.
1 code implementation • ICCV 2023 • Han Xiao, Wenzhao Zheng, Zheng Zhu, Jie zhou, Jiwen Lu
Data mixing strategies (e. g., CutMix) have shown the ability to greatly improve the performance of convolutional neural networks (CNNs).
1 code implementation • ICCV 2023 • Chengkun Wang, Wenzhao Zheng, Zheng Zhu, Jie zhou, Jiwen Lu
The pretrain-finetune paradigm in modern computer vision facilitates the success of self-supervised learning, which tends to achieve better transferability than supervised learning.
1 code implementation • 22 Aug 2022 • Yunpeng Zhang, Wenzhao Zheng, Zheng Zhu, Guan Huang, Jie zhou, Jiwen Lu
First, we extract multi-scale features and generate the perspective object proposals on each monocular image.
no code implementations • 7 Aug 2022 • Quan Zheng, Ziwei Wang, Jie zhou, Jiwen Lu
Explaining deep convolutional neural networks has been recently drawing increasing attention since it helps to understand the networks' internal operations and why they make certain decisions.
1 code implementation • 4 Aug 2022 • Ziyi Wang, Xumin Yu, Yongming Rao, Jie zhou, Jiwen Lu
Nowadays, pre-training big models on large-scale datasets has become a crucial topic in deep learning.
Ranked #18 on 3D Point Cloud Classification on ScanObjectNN (using extra training data)
7 code implementations • 28 Jul 2022 • Yongming Rao, Wenliang Zhao, Yansong Tang, Jie zhou, Ser-Nam Lim, Jiwen Lu
In this paper, we show that the key ingredients behind the vision Transformers, namely input-adaptive, long-range and high-order spatial interactions, can also be efficiently implemented with a convolution-based framework.
Ranked #20 on Semantic Segmentation on ADE20K
1 code implementation • 26 Jul 2022 • Cheng Ma, Jingyi Zhang, Jie zhou, Jiwen Lu
On the other hand, we propose a parallel network which includes two branches of cascaded lookup tables which process different components of the input low-resolution images.
1 code implementation • 24 Jul 2022 • Shuai Shen, Wanhua Li, Zheng Zhu, Yueqi Duan, Jie zhou, Jiwen Lu
Thus the facial radiance field can be flexibly adjusted to the new identity with few reference images.
1 code implementation • 18 Jul 2022 • Wanhua Li, Zhexuan Cao, Jianjiang Feng, Jie zhou, Jiwen Lu
As each sample is annotated with multiple attribute labels, these "words" will naturally form an unordered but meaningful "sentence", which depicts the semantic information of the corresponding sample.
1 code implementation • 17 Jul 2022 • Yansong Tang, Xingyu Liu, Xumin Yu, Danyang Zhang, Jiwen Lu, Jie zhou
Different from the conventional adversarial learning-based approaches for UDA, we utilize a self-supervision scheme to reduce the domain shift between two skeleton-based action datasets.
1 code implementation • 12 Jul 2022 • Wanhua Li, Jiwen Lu, Abudukelimu Wuerkaixi, Jianjiang Feng, Jie zhou
Unlike most existing personalized methods that learn the parameters of a personalized estimator for each person in the training set, our method learns the mapping from identity information to age estimator parameters.
Ranked #1 on Age Estimation on ChaLearn 2015
1 code implementation • 4 Jul 2022 • Yongming Rao, Zuyan Liu, Wenliang Zhao, Jie zhou, Jiwen Lu
We extend our method to hierarchical models including CNNs and hierarchical vision Transformers as well as more complex dense prediction tasks that require structured feature maps by formulating a more generic dynamic spatial sparsification framework with progressive sparsification and asymmetric computation for different spatial locations.
1 code implementation • CVPR 2022 • Han Xiao, Ziwei Wang, Zheng Zhu, Jie zhou, Jiwen Lu
Differentiable architecture search (DARTS) acquires the optimal architectures by optimizing the architecture parameters with gradient descent, which significantly reduces the search cost.
1 code implementation • 6 Jun 2022 • Wanhua Li, Xiaoke Huang, Zheng Zhu, Yansong Tang, Xiu Li, Jie zhou, Jiwen Lu
In this paper, we propose to learn the rank concepts from the rich semantic CLIP latent space.
Ranked #1 on Few-shot Age Estimation on MORPH Album2
1 code implementation • CVPR 2022 • Ziyi Wang, Yongming Rao, Xumin Yu, Jie zhou, Jiwen Lu
Conventional point cloud semantic segmentation methods usually employ an encoder-decoder architecture, where mid-level features are locally aggregated to extract geometric information.
1 code implementation • 19 May 2022 • Yunpeng Zhang, Zheng Zhu, Wenzhao Zheng, JunJie Huang, Guan Huang, Jie zhou, Jiwen Lu
Specifically, BEVerse first performs shared feature extraction and lifting to generate 4D BEV representations from multi-timestamp and multi-view images.
Ranked #15 on Robust Camera Only 3D Object Detection on nuScenes-C
2 code implementations • 9 May 2022 • Wenzhao Zheng, Chengkun Wang, Jie zhou, Jiwen Lu
This paper proposes an introspective deep metric learning (IDML) framework for uncertainty-aware comparisons of images.
no code implementations • ICCV 2021 • Xianda Guo, Zheng Zhu, Tian Yang, Beibei Lin, JunJie Huang, Jiankang Deng, Guan Huang, Jie zhou, Jiwen Lu
To the best of our knowledge, this is the first large-scale dataset for gait recognition in the wild.
no code implementations • 21 Apr 2022 • Zheng Zhu, Guan Huang, Jiankang Deng, Yun Ye, JunJie Huang, Xinze Chen, Jiagang Zhu, Tian Yang, Dalong Du, Jiwen Lu, Jie zhou
For a comprehensive evaluation of face matchers, three recognition tasks are performed under standard, masked and unbiased settings, respectively.
no code implementations • CVPR 2022 • Yu Zheng, Yueqi Duan, Jiwen Lu, Jie zhou, Qi Tian
A bathtub in a library, a sink in an office, a bed in a laundry room -- the counter-intuition suggests that scene provides important prior knowledge for 3D object detection, which instructs to eliminate the ambiguous detection of similar objects.
1 code implementation • 7 Apr 2022 • Yi Wei, Linqing Zhao, Wenzhao Zheng, Zheng Zhu, Yongming Rao, Guan Huang, Jiwen Lu, Jie zhou
In this paper, we propose a SurroundDepth method to incorporate the information from multiple surrounding views to predict depth maps across cameras.
2 code implementations • CVPR 2022 • Jinglin Xu, Yongming Rao, Xumin Yu, Guangyi Chen, Jie zhou, Jiwen Lu
Most existing action quality assessment methods rely on the deep features of an entire video to predict the score, which is less reliable due to the non-transparent inference process and poor interpretability.
no code implementations • IEEE Transactions on Image Processing 2022 • Wencheng Zhu, Yucheng Han, Jiwen Lu, Jie zhou
Then, we construct a temporal graph by using the aggregated representations of spatial graphs.
Ranked #1 on Video Summarization on TvSum (using extra training data)
1 code implementation • 28 Mar 2022 • Yi Wei, Zibu Wei, Yongming Rao, Jiaxin Li, Jie zhou, Jiwen Lu
In this paper, we propose the LiDAR Distillation to bridge the domain gap induced by different LiDAR beams for 3D object detection.
1 code implementation • CVPR 2022 • Borui Zhang, Wenzhao Zheng, Jie zhou, Jiwen Lu
This paper proposes an attributable visual similarity learning (AVSL) framework for a more accurate and explainable similarity measure between images.
Ranked #3 on Metric Learning on CARS196 (using extra training data)
1 code implementation • CVPR 2022 • Muheng Li, Lei Chen, Yueqi Duan, Zhilan Hu, Jianjiang Feng, Jie zhou, Jiwen Lu
The generated text prompts are paired with corresponding video clips, and together co-train the text encoder and the video encoder via a contrastive approach.
Ranked #5 on Action Segmentation on GTEA (using extra training data)
no code implementations • 26 Mar 2022 • Sha Yuan, Hanyu Zhao, Shuai Zhao, Jiahong Leng, Yangxiao Liang, Xiaozhi Wang, Jifan Yu, Xin Lv, Zhou Shao, Jiaao He, Yankai Lin, Xu Han, Zhenghao Liu, Ning Ding, Yongming Rao, Yizhao Gao, Liang Zhang, Ming Ding, Cong Fang, Yisen Wang, Mingsheng Long, Jing Zhang, Yinpeng Dong, Tianyu Pang, Peng Cui, Lingxiao Huang, Zheng Liang, HuaWei Shen, HUI ZHANG, Quanshi Zhang, Qingxiu Dong, Zhixing Tan, Mingxuan Wang, Shuo Wang, Long Zhou, Haoran Li, Junwei Bao, Yingwei Pan, Weinan Zhang, Zhou Yu, Rui Yan, Chence Shi, Minghao Xu, Zuobai Zhang, Guoqiang Wang, Xiang Pan, Mengjie Li, Xiaoyu Chu, Zijun Yao, Fangwei Zhu, Shulin Cao, Weicheng Xue, Zixuan Ma, Zhengyan Zhang, Shengding Hu, Yujia Qin, Chaojun Xiao, Zheni Zeng, Ganqu Cui, Weize Chen, Weilin Zhao, Yuan YAO, Peng Li, Wenzhao Zheng, Wenliang Zhao, Ziyi Wang, Borui Zhang, Nanyi Fei, Anwen Hu, Zenan Ling, Haoyang Li, Boxi Cao, Xianpei Han, Weidong Zhan, Baobao Chang, Hao Sun, Jiawen Deng, Chujie Zheng, Juanzi Li, Lei Hou, Xigang Cao, Jidong Zhai, Zhiyuan Liu, Maosong Sun, Jiwen Lu, Zhiwu Lu, Qin Jin, Ruihua Song, Ji-Rong Wen, Zhouchen Lin, LiWei Wang, Hang Su, Jun Zhu, Zhifang Sui, Jiajun Zhang, Yang Liu, Xiaodong He, Minlie Huang, Jian Tang, Jie Tang
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
1 code implementation • CVPR 2022 • Tianpei Gu, Guangyi Chen, Junlong Li, Chunze Lin, Yongming Rao, Jie zhou, Jiwen Lu
Human behavior has the nature of indeterminacy, which requires the pedestrian trajectory prediction system to model the multi-modality of future motion states.
2 code implementations • CVPR 2022 • Xiuwei Xu, Yifan Wang, Yu Zheng, Yongming Rao, Jie zhou, Jiwen Lu
In this paper, we propose a weakly-supervised approach for 3D object detection, which makes it possible to train a strong 3D detector with position-level annotations (i. e. annotations of object centers).
1 code implementation • 22 Jan 2022 • Mantang Guo, Junhui Hou, Jing Jin, Hui Liu, Huanqiang Zeng, Jiwen Lu
To this end, we propose content-aware warping, which adaptively learns the interpolation weights for pixels of a relatively large neighborhood from their contextual information via a lightweight neural network.
no code implementations • 20 Jan 2022 • Kun Song, Junwei Han, Gong Cheng, Jiwen Lu, Feiping Nie
In this paper, we reveal that metric learning would suffer from serious inseparable problem if without informative sample mining.
no code implementations • CVPR 2022 • Yunpeng Zhang, Wenzhao Zheng, Zheng Zhu, Guan Huang, Dalong Du, Jie zhou, Jiwen Lu
In this paper, we propose a general method to learn appropriate embeddings for dimension estimation in monocular 3D object detection.
2 code implementations • 22 Dec 2021 • Liang Pan, Tong Wu, Zhongang Cai, Ziwei Liu, Xumin Yu, Yongming Rao, Jiwen Lu, Jie zhou, Mingye Xu, Xiaoyuan Luo, Kexue Fu, Peng Gao, Manning Wang, Yali Wang, Yu Qiao, Junsheng Zhou, Xin Wen, Peng Xiang, Yu-Shen Liu, Zhizhong Han, Yuanjie Yan, Junyi An, Lifa Zhu, Changwei Lin, Dongrui Liu, Xin Li, Francisco Gómez-Fernández, Qinlong Wang, Yang Yang
Based on the MVP dataset, this paper reports methods and results in the Multi-View Partial Point Cloud Challenge 2021 on Completion and Registration.
1 code implementation • 17 Dec 2021 • An Tao, Yueqi Duan, He Wang, Ziyi Wu, Pengliang Ji, Haowen Sun, Jie zhou, Jiwen Lu
It results in a serious issue of lagged gradient, making the learned attack at the current step ineffective due to the architecture changes afterward.
1 code implementation • CVPR 2022 • Yongming Rao, Wenliang Zhao, Guangyi Chen, Yansong Tang, Zheng Zhu, Guan Huang, Jie zhou, Jiwen Lu
In this work, we present a new framework for dense prediction by implicitly and explicitly leveraging the pre-trained knowledge from CLIP.
2 code implementations • CVPR 2022 • Xumin Yu, Lulu Tang, Yongming Rao, Tiejun Huang, Jie zhou, Jiwen Lu
Inspired by BERT, we devise a Masked Point Modeling (MPM) task to pre-train point cloud Transformers.
Ranked #14 on Few-Shot 3D Point Cloud Classification on ModelNet40 5-way (10-shot) (using extra training data)
3D Point Cloud Linear Classification Few-Shot 3D Point Cloud Classification +2
1 code implementation • 17 Oct 2021 • Yinghuan Shi, Jian Zhang, Tong Ling, Jiwen Lu, Yefeng Zheng, Qian Yu, Lei Qi, Yang Gao
In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty.
1 code implementation • 26 Sep 2021 • Cheng Ma, Yongming Rao, Jiwen Lu, Jie zhou
Firstly, we propose SPSR with gradient guidance (SPSR-G) by exploiting gradient maps of images to guide the recovery in two aspects.
no code implementations • 6 Sep 2021 • Wanhua Li, Jiwen Lu, Abudukelimu Wuerkaixi, Jianjiang Feng, Jie zhou
To address this, we propose a Star-shaped Reasoning Graph Network (S-RGN).
Ranked #1 on Kinship Verification on KinFaceW-I
1 code implementation • ICCV 2021 • Yi Wei, Shaohui Liu, Yongming Rao, Wang Zhao, Jiwen Lu, Jie zhou
In this work, we present a new multi-view depth estimation method that utilizes both conventional reconstruction and learning-based priors over the recently proposed neural radiance fields (NeRF).
1 code implementation • 1 Sep 2021 • Haotong Qin, Yifu Ding, Xiangguo Zhang, Jiakai Wang, Xianglong Liu, Jiwen Lu
We first give a theoretical analysis that the diversity of synthetic samples is crucial for the data-free quantization, while in existing approaches, the synthetic data completely constrained by BN statistics experimentally exhibit severe homogenization at distribution and sample levels.
1 code implementation • ICCV 2021 • Wenzhao Zheng, Borui Zhang, Jiwen Lu, Jie zhou
This paper presents a deep relational metric learning (DRML) framework for image clustering and retrieval.
1 code implementation • ICCV 2021 • Xumin Yu, Yongming Rao, Ziyi Wang, Zuyan Liu, Jiwen Lu, Jie zhou
In this paper, we present a new method that reformulates point cloud completion as a set-to-set translation problem and design a new model, called PoinTr that adopts a transformer encoder-decoder architecture for point cloud completion.
Ranked #1 on Point Cloud Completion on ShapeNet (Chamfer Distance L2 metric)
1 code implementation • ICCV 2021 • Yongming Rao, Guangyi Chen, Jiwen Lu, Jie zhou
Unlike most existing methods that learn visual attention based on conventional likelihood, we propose to learn the attention with counterfactual causality, which provides a tool to measure the attention quality and a powerful supervisory signal to guide the learning process.
Ranked #8 on Vehicle Re-Identification on VehicleID Medium
2 code implementations • ICCV 2021 • Yongming Rao, Benlin Liu, Yi Wei, Jiwen Lu, Cho-Jui Hsieh, Jie zhou
In particular, we propose to generate random layouts of a scene by making use of the objects in the synthetic CAD dataset and learn the 3D scene representation by applying object-level contrastive learning on two random scenes generated from the same set of synthetic objects.
1 code implementation • ICCV 2021 • Xumin Yu, Yongming Rao, Wenliang Zhao, Jiwen Lu, Jie zhou
Assessing action quality is challenging due to the subtle differences between videos and large variations in scores.
Ranked #2 on Action Quality Assessment on MTL-AQA
no code implementations • 16 Aug 2021 • Zheng Zhu, Guan Huang, Jiankang Deng, Yun Ye, JunJie Huang, Xinze Chen, Jiagang Zhu, Tian Yang, Jia Guo, Jiwen Lu, Dalong Du, Jie zhou
There are second phase of the challenge till October 1, 2021 and on-going leaderboard.
1 code implementation • ICCV 2021 • Wenliang Zhao, Yongming Rao, Ziyi Wang, Jiwen Lu, Jie zhou
Our method is model-agnostic, which can be applied to off-the-shelf backbone networks and metric learning methods.
Ranked #16 on Metric Learning on CUB-200-2011
1 code implementation • 11 Aug 2021 • Guangyi Chen, Tianpei Gu, Jiwen Lu, Jin-An Bao, Jie zhou
Experimental results demonstrate the superiority of our method, which outperforms the state-of-the-art methods by a large margin with limited computational cost.
Ranked #21 on Person Re-Identification on MSMT17
1 code implementation • ICCV 2021 • Ziwei Wang, Yunsong Wang, Ziyi Wu, Jiwen Lu, Jie zhou
In this paper, we propose an instance similarity learning (ISL) method for unsupervised feature representation.
1 code implementation • ICCV 2021 • Ziwei Wang, Han Xiao, Jiwen Lu, Jie zhou
On the contrary, our GMPQ searches the mixed-quantization policy that can be generalized to largescale datasets with only a small amount of data, so that the search cost is significantly reduced without performance degradation.
1 code implementation • ICCV 2021 • Guangyi Chen, Junlong Li, Jiwen Lu, Jie zhou
Most existing methods learn to predict future trajectories by behavior clues from history trajectories and interaction clues from environments.
1 code implementation • ICCV 2021 • Guangyi Chen, Junlong Li, Nuoxing Zhou, Liangliang Ren, Jiwen Lu
In this paper, we present a distribution discrimination (DisDis) method to predict personalized motion patterns by distinguishing the potential distributions.
1 code implementation • 4 Jul 2021 • Linqing Zhao, Jiwen Lu, Jie zhou
To address this, we employ a late fusion strategy where we first learn the geometric and contextual similarities between the input and back-projected (from 2D pixels) point clouds and utilize them to guide the fusion of two modalities to further exploit complementary information.
Ranked #21 on Semantic Segmentation on ScanNet
4 code implementations • NeurIPS 2021 • Yongming Rao, Wenliang Zhao, Zheng Zhu, Jiwen Lu, Jie zhou
Recent advances in self-attention and pure multi-layer perceptrons (MLP) models for vision have shown great potential in achieving promising performance with fewer inductive biases.
Ranked #9 on Image Classification on Stanford Cars (using extra training data)
no code implementations • CVPR 2021 • Guoli Wang, Jiaqi Ma, Qian Zhang, Jiwen Lu, Jie zhou
Many of them settle it by generating fake frontal faces from extreme ones, whereas they are tough to maintain the identity information with high computational consumption and uncontrolled disturbances.
1 code implementation • CVPR 2021 • Shuyan Li, Xiu Li, Jiwen Lu, Jie zhou
Most existing unsupervised video hashing methods are built on unidirectional models with less reliable training objectives, which underuse the correlations among frames and the similarity structure between videos.
1 code implementation • CVPR 2021 • Wenzhao Zheng, Chengkun Wang, Jiwen Lu, Jie zhou
In this paper, we propose a deep compositional metric learning (DCML) framework for effective and generalizable similarity measurement between images.
1 code implementation • CVPR 2021 • Shuai Shen, Wanhua Li, Zheng Zhu, Guan Huang, Dalong Du, Jiwen Lu, Jie zhou
To address the dilemma of large-scale training and efficient inference, we propose the STructure-AwaRe Face Clustering (STAR-FC) method.
1 code implementation • NeurIPS 2021 • Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie zhou, Cho-Jui Hsieh
Based on this observation, we propose a dynamic token sparsification framework to prune redundant tokens progressively and dynamically based on the input.
Ranked #3 on Efficient ViTs on ImageNet-1K (With LV-ViT-S)
1 code implementation • 17 May 2021 • Yi Wei, Shang Su, Jiwen Lu, Jie zhou
To tackle this problem, we propose frustum-aware geometric reasoning (FGR) to detect vehicles in point clouds without any 3D annotations.
no code implementations • 6 Apr 2021 • Jiabin Zhang, Zheng Zhu, Jiwen Lu, JunJie Huang, Guan Huang, Jie zhou
To make a better trade-off between accuracy and efficiency, we propose a novel multi-person pose estimation framework, SIngle-network with Mimicking and Point Learning for Bottom-up Human Pose Estimation (SIMPLE).
3 code implementations • CVPR 2021 • Yunpeng Zhang, Jiwen Lu, Jie zhou
The precise localization of 3D objects from a single image without depth information is a highly challenging problem.
Ranked #8 on Monocular 3D Object Detection on KITTI Cars Moderate
no code implementations • CVPR 2021 • Wanhua Li, Shiwei Wang, Jiwen Lu, Jianjiang Feng, Jie zhou
In the end, the samples in the unbalanced train batch are re-weighted by the learned meta-miner to optimize the kinship models.
Ranked #1 on Kinship Verification on KinFaceW-II
1 code implementation • CVPR 2021 • Wanhua Li, Xiaoke Huang, Jiwen Lu, Jianjiang Feng, Jie zhou
An ordinal distribution constraint is proposed to exploit the ordinal nature of regression.
Ranked #2 on Age Estimation on Adience
Aesthetics Quality Assessment Age And Gender Classification +3
1 code implementation • 24 Mar 2021 • Shuai Shen, Wanhua Li, Zheng Zhu, Guan Huang, Dalong Du, Jiwen Lu, Jie zhou
To address the dilemma of large-scale training and efficient inference, we propose the STructure-AwaRe Face Clustering (STAR-FC) method.
no code implementations • CVPR 2021 • Zheng Zhu, Guan Huang, Jiankang Deng, Yun Ye, JunJie Huang, Xinze Chen, Jiagang Zhu, Tian Yang, Jiwen Lu, Dalong Du, Jie zhou
In this paper, we contribute a new million-scale face benchmark containing noisy 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) training data, as well as an elaborately designed time-constrained evaluation protocol.
Ranked #1 on Face Verification on IJB-C (training dataset metric)
1 code implementation • 18 Feb 2021 • Wencheng Zhu, Jiahao Li, Jiwen Lu, Jie zhou
Specifically, we first compute a pixel-wise similarity matrix by using representations of reference and target pixels and then select top-rank reference pixels for target pixel classification.
no code implementations • 2 Feb 2021 • Cheng Ma, Jiwen Lu, Jie zhou
As hashing becomes an increasingly appealing technique for large-scale image retrieval, multi-label hashing is also attracting more attention for the ability to exploit multi-level semantic contents.
no code implementations • 19 Jan 2021 • Lei He, Jiwen Lu, Guanghui Wang, Shiyu Song, Jie zhou
In this paper, we first introduce the concept of semantic objectness to exploit the geometric relationship of these two tasks through an analysis of the imaging process, then propose a Semantic Object Segmentation and Depth Estimation Network (SOSD-Net) based on the objectness assumption.
Ranked #86 on Semantic Segmentation on NYU Depth v2
no code implementations • ICCV 2021 • Bingyao Yu, Wanhua Li, Xiu Li, Jiwen Lu, Jie zhou
In this paper, we propose a frequency-aware spatiotemporal transformers for deep In this paper, we propose a Frequency-Aware Spatiotemporal Transformer (FAST) for video inpainting detection, which aims to simultaneously mine the traces of video inpainting from spatial, temporal, and frequency domains.
1 code implementation • 18 Dec 2020 • An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie zhou
Most existing point cloud instance and semantic segmentation methods rely heavily on strong supervision signals, which require point-level labels for every point in the scene.
1 code implementation • CVPR 2021 • Yi Wei, Ziyi Wang, Yongming Rao, Jiwen Lu, Jie zhou
In this paper, we propose a Point-Voxel Recurrent All-Pairs Field Transforms (PV-RAFT) method to estimate scene flow from point clouds.
1 code implementation • 1 Dec 2020 • Wencheng Zhu, Jiwen Lu, Jiahao Li, and Jie Zhou
In this paper, we propose a Detect-to-Summarize network (DSNet) framework for supervised video summarization.
Ranked #2 on Video Summarization on TvSum (using extra training data)
no code implementations • ECCV 2020 • Lijie Liu, Chufan Wu, Jiwen Lu, Lingxi Xie, Jie zhou, Qi Tian
Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image.
Ranked #16 on Vehicle Pose Estimation on KITTI Cars Hard
no code implementations • ECCV 2020 • Benlin Liu, Yongming Rao, Jiwen Lu, Jie zhou, Cho-Jui Hsieh
Knowledge Distillation (KD) has been one of the most popu-lar methods to learn a compact model.
1 code implementation • ECCV 2020 • Wanhua Li, Yueqi Duan, Jiwen Lu, Jianjiang Feng, Jie zhou
Human beings are fundamentally sociable -- that we generally organize our social lives in terms of relations with other people.
Ranked #1 on Visual Social Relationship Recognition on PIPA
2 code implementations • CVPR 2020 • Yansong Tang, Zanlin Ni, Jiahuan Zhou, Danyang Zhang, Jiwen Lu, Ying Wu, Jie zhou
Assessing action quality from videos has attracted growing attention in recent years.
Ranked #4 on Action Quality Assessment on AQA-7
no code implementations • 12 May 2020 • Shan Gu, Jianjiang Feng, Jiwen Lu, Jie zhou
Given a pair of fingerprints to match, we bypass the minutiae extraction step and take uniformly sampled points as key points.
no code implementations • 22 Apr 2020 • Wanhua Li, Yingqiang Zhang, Kangchen Lv, Jiwen Lu, Jianjiang Feng, Jie zhou
In this paper, we propose a graph-based kinship reasoning (GKR) network for kinship verification, which aims to effectively perform relational reasoning on the extracted features of an image pair.
Ranked #3 on Kinship Verification on KinFaceW-II
1 code implementation • CVPR 2020 • Yongming Rao, Jiwen Lu, Jie zhou
Based on this hypothesis, we propose to learn point cloud representation by bidirectional reasoning between the local structures at different abstraction hierarchies and the global shape without human supervision.
1 code implementation • CVPR 2020 • Cheng Ma, Zhenyu Jiang, Yongming Rao, Jiwen Lu, Jie zhou
In this paper, we propose a deep face super-resolution (FSR) method with iterative collaboration between two recurrent networks which focus on facial image recovery and landmark estimation respectively.
2 code implementations • CVPR 2020 • Cheng Ma, Yongming Rao, Yean Cheng, Ce Chen, Jiwen Lu, Jie zhou
In this paper, we propose a structure-preserving super resolution method to alleviate the above issue while maintaining the merits of GAN-based methods to generate perceptual-pleasant details.
Ranked #46 on Image Super-Resolution on Urban100 - 4x upscaling
no code implementations • 20 Mar 2020 • Yansong Tang, Jiwen Lu, Jie zhou
We believe the introduction of the COIN dataset will promote the future in-depth research on instructional video analysis for the community.
2 code implementations • CVPR 2020 • Ziwei Wang, Ziyi Wu, Jiwen Lu, Jie zhou
Conventional network binarization methods directly quantize the weights and activations in one-stage or two-stage detectors with constrained representational capacity, so that the information redundancy in the networks causes numerous false positives and degrades the performance significantly.
no code implementations • 23 Feb 2020 • Hao-Chiang Shao, Kang-Yu Liu, Chia-Wen Lin, Jiwen Lu
With their aid, DotFAN can learn a disentangled face representation and effectively generate face images of various facial attributes while preserving the identity of augmented faces.
no code implementations • 19 Dec 2019 • Peiyu Yu, Yongming Rao, Jiwen Lu, Jie zhou
Humans are able to perform fast and accurate object pose estimation even under severe occlusion by exploiting learned object model priors from everyday life.
1 code implementation • 18 Oct 2019 • Yinghuan Shi, Tiexin Qin, Yong liu, Jiwen Lu, Yang Gao, Dinggang Shen
By introducing an unified optimization goal, DeepAugNet intends to combine the data augmentation and the deep model training in an end-to-end training manner which is realized by simultaneously training a hybrid architecture of dueling deep Q-learning algorithm and a surrogate deep model.
1 code implementation • ICCV 2019 • Yongcheng Liu, Bin Fan, Gaofeng Meng, Jiwen Lu, Shiming Xiang, Chunhong Pan
Point cloud processing is very challenging, as the diverse shapes formed by irregular points are often indistinguishable.
Ranked #22 on 3D Part Segmentation on ShapeNet-Part
no code implementations • ICLR 2019 • Shaohui Liu*, Yi Wei*, Jiwen Lu, Jie zhou
Unlike most existing evaluation frameworks which transfer the representation of ImageNet inception model to map images onto the feature space, our framework uses a specialized encoder to acquire fine-grained domain-specific representation.
no code implementations • CVPR 2019 • Lijie Liu, Jiwen Lu, Chunjing Xu, Qi Tian, Jie zhou
In this paper, we propose to learn a deep fitting degree scoring network for monocular 3D object detection, which aims to score fitting degree between proposals and object conclusively.
Ranked #7 on Vehicle Pose Estimation on KITTI Cars Hard
no code implementations • CVPR 2019 • Yi Wei, Shaohui Liu, Wang Zhao, Jiwen Lu, Jie zhou
In this paper, we present a new perspective towards image-based shape generation.
no code implementations • CVPR 2019 • Wanhua Li, Jiwen Lu, Jianjiang Feng, Chunjing Xu, Jie zhou, Qi Tian
Existing methods for age estimation usually apply a divide-and-conquer strategy to deal with heterogeneous data caused by the non-stationary aging process.
Ranked #2 on Age Estimation on FGNET
2 code implementations • CVPR 2019 • Wenzhao Zheng, Zhaodong Chen, Jiwen Lu, Jie zhou
This paper presents a hardness-aware deep metric learning (HDML) framework.
Ranked #30 on Metric Learning on CUB-200-2011 (using extra training data)
no code implementations • CVPR 2019 • Yansong Tang, Dajun Ding, Yongming Rao, Yu Zheng, Danyang Zhang, Lili Zhao, Jiwen Lu, Jie zhou
There are substantial instructional videos on the Internet, which enables us to acquire knowledge for completing various tasks.
no code implementations • ECCV 2018 • Xin Yuan, Liangliang Ren, Jiwen Lu, Jie zhou
In this paper, we propose a simple yet effective relaxation-free method to learn more effective binary codes via policy gradient for scalable image search.
no code implementations • ECCV 2018 • Lei Chen, Jiwen Lu, Zhanjie Song, Jie zhou
In this paper, we propose a part-activated deep reinforcement learning (PA-DRL) for action prediction.
no code implementations • ECCV 2018 • Chunze Lin, Jiwen Lu, Gang Wang, Jie zhou
In this paper, we propose a graininess-aware deep feature learning method for pedestrian detection.
no code implementations • ECCV 2018 • Liangliang Ren, Xin Yuan, Jiwen Lu, Ming Yang, Jie Zhou
Visual tracking is confronted by the dilemma to locate a target both}accurately and efficiently, and make decisions online whether and how to adapt the appearance model or even restart tracking.
no code implementations • ECCV 2018 • Minghao Guo, Jiwen Lu, Jie zhou
In this paper, we propose a dual-agent deep reinforcement learning (DADRL) method for deformable face tracking, which generates bounding boxes and detects facial landmarks interactively from face videos.
no code implementations • ECCV 2018 • Liangliang Ren, Jiwen Lu, Zifeng Wang, Qi Tian, Jie zhou
To address this, we develop a deep prediction-decision network in our C-DRL, which simultaneously detects and predicts objects under a unified network via deep reinforcement learning.
no code implementations • ECCV 2018 • Xudong Lin, Yueqi Duan, Qiyuan Dong, Jiwen Lu, Jie zhou
Deep metric learning has been extensively explored recently, which trains a deep neural network to produce discriminative embedding features.
no code implementations • CVPR 2018 • Yongming Rao, Dahua Lin, Jiwen Lu, Jie zhou
In this paper, we propose a simple yet effective method to learn globally optimized detector for object detection, which is a simple modification to the standard cross-entropy gradient inspired by the REINFORCE algorithm.
no code implementations • CVPR 2018 • Yueqi Duan, Wenzhao Zheng, Xudong Lin, Jiwen Lu, Jie zhou
Learning an effective distance metric between image pairs plays an important role in visual analysis, where the training procedure largely relies on hard negative samples.
no code implementations • CVPR 2018 • Yueqi Duan, Ziwei Wang, Jiwen Lu, Xudong Lin, Jie zhou
Specifically, we design a deep reinforcement learning model to learn the structure of the graph for bitwise interaction mining, reducing the uncertainty of binary codes by maximizing the mutual information with inputs and related bits, so that the ambiguous bits receive additional instruction from the graph for confident binarization.
no code implementations • CVPR 2018 • Yansong Tang, Yi Tian, Jiwen Lu, Peiyang Li, Jie zhou
In this paper, we propose a deep progressive reinforcement learning (DPRL) method for action recognition in skeleton-based videos, which aims to distil the most informative frames and discard ambiguous frames in sequences for recognizing actions.
Ranked #3 on Skeleton Based Action Recognition on UT-Kinect
no code implementations • CVPR 2018 • Zhixiang Chen, Xin Yuan, Jiwen Lu, Qi Tian, Jie zhou
This paper presents a discrepancy minimizing model to address the discrete optimization problem in hashing learning.
1 code implementation • 20 Mar 2018 • Shaohui Liu, Yi Wei, Jiwen Lu, Jie zhou
Unlike most existing evaluation frameworks which transfer the representation of ImageNet inception model to map images onto the feature space, our framework uses a specialized encoder to acquire fine-grained domain-specific representation.
no code implementations • NeurIPS 2017 • Ji Lin, Yongming Rao, Jiwen Lu, Jie zhou
In this paper, we propose a Runtime Neural Pruning (RNP) framework which prunes the deep neural network dynamically at the runtime.
no code implementations • ICCV 2017 • Venice Erin Liong, Jiwen Lu, Yap-Peng Tan, Jie zhou
In this paper, we propose a cross-modal deep variational hashing (CMDVH) method to learn compact binary codes for cross-modality multimedia retrieval.
no code implementations • ICCV 2017 • Yongming Rao, Ji Lin, Jiwen Lu, Jie zhou
In this paper, we propose a discriminative aggregation network (DAN) for video face recognition, which aims to integrate information from video frames effectively and efficiently.
no code implementations • ICCV 2017 • Yongming Rao, Jiwen Lu, Jie zhou
In this paper, we propose an attention-aware deep reinforcement learning (ADRL) method for video face recognition, which aims to discard the misleading and confounding frames and find the focuses of attention in face videos for person recognition.
no code implementations • 25 Sep 2017 • Xi Peng, Jiashi Feng, Shijie Xiao, Jiwen Lu, Zhang Yi, Shuicheng Yan
In this paper, we present a deep extension of Sparse Subspace Clustering, termed Deep Sparse Subspace Clustering (DSSC).
no code implementations • ICCV 2017 • Fangyu Liu, Shuaipeng Li, Liqiang Zhang, Chenghu Zhou, Rongtian Ye, Yuebin Wang, Jiwen Lu
Our method provides an automatic process that maps the raw data to the classification results.
no code implementations • CVPR 2017 • Yueqi Duan, Jiwen Lu, Ziwei Wang, Jianjiang Feng, Jie zhou
In this paper, we propose an unsupervised feature learning method called deep binary descriptor with multi-quantization (DBD-MQ) for visual matching.
no code implementations • CVPR 2017 • Ji Lin, Liangliang Ren, Jiwen Lu, Jianjiang Feng, Jie zhou
In this paper, we propose a consistent-aware deep learning (CADL) framework for person re-identification in a camera network.
no code implementations • European Conference on Computer Vision 2016 • Rahul Rama Varior, Bing Shuai, Jiwen Lu, Dong Xu, Gang Wang
Matching pedestrians across multiple camera views known as human re-identification (re-identification) is a challenging problem in visual surveillance.
no code implementations • CVPR 2016 • Anran Wang, Jianfei Cai, Jiwen Lu, Tat-Jen Cham
While convolutional neural networks (CNN) have been excellent for object recognition, the greater spatial variability in scene images typically meant that the standard full-image CNN features are suboptimal for scene classification.
no code implementations • CVPR 2016 • Kevin Lin, Jiwen Lu, Chu-Song Chen, Jie zhou
In this paper, we propose a new unsupervised deep learning approach called DeepBit to learn compact binary descriptor for efficient visual object matching.
no code implementations • 6 Apr 2016 • Ziyan Wang, Jiwen Lu, Ruogu Lin, Jianjiang Feng, Jie zhou
Specifically, we construct a pair of deep convolutional neural networks (CNNs) for the RGB and depth data, and concatenate them at the top layer of the network with a loss function which learns a new feature space where both correlated part and the individual part of the RGB-D information are well modelled.
no code implementations • 4 Jan 2016 • Abrar H. Abdulnabi, Gang Wang, Jiwen Lu, Kui Jia
Each CNN will generate attribute-specific feature representations, and then we apply multi-task learning on the features to predict their attributes.
no code implementations • ICCV 2015 • Xianglong Liu, Lei Huang, Cheng Deng, Jiwen Lu, Bo Lang
have enjoyed the benefits of complementary hash tables and information fusion over multiple views.
no code implementations • ICCV 2015 • Anran Wang, Jianfei Cai, Jiwen Lu, Tat-Jen Cham
We first construct deep CNN layers for color and depth separately, and then connect them with our carefully designed multi-modal layers, which fuse color and depth information by enforcing a common part to be shared by features of different modalities.
no code implementations • ICCV 2015 • Lin Ma, Jiwen Lu, Jianjiang Feng, Jie zhou
It is desirable to combine multiple feature descriptors to improve the visual tracking performance because different features can provide complementary information to describe objects of interest.
no code implementations • ICCV 2015 • Jiwen Lu, Venice Erin Liong, Jie zhou
In this paper, we propose a simultaneous local binary feature learning and encoding (SLBFLE) method for face recognition.
no code implementations • ICCV 2015 • Lin Ma, Xiaoqin Zhang, Weiming Hu, Junliang Xing, Jiwen Lu, Jie zhou
To address this, this paper presents a local subspace collaborative tracking method for robust visual tracking, where multiple linear and nonlinear subspaces are learned to better model the nonlinear relationship of object appearances.
no code implementations • 16 Nov 2015 • Siyuan Huang, Jiwen Lu, Jie zhou, Anil K. Jain
In this paper, we propose a nonlinear local metric learning (NLML) method to improve the state-of-the-art performance of person re-identification on public datasets.
no code implementations • CVPR 2015 • Jiwen Lu, Gang Wang, Weihong Deng, Pierre Moulin, Jie zhou
In this paper, we propose a multi-manifold deep metric learning (MMDML) method for image set classification, which aims to recognize an object of interest from a set of image instances captured from varying viewpoints or under varying illuminations.
no code implementations • CVPR 2015 • Venice Erin Liong, Jiwen Lu, Gang Wang, Pierre Moulin, Jie zhou
In this paper, we propose a new deep hashing (DH) approach to learn compact binary codes for large scale visual search.
no code implementations • CVPR 2015 • Junlin Hu, Jiwen Lu, Yap-Peng Tan
Conventional metric learning methods usually assume that the training and test samples are captured in similar scenarios so that their distributions are assumed to be the same.
no code implementations • 17 Nov 2014 • Xi Peng, Jiwen Lu, Zhang Yi, Rui Yan
In this paper, we address two challenging problems in unsupervised subspace learning: 1) how to automatically identify the feature dimension of the learned subspace (i. e., automatic subspace learning), and 2) how to learn the underlying subspace in the presence of Gaussian noise (i. e., robust subspace learning).
no code implementations • 4 Oct 2014 • Rahul Rama Varior, Gang Wang, Jiwen Lu
We model color feature generation as a learning problem by jointly learning a linear transformation and a dictionary to encode pixel values.
no code implementations • CVPR 2014 • Junlin Hu, Jiwen Lu, Yap-Peng Tan
This paper presents a new discriminative deep metric learning (DDML) method for face verification in the wild.
3 code implementations • 14 Apr 2014 • Tsung-Han Chan, Kui Jia, Shenghua Gao, Jiwen Lu, Zinan Zeng, Yi Ma
In this work, we propose a very simple deep learning network for image classification which comprises only the very basic data processing components: cascaded principal component analysis (PCA), binary hashing, and block-wise histograms.
Ranked #38 on Image Classification on MNIST
no code implementations • 6 Nov 2013 • Sheng Huang, Dan Yang, Fei Yang, Yongxin Ge, Xiaohong Zhang, Jiwen Lu
We present an improved Locality Preserving Projections (LPP) method, named Gloablity-Locality Preserving Projections (GLPP), to preserve both the global and local geometric structures of data.
no code implementations • TPAMI 2013 • Jiwen Lu, Xiuzhuang Zhou, Yap-Pen Tan, Yuanyuan Shang, Jie zhou
In this paper, we propose a new neighborhood repulsed metric learning (NRML) method for kinship verification.
Ranked #5 on Kinship Verification on KinFaceW-I