no code implementations • ECCV 2020 • Zilong Ji, Xiaolong Zou, Xiaohan Lin, Xiao Liu, Tiejun Huang, Si Wu
By iteratively learning with the two strategies, the attentive regions are gradually shifted from the background to the foreground and the features become more discriminative.
1 code implementation • 6 Jun 2024 • Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Shitao Xiao, Xi Yang, Yongping Xiong, Bo Zhang, Tiejun Huang, Zheng Liu
To address the above problems, we propose a new benchmark, called MLVU (Multi-task Long Video Understanding Benchmark), for the comprehensive and in-depth evaluation of LVU.
no code implementations • 1 Jun 2024 • Baoyue Zhang, Yajing Zheng, Shiyan Chen, Jiyuan Zhang, Kang Chen, Zhaofei Yu, Tiejun Huang
This innovative approach comprehensively records temporal and spatial visual information, rendering it particularly suitable for magnifying high-speed micro-motions. This paper introduces SpikeMM, a pioneering spike-based algorithm tailored specifically for high-speed motion magnification.
1 code implementation • 31 May 2024 • Jianhao Ding, Zhiyu Pan, Yujia Liu, Zhaofei Yu, Tiejun Huang
We present that membrane potential perturbation dynamics can reliably convey the intensity of perturbation.
no code implementations • 30 May 2024 • Yujia Liu, Tong Bu, Jianhao Ding, Zecheng Hao, Tiejun Huang, Zhaofei Yu
In this paper, we propose a novel approach to enhance the robustness of SNNs through gradient sparsity regularization.
1 code implementation • 29 May 2024 • Gaole Dai, Cheng-Ching Tseng, Qingpo Wuwu, Rongyu Zhang, Shaokang Wang, Ming Lu, Tiejun Huang, Yu Zhou, Ali Ata Tuz, Matthias Gunzer, Jianxu Chen, Shanghang Zhang
The rapid pace of innovation in biological microscopy imaging has led to large images, putting pressure on data storage and impeding efficient sharing, management, and visualization.
1 code implementation • 27 May 2024 • Liwen Hu, Lei Ma, Yijia Guo, Tiejun Huang
Large-scale synthetic datasets have significantly accelerated the development of these cameras, particularly in reconstruction and optical flow.
no code implementations • 21 May 2024 • Yuanhong Tang, Shanshan Jia, Tiejun Huang, Zhaofei Yu, Jian K. Liu
A single neuron receives an extensive array of synaptic inputs through its dendrites, raising the fundamental question of how these inputs undergo integration and summation, culminating in the initiation of spikes in the soma.
no code implementations • 25 Apr 2024 • Xiang Li, Yiqun Yao, Xin Jiang, Xuezhi Fang, Chao Wang, Xinzhang Liu, Zihan Wang, Yu Zhao, Xin Wang, Yuyao Huang, Shuangyong Song, Yongxiang Li, Zheng Zhang, Bo Zhao, Aixin Sun, Yequan Wang, Zhongjiang He, Zhongyuan Wang, Xuelong Li, Tiejun Huang
Large language models (LLMs) have showcased profound capabilities in language understanding and generation, facilitating a wide array of applications.
no code implementations • 10 Apr 2024 • Gaole Dai, Zhenyu Wang, Qinwen Xu, Ming Lu, Wen Chen, Boxin Shi, Shanghang Zhang, Tiejun Huang
Since the spike camera relies on temporal integration instead of temporal differentiation used by event cameras, our proposed TfS loss maintains manageable training costs.
1 code implementation • 31 Mar 2024 • Fan Bai, Yuxin Du, Tiejun Huang, Max Q. -H. Meng, Bo Zhao
Additionally, we propose M3D-LaMed, a versatile multi-modal large language model for 3D medical image analysis.
no code implementations • 25 Mar 2024 • Yijia Guo, Yuanxi Bai, Liwen Hu, Mianzhi Liu, Ziyi Guo, Lei Ma, Tiejun Huang
As a neuromorphic sensor with high temporal resolution, spike cameras offer notable advantages over traditional cameras in high-speed vision applications such as high-speed optical estimation, depth estimation, and object tracking.
1 code implementation • 14 Mar 2024 • Kang Chen, Shiyan Chen, Jiyuan Zhang, Baoyue Zhang, Yajing Zheng, Tiejun Huang, Zhaofei Yu
Our approach begins with the formulation of a spike-guided deblurring model that explores the theoretical relationships among spike streams, blurry images, and their corresponding sharp sequences.
no code implementations • 7 Mar 2024 • Ding Chen, Peixi Peng, Tiejun Huang, Yonghong Tian
As a general method for exploration in deep reinforcement learning (RL), NoisyNet can produce problem-specific exploration strategies.
no code implementations • 19 Feb 2024 • Xuelin Qian, Yu Wang, Simian Luo, yinda zhang, Ying Tai, Zhenyu Zhang, Chengjie Wang, xiangyang xue, Bo Zhao, Tiejun Huang, Yunsheng Wu, Yanwei Fu
In this paper, we extend auto-regressive models to 3D domains, and seek a stronger ability of 3D shape generation by improving auto-regressive models at capacity and scalability simultaneously.
1 code implementation • 18 Feb 2024 • Muyang He, Yexin Liu, Boya Wu, Jianhao Yuan, Yueze Wang, Tiejun Huang, Bo Zhao
Multimodal Large Language Models (MLLMs) have demonstrated notable capabilities in general visual understanding and reasoning tasks.
no code implementations • 1 Feb 2024 • Zecheng Hao, Xinyu Shi, Zhiyu Pan, Yujia Liu, Zhaofei Yu, Tiejun Huang
Compared to traditional Artificial Neural Network (ANN), Spiking Neural Network (SNN) has garnered widespread academic interest for its intrinsic ability to transmit information in a more biological-inspired and energy-efficient manner.
no code implementations • 19 Jan 2024 • Liwen Hu, Ziluo Ding, Mianzhi Liu, Lei Ma, Tiejun Huang
In this paper, we propose a bidirectional recurrent-based reconstruction framework, including a Light-Robust Representation (LR-Rep) and a fusion module, to better handle such extreme conditions.
no code implementations • 9 Jan 2024 • Ding Chen, Peixi Peng, Tiejun Huang, Yonghong Tian
Recently, the surrogate gradient method has been utilized for training multi-layer SNNs, which allows SNNs to achieve comparable performance with the corresponding deep networks in this task.
1 code implementation • 20 Dec 2023 • Quan Sun, Yufeng Cui, Xiaosong Zhang, Fan Zhang, Qiying Yu, Zhengxiong Luo, Yueze Wang, Yongming Rao, Jingjing Liu, Tiejun Huang, Xinlong Wang
The human ability to easily solve multimodal tasks in context (i. e., with only a few demonstrations or simple instructions), is what current multimodal systems have largely struggled to imitate.
Ranked #28 on Visual Question Answering on MM-Vet
2 code implementations • 29 Nov 2023 • Baorui Ma, Haoge Deng, Junsheng Zhou, Yu-Shen Liu, Tiejun Huang, Xinlong Wang
We justify that the refined 3D geometric priors aid in the 3D-aware capability of 2D diffusion priors, which in turn provides superior guidance for the refinement of 3D geometric priors.
1 code implementation • 22 Nov 2023 • Yuxin Du, Fan Bai, Tiejun Huang, Bo Zhao
Precise image segmentation provides clinical study with instructive information.
2 code implementations • 10 Oct 2023 • Junsheng Zhou, Jinsheng Wang, Baorui Ma, Yu-Shen Liu, Tiejun Huang, Xinlong Wang
Scaling up representations for images or text has been extensively investigated in the past few years and has led to revolutions in learning vision and language.
Ranked #1 on Zero-shot 3D classification on Objaverse LVIS (using extra training data)
no code implementations • IEEE Transactions on Multimedia 2023 • Zhixuan Li, Weining Ye, Tingting Jiang, Tiejun Huang
In human amodal perception, shape-prior knowledge is helpful for AIS.
2 code implementations • 11 Jul 2023 • Quan Sun, Qiying Yu, Yufeng Cui, Fan Zhang, Xiaosong Zhang, Yueze Wang, Hongcheng Gao, Jingjing Liu, Tiejun Huang, Xinlong Wang
We present Emu, a Transformer-based multimodal foundation model, which can seamlessly generate images and texts in multimodal context.
Ranked #1 on Visual Question Answering on VQA v2
2 code implementations • 9 Jul 2023 • Bo Zhao, Boya Wu, Muyang He, Tiejun Huang
Thanks to the emerging of foundation models, the large language and vision models are integrated to acquire the multimodal ability of visual captioning, question answering, etc.
no code implementations • 3 Jul 2023 • Jiyuan Zhang, Shiyan Chen, Yajing Zheng, Zhaofei Yu, Tiejun Huang
To process the spikes, we build a novel model \textbf{SpkOccNet}, in which we integrate information of spikes from continuous viewpoints within multi-windows, and propose a novel cross-view mutual attention mechanism for effective fusion and refinement.
no code implementations • 21 Jun 2023 • Xundong Wu, Pengfei Zhao, Zilin Yu, Lei Ma, Ka-Wa Yip, Huajin Tang, Gang Pan, Tiejun Huang
Our comprehension of biological neuronal networks has profoundly influenced the evolution of artificial neural networks (ANNs).
no code implementations • 20 Jun 2023 • Yu Wang, Xuelin Qian, Jingyang Huo, Tiejun Huang, Bo Zhao, Yanwei Fu
Through the adaptation of the Auto-Regressive model and the utilization of large language models, we have developed a remarkable model with an astounding 3. 6 billion trainable parameters, establishing it as the largest 3D shape generation model to date, named Argus-3D.
no code implementations • 9 Jun 2023 • Jianhao Ding, Zhaofei Yu, Tiejun Huang, Jian K. Liu
The success of deep learning in the past decade is partially shrouded in the shadow of adversarial attacks.
1 code implementation • 8 Jun 2023 • Muyang He, Shuo Yang, Tiejun Huang, Bo Zhao
The state of the art of many learning tasks, e. g., image classification, is advanced by collecting larger datasets and then training larger models on them.
no code implementations • IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2023 • Zhixuan Li, Ruohua Shi, Tiejun Huang, Tingting Jiang
Hence we believe it is vital for the method to be distinguishable about the degree of occlusion for each instance.
no code implementations • 26 May 2023 • Gaole Dai, Wei Wu, Ziyu Wang, Jie Fu, Shanghang Zhang, Tiejun Huang
By incorporating hand-designed optimizers as the second component in our hybrid approach, we are able to retain the benefits of learned optimizers while stabilizing the training process and, more importantly, improving testing performance.
1 code implementation • 6 Apr 2023 • Xinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang, Chunhua Shen, Tiejun Huang
We unify various segmentation tasks into a generalist in-context learning framework that accommodates different kinds of segmentation data by transforming them into the same format of images.
Ranked #1 on Few-Shot Semantic Segmentation on PASCAL-5i (5-Shot) (using extra training data)
no code implementations • 6 Apr 2023 • Liwen Hu, Lei Ma, Zhaofei Yu, Boxin Shi, Tiejun Huang
Based on our noise model, the first benchmark for spike stream denoising is proposed which includes clear (noisy) spike stream.
no code implementations • 29 Mar 2023 • Shiyan Chen, Jiyuan Zhang, Zhaofei Yu, Tiejun Huang
Based on this, we propose Asymmetric Tunable Blind-Spot Network (AT-BSN), where the blind-spot size can be freely adjusted, thus better balancing noise correlation suppression and image local spatial destruction during training and inference.
1 code implementation • 21 Mar 2023 • Yajing Zheng, Jiyuan Zhang, Rui Zhao, Jianhao Ding, Shiyan Chen, Ruiqin Xiong, Zhaofei Yu, Tiejun Huang
SpikeCV focuses on encapsulation for spike data, standardization for dataset interfaces, modularization for vision tasks, and real-time applications for challenging scenes.
6 code implementations • 20 Mar 2023 • Yuxin Fang, Quan Sun, Xinggang Wang, Tiejun Huang, Xinlong Wang, Yue Cao
We launch EVA-02, a next-generation Transformer-based visual representation pre-trained to reconstruct strong and robust language-aligned vision features via masked image modeling.
1 code implementation • 19 Mar 2023 • Ziluo Ding, Hao Luo, Ke Li, Junpeng Yue, Tiejun Huang, Zongqing Lu
One of the essential missions in the AI research community is to build an autonomous embodied agent that can attain high-level performance across a wide spectrum of tasks.
2 code implementations • ICLR 2022 • Tong Bu, Wei Fang, Jianhao Ding, Penglin Dai, Zhaofei Yu, Tiejun Huang
In this paper, we theoretically analyze ANN-SNN conversion error and derive the estimated activation function of SNNs.
2 code implementations • 21 Feb 2023 • Zecheng Hao, Jianhao Ding, Tong Bu, Tiejun Huang, Zhaofei Yu
The experimental results show that our proposed method achieves state-of-the-art performance on CIFAR-10, CIFAR-100, and ImageNet datasets.
1 code implementation • 14 Feb 2023 • Chuang Zhu, Kebin Liu, Wenqi Tang, Ke Mei, Jiaqi Zou, Tiejun Huang
The divergence between labeled training data and unlabeled testing data is a significant challenge for recent deep learning models.
2 code implementations • 4 Feb 2023 • Zecheng Hao, Tong Bu, Jianhao Ding, Tiejun Huang, Zhaofei Yu
Spiking Neural Networks (SNNs) have received extensive academic attention due to the unique properties of low power consumption and high-speed computing on neuromorphic chips.
no code implementations • ICCV 2023 • Xinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang, Chunhua Shen, Tiejun Huang
We unify various segmentation tasks into a generalist in-context learning framework that accommodates different kinds of segmentation data by transforming them into the same format of images.
no code implementations • ICCV 2023 • Zhixuan Li, Weining Ye, Juan Terven, Zachary Bennett, Ying Zheng, Tingting Jiang, Tiejun Huang
To bridge this gap, we propose a new task called Multi-view Amodal Instance Segmentation (MAIS) and introduce the MUVA dataset, the first MUlti-View AIS dataset that takes the shopping scenario as instantiation.
no code implementations • CVPR 2023 • Yakun Chang, Chu Zhou, Yuchen Hong, Liwen Hu, Chao Xu, Tiejun Huang, Boxin Shi
Capturing high frame rate and high dynamic range (HFR&HDR) color videos in high-speed scenes with conventional frame-based cameras is very challenging.
1 code implementation • CVPR 2023 • Xinlong Wang, Wen Wang, Yue Cao, Chunhua Shen, Tiejun Huang
In this work, we present Painter, a generalist model which addresses these obstacles with an "image"-centric solution, that is, to redefine the output of core vision tasks as images, and specify task prompts as also images.
Ranked #6 on Personalized Segmentation on PerSeg
6 code implementations • CVPR 2023 • Yuxin Fang, Wen Wang, Binhui Xie, Quan Sun, Ledell Wu, Xinggang Wang, Tiejun Huang, Xinlong Wang, Yue Cao
We launch EVA, a vision-centric foundation model to explore the limits of visual representation at scale using only publicly accessible data.
Ranked #1 on Object Detection on COCO-O
no code implementations • 25 Oct 2022 • Ziluo Ding, Wanpeng Zhang, Junpeng Yue, Xiangjun Wang, Tiejun Huang, Zongqing Lu
We investigate the use of natural language to drive the generalization of policies in multi-agent settings.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 26 Sep 2022 • Ziluo Ding, Kefan Su, Weixin Hong, Liwen Zhu, Tiejun Huang, Zongqing Lu
Communication helps agents to obtain information about others so that better coordinated behavior can be learned.
1 code implementation • 26 Aug 2022 • Jiaming Liu, Qizhe Zhang, Jianing Li, Ming Lu, Tiejun Huang, Shanghang Zhang
Neuromorphic spike data, an upcoming modality with high temporal resolution, has shown promising potential in real-world applications due to its inherent advantage to overcome high-velocity motion blur.
no code implementations • 26 Aug 2022 • Jianing Li, Jiaming Liu, Xiaobao Wei, Jiyuan Zhang, Ming Lu, Lei Ma, Li Du, Tiejun Huang, Shanghang Zhang
In this paper, we propose a novel Uncertainty-Guided Depth Fusion (UGDF) framework to fuse the predictions of monocular and stereo depth estimation networks for spike camera.
no code implementations • European Conference on Computer Vision (ECCV) 2022 • Zhixuan Li, Weining Ye, Tingting Jiang, Tiejun Huang
However, masks in 2D space are only some observations and samples from the 3D model in different viewpoints and thus can not represent the real complete physical shape of the instances.
no code implementations • 10 Mar 2022 • Lantian Xue, Yixiong Zou, Peixi Peng, Yonghong Tian, Tiejun Huang
To solve this problem, we propose the Annotation Efficient Person Re-Identification method to select image pairs from an alternative pair set according to the fallibility and diversity of pairs, and train the Re-ID model based on the annotation.
no code implementations • 3 Feb 2022 • Tong Bu, Jianhao Ding, Zhaofei Yu, Tiejun Huang
We evaluate our algorithm on the CIFAR-10, CIFAR-100 and ImageNet datasets and achieve state-of-the-art accuracy, using fewer time-steps.
1 code implementation • CVPR 2022 • Lin Zhu, Xiao Wang, Yi Chang, Jianing Li, Tiejun Huang, Yonghong Tian
We propose a novel Event-based Video reconstruction framework based on a fully Spiking Neural Network (EVSNN), which utilizes Leaky-Integrate-and-Fire (LIF) neuron and Membrane Potential (MP) neuron.
Computational Efficiency Event-Based Video Reconstruction +2
no code implementations • 23 Jan 2022 • Tiejun Huang, Yajing Zheng, Zhaofei Yu, Rui Chen, Yuan Li, Ruiqin Xiong, Lei Ma, Junwei Zhao, Siwei Dong, Lin Zhu, Jianing Li, Shanshan Jia, Yihua Fu, Boxin Shi, Si Wu, Yonghong Tian
By treating vidar as spike trains in biological vision, we have further developed a spiking neural network-based machine vision system that combines the speed of the machine and the mechanism of biological vision, achieving high-speed object detection and tracking 1, 000x faster than human vision.
no code implementations • 21 Jan 2022 • Ding Chen, Peixi Peng, Tiejun Huang, Yonghong Tian
With the help of special neuromorphic hardware, spiking neural networks (SNNs) are expected to realize artificial intelligence (AI) with less energy consumption.
no code implementations • 4 Jan 2022 • Liwen Hu, Lei Ma, Dawei Weng, Tiejun Huang
More importantly, due to mimicking receptive field mechanism to collect regional information, RVSM can filter high intensity noise effectively and improves the problem that Spike camera is sensitive to noise largely.
1 code implementation • 2 Dec 2021 • Wenkai Chen, Chuang Zhu, Yi Chen, Mengting Li, Tiejun Huang
Imperfect labels are ubiquitous in real-world datasets and seriously harm the model performance.
Ranked #1 on Learning with noisy labels on CIFAR-100N
no code implementations • NeurIPS 2021 • Xingsi Dong, Tianhao Chu, Tiejun Huang, Zilong Ji, Si Wu
To elucidate the underlying mechanism clearly, we first study continuous attractor neural networks (CANNs), and find that noisy neural adaptation, exemplified by spike frequency adaptation (SFA) in this work, can generate Lévy flights representing transitions of the network state in the attractor space.
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 • CVPR 2022 • Liwen Hu, Rui Zhao, Ziluo Ding, Lei Ma, Boxin Shi, Ruiqin Xiong, Tiejun Huang
Further, for training SCFlow, we synthesize two sets of optical flow data for the spiking camera, SPIkingly Flying Things and Photo-realistic High-speed Motion, denoted as SPIFT and PHM respectively, corresponding to random high-speed and well-designed scenes.
no code implementations • 29 Sep 2021 • Jianhao Ding, Jiyuan Zhang, Zhaofei Yu, Tiejun Huang
Despite that spiking neural networks (SNNs) show strong advantages in information encoding, power consuming, and computational capability, the underdevelopment of supervised learning algorithms is still a hindrance for training SNN.
no code implementations • 29 Sep 2021 • Ziluo Ding, Weixin Hong, Liwen Zhu, Tiejun Huang, Zongqing Lu
Agents determine the priority of decision-making by comparing the value of intention.
1 code implementation • 10 Sep 2021 • Ziluo Ding, Rui Zhao, Jiyuan Zhang, Tianxiao Gao, Ruiqin Xiong, Zhaofei Yu, Tiejun Huang
Recently, many deep learning methods have shown great success in providing promising solutions to many event-based problems, such as optical flow estimation.
no code implementations • CVPR 2021 • Jing Zhao, Ruiqin Xiong, Hangfan Liu, Jian Zhang, Tiejun Huang
Different from the conventional digital cameras that compact the photoelectric information within the exposure interval into a single snapshot, the spike camera produces a continuous spike stream to record the dynamic light intensity variation process.
no code implementations • CVPR 2021 • Yajing Zheng, Lingxiao Zheng, Zhaofei Yu, Boxin Shi, Yonghong Tian, Tiejun Huang
Mimicking the sampling mechanism of the fovea, a retina-inspired camera, named spiking camera, is developed to record the external information with a sampling rate of 40, 000 Hz, and outputs asynchronous binary spike streams.
1 code implementation • 25 May 2021 • Jianhao Ding, Zhaofei Yu, Yonghong Tian, Tiejun Huang
We show that the inference time can be reduced by optimizing the upper bound of the fit curve in the revised ANN to achieve fast inference.
1 code implementation • 11 May 2021 • Yanqi Chen, Zhaofei Yu, Wei Fang, Tiejun Huang, Yonghong Tian
Our key innovation is to redefine the gradient to a new synaptic parameter, allowing better exploration of network structures by taking full advantage of the competition between pruning and regrowth of connections.
1 code implementation • NeurIPS 2021 • Wei Fang, Zhaofei Yu, Yanqi Chen, Tiejun Huang, Timothée Masquelier, Yonghong Tian
Previous Spiking ResNet mimics the standard residual block in ANNs and simply replaces ReLU activation layers with spiking neurons, which suffers the degradation problem and can hardly implement residual learning.
no code implementations • ICCV 2021 • Lin Zhu, Jianing Li, Xiao Wang, Tiejun Huang, Yonghong Tian
In this paper, we propose a NeuSpike-Net to learn both the high dynamic range and high motion sensitivity of DVS and the full texture sampling of spike camera to achieve high-speed and high dynamic image reconstruction.
no code implementations • 1 Jan 2021 • Ziluo Ding, Tiejun Huang, Zongqing Lu
The emergence of language is a mystery.
no code implementations • ICCV 2021 • Jing Zhao, Jiyu Xie, Ruiqin Xiong, Jian Zhang, Zhaofei Yu, Tiejun Huang
In this paper, we properly exploit the relative motion and derive the relationship between light intensity and each spike, so as to recover the external scene with both high temporal and high spatial resolution.
no code implementations • NeurIPS 2020 • Chu Zhou, Hang Zhao, Jin Han, Chang Xu, Chao Xu, Tiejun Huang, Boxin Shi
A conventional camera often suffers from over- or under-exposure when recording a real-world scene with a very high dynamic range (HDR).
1 code implementation • ECCV 2020 • Guangyao Chen, Limeng Qiao, Yemin Shi, Peixi Peng, Jia Li, Tiejun Huang, ShiLiang Pu, Yonghong Tian
In this process, one of the key challenges is to reduce the risk of generalizing the inherent characteristics of numerous unknown samples learned from a small amount of known data.
no code implementations • 16 Oct 2020 • Ruohua Shi, Wenyao Wang, Zhixuan Li, Liuyuan He, Kaiwen Sheng, Lei Ma, Kai Du, Tingting Jiang, Tiejun Huang
Computer vision technology is widely used in biological and medical data analysis and understanding.
no code implementations • 23 Aug 2020 • Zilong Ji, Xiaolong Zou, Tiejun Huang, Si Wu
In this study, we build a computational model to elucidate the computational advantages associated with the interactions between two pathways.
1 code implementation • ICCV 2021 • Wei Fang, Zhaofei Yu, Yanqi Chen, Timothee Masquelier, Tiejun Huang, Yonghong Tian
In this paper, we take inspiration from the observation that membrane-related parameters are different across brain regions, and propose a training algorithm that is capable of learning not only the synaptic weights but also the membrane time constants of SNNs.
1 code implementation • NeurIPS 2020 • Ziluo Ding, Tiejun Huang, Zongqing Lu
Empirically, we show that I2C can not only reduce communication overhead but also improve the performance in a variety of multi-agent cooperative scenarios, comparing to existing methods.
no code implementations • 11 Mar 2020 • Zhongzhi Yu, Yemin Shi, Tiejun Huang, Yizhou Yu
Thus, KQ can represent the weight tensor in the convolution layer with low-bit indexes and a kernel codebook with limited size, which enables KQ to achieve significant compression ratio.
no code implementations • 10 Jan 2020 • Ling-Yu Duan, Jiaying Liu, Wenhan Yang, Tiejun Huang, Wen Gao
Meanwhile, we systematically review state-of-the-art techniques in video compression and feature compression from the unique perspective of MPEG standardization, which provides the academic and industrial evidence to realize the collaborative compression of video and feature streams in a broad range of AI applications.
no code implementations • 20 Dec 2019 • Zilong Ji, Xiaolong Zou, Tiejun Huang, Si Wu
The proposed model consists of two alternate processes, progressive clustering and episodic training.
1 code implementation • NeurIPS 2019 • Xiao Liu, Xiaolong Zou, Zilong Ji, Gengshuo Tian, Yuanyuan Mi, Tiejun Huang, K. Y. Michael Wong, Si Wu
Experimental data has revealed that in addition to feedforward connections, there exist abundant feedback connections in a neural pathway.
no code implementations • ICCV 2019 • Limeng Qiao, Yemin Shi, Jia Li, Yao-Wei Wang, Tiejun Huang, Yonghong Tian
By solving the problem with its closed-form solution on the fly with the setup of transduction, our approach efficiently tailors an episodic-wise metric for each task to adapt all features from a shared task-agnostic embedding space into a more discriminative task-specific metric space.
no code implementations • 25 Sep 2019 • Tianxiao Gao, Ruiqin Xiong, Zhenhua Liu, Siwei Ma, Feng Wu, Tiejun Huang, Wen Gao
One way to compress these heavy models is knowledge transfer (KT), in which a light student network is trained through absorbing the knowledge from a powerful teacher network.
no code implementations • 25 Sep 2019 • Zilong Ji, Xiaolong Zou, Tiejun Huang, Si Wu
Using the benchmark dataset Omniglot, we show that our model outperforms other unsupervised few-shot learning methods to a large extend and approaches to the performances of supervised methods.
no code implementations • 28 Jul 2019 • Yuanyuan Mi, Xiaohan Lin, Xiaolong Zou, Zilong Ji, Tiejun Huang, Si Wu
Spatiotemporal information processing is fundamental to brain functions.
no code implementations • 20 Jul 2019 • Lin Zhu, Siwei Dong, Tiejun Huang, Yonghong Tian
Conventional frame-based camera is not able to meet the demand of rapid reaction for real-time applications, while the emerging dynamic vision sensor (DVS) can realize high speed capturing for moving objects.
no code implementations • 6 May 2019 • Yu Shu, Yemin Shi, Yao-Wei Wang, Tiejun Huang, Yonghong Tian
Predictors for new categories are added to the classification layer to "open" the deep neural networks to incorporate new categories dynamically.
no code implementations • 30 Apr 2019 • Yichen Zhang, Shanshan Jia, Yajing Zheng, Zhaofei Yu, Yonghong Tian, Siwei Ma, Tiejun Huang, Jian. K. Liu
The SID is an end-to-end decoder with one end as neural spikes and the other end as images, which can be trained directly such that visual scenes are reconstructed from spikes in a highly accurate fashion.
2 code implementations • CVPR 2019 • Jianzhong He, Shiliang Zhang, Ming Yang, Yanhu Shan, Tiejun Huang
Exploiting multi-scale representations is critical to improve edge detection for objects at different scales.
Ranked #2 on Edge Detection on BRIND
no code implementations • 22 Feb 2019 • Yajing Zheng, Shanshan Jia, Zhaofei Yu, Tiejun Huang, Jian. K. Liu, Yonghong Tian
Recent studies have suggested that the cognitive process of the human brain is realized as probabilistic inference and can be further modeled by probabilistic graphical models like Markov random fields.
no code implementations • 19 Nov 2018 • Jianing Li, Shiliang Zhang, Tiejun Huang
A temporal stream in this network is constructed by inserting several Multi-scale 3D (M3D) convolution layers into a 2D CNN network.
no code implementations • 6 Nov 2018 • Qi Yan, Yajing Zheng, Shanshan Jia, Yichen Zhang, Zhaofei Yu, Feng Chen, Yonghong Tian, Tiejun Huang, Jian. K. Liu
When a deep CNN with many layers is used for the visual system, it is not easy to compare the structure components of CNNs with possible neuroscience underpinnings due to highly complex circuits from the retina to higher visual cortex.
4 code implementations • ICLR 2020 • Jiechuan Jiang, Chen Dun, Tiejun Huang, Zongqing Lu
The key is to understand the mutual interplay between agents.
no code implementations • 12 Aug 2018 • Shanshan Jia, Zhaofei Yu, Arno Onken, Yonghong Tian, Tiejun Huang, Jian. K. Liu
Furthermore, we show that STNMF can separate spikes of a ganglion cell into a few subsets of spikes where each subset is contributed by one presynaptic bipolar cell.
no code implementations • 2 Aug 2018 • Zhaofei Yu, Yonghong Tian, Tiejun Huang, Jian. K. Liu
Taken together, our results suggest that the WTA circuit could be seen as the minimal inference unit of neuronal circuits.
no code implementations • 15 Jun 2018 • Yang Yue, Liuyuan He, Gan He, Jian. K. Liu, Kai Du, Yonghong Tian, Tiejun Huang
Photoreceptors in the retina are coupled by electrical synapses called "gap junctions".
no code implementations • CVPR 2018 • Bing Li, Chia-Wen Lin, Boxin Shi, Tiejun Huang, Wen Gao, C. -C. Jay Kuo
As compared with traditional video retargeting, stereo video retargeting poses new challenges because stereo video contains the depth information of salient objects and its time dynamics.
no code implementations • ICCV 2017 • Ke Yan, Yonghong Tian, Yao-Wei Wang, Wei Zeng, Tiejun Huang
In this paper, we model the relationship of vehicle images as multiple grains.
no code implementations • 18 Sep 2017 • Xiaobin Liu, Shiliang Zhang, Tiejun Huang, Qi Tian
To conquer these issues, we propose an End-to-End BoWs (E$^2$BoWs) model based on Deep Convolutional Neural Network (DCNN).
no code implementations • 26 Apr 2017 • Ling-Yu Duan, Vijay Chandrasekhar, Shiqi Wang, Yihang Lou, Jie Lin, Yan Bai, Tiejun Huang, Alex ChiChung Kot, Wen Gao
This paper provides an overview of the on-going compact descriptors for video analysis standard (CDVA) from the ISO/IEC moving pictures experts group (MPEG).
no code implementations • 1 Mar 2017 • Yan Bai, Feng Gao, Yihang Lou, Shiqi Wang, Tiejun Huang, Ling-Yu Duan
In this paper, we propose to leverage intra-class variance in metric learning of triplet network to improve the performance of fine-grained recognition.
no code implementations • 1 Mar 2017 • Feng Gao, Yihang Lou, Yan Bai, Shiqi Wang, Tiejun Huang, Ling-Yu Duan
Object detection aims to identify instances of semantic objects of a certain class in images or videos.
1 code implementation • ICCV 2017 • Yemin Shi, Yonghong Tian, Yao-Wei Wang, Tiejun Huang
Despite a lot of research efforts devoted in recent years, how to efficiently learn long-term dependencies from sequences still remains a pretty challenging task.
no code implementations • 16 Nov 2016 • Yemin Shi, Yonghong Tian, Yao-Wei Wang, Tiejun Huang
We also introduce an attention mechanism on the temporal domain to capture the long-term dependence meanwhile finding the salient portions.
no code implementations • 10 Sep 2016 • Yemin Shi, Yonghong Tian, Yao-Wei Wang, Tiejun Huang
Nevertheless, most of the existing features or descriptors cannot capture motion information effectively, especially for long-term motion.
no code implementations • CVPR 2016 • Hongye Liu, Yonghong Tian, Yaowei Yang, Lu Pang, Tiejun Huang
To further facilitate the future research on this problem, we also present a carefully-organized large-scale image database "VehicleID", which includes multiple images of the same vehicle captured by different real-world cameras in a city.
no code implementations • CVPR 2016 • Peixi Peng, Tao Xiang, Yao-Wei Wang, Massimiliano Pontil, Shaogang Gong, Tiejun Huang, Yonghong Tian
Most existing person re-identification (Re-ID) approaches follow a supervised learning framework, in which a large number of labelled matching pairs are required for training.