no code implementations • 4 Apr 2024 • Yin Li, Qi Chen, Kai Wang, Meige Li, Liping Si, Yingwei Guo, Yu Xiong, Qixing Wang, Yang Qin, Ling Xu, Patrick van der Smagt, Jun Tang, Nutan Chen
Multi-modality magnetic resonance imaging data with various sequences facilitate the early diagnosis, tumor segmentation, and disease staging in the management of nasopharyngeal carcinoma (NPC).
1 code implementation • 2 Apr 2024 • Fangzhou Mu, Sicheng Mo, Yin Li
In this paper, we study the effect of cross-modal fusion on the scalability of video grounding models.
no code implementations • 26 Mar 2024 • Fangzhou Mu, Carter Sifferman, Sacha Jungerman, Yiquan Li, Mark Han, Michael Gleicher, Mohit Gupta, Yin Li
We present a method for reconstructing 3D shape of arbitrary Lambertian objects based on measurements by miniature, energy-efficient, low-cost single-photon cameras.
1 code implementation • 22 Feb 2024 • Zhuoyan Xu, Zhenmei Shi, Junyi Wei, Fangzhou Mu, Yin Li, YIngyu Liang
An emerging solution with recent success in vision and NLP involves finetuning a foundation model on a selection of relevant tasks, before its adaptation to a target task with limited labeled samples.
no code implementations • 7 Feb 2024 • Xin Zhao, Shiyu Hu, Yipei Wang, Jing Zhang, Yimin Hu, Rongshuai Liu, Haibin Ling, Yin Li, Renshu Li, Kun Liu, Jiadong Li
These challenges are especially manifested in videos captured by unmanned aerial vehicles (UAV), where the target is usually far away from the camera and often with significant motion relative to the camera.
1 code implementation • 21 Jan 2024 • Yin Li, Yu Xiong, Wenxin Fan, Kai Wang, Qingqing Yu, Liping Si, Patrick van der Smagt, Jun Tang, Nutan Chen
Conclusion: We creatively apply sequential models in the long-term management of SCIT with promising accuracy in the prediction of SCIT nonadherence in Allergic Rhinitis (AR) patients.
no code implementations • 12 Dec 2023 • Sicheng Mo, Fangzhou Mu, Kuan Heng Lin, Yanli Liu, Bochen Guan, Yin Li, Bolei Zhou
Recent approaches such as ControlNet offer users fine-grained spatial control over text-to-image (T2I) diffusion models.
1 code implementation • ICCV 2023 • Matthew Dutson, Yin Li, Mohit Gupta
In this work, we exploit temporal redundancy between subsequent inputs to reduce the cost of Transformers for video processing.
no code implementations • 15 Aug 2023 • Yutong Zhang, Yao Li, Yin Li, Zhichang Guo
Deep neural networks have been widely used in various downstream tasks, especially those safety-critical scenario such as autonomous driving, but deep networks are often threatened by adversarial samples.
1 code implementation • 5 Jul 2023 • Lin Sui, Fangzhou Mu, Yin Li
This report describes our submission to the Ego4D Moment Queries Challenge 2023.
no code implementations • 25 May 2023 • Zhengyang Lou, Huan Xu, Fangzhou Mu, Yanli Liu, XiaoYu Zhang, Liang Shang, Jiang Li, Bochen Guan, Yin Li, Yu Hen Hu
Using a modern game engine, our approach renders crisp clean images and their precise depth maps, based on which high-quality hazy images can be synthesized for training dehazing models.
1 code implementation • CVPR 2023 • Yiwu Zhong, Licheng Yu, Yang Bai, Shangwen Li, Xueting Yan, Yin Li
In this work, we propose to learn video representation that encodes both action steps and their temporal ordering, based on a large-scale dataset of web instructional videos and their narrations, without using human annotations.
no code implementations • 13 Mar 2023 • Zhuoran Yu, Yin Li, Yong Jae Lee
Without relying on model confidence, we propose to measure whether an unlabeled sample is likely to be ``in-distribution''; i. e., close to the current training data.
no code implementations • ICCV 2023 • Felipe Gutierrez-Barragan, Fangzhou Mu, Andrei Ardelean, Atul Ingle, Claudio Bruschini, Edoardo Charbon, Yin Li, Mohit Gupta, Andreas Velten
Single-photon 3D cameras can record the time-of-arrival of billions of photons per second with picosecond accuracy.
1 code implementation • 16 Nov 2022 • Sicheng Mo, Fangzhou Mu, Yin Li
This report describes Badgers@UW-Madison, our submission to the Ego4D Natural Language Queries (NLQ) Challenge.
2 code implementations • 16 Nov 2022 • Fangzhou Mu, Sicheng Mo, Gillian Wang, Yin Li
This report describes our submission to the Ego4D Moment Queries Challenge 2022.
Ranked #1 on
Temporal Action Localization
on Ego4D MQ test
no code implementations • 9 Nov 2022 • Sacha Jungerman, Atul Ingle, Yin Li, Mohit Gupta
Time-resolved image sensors that capture light at pico-to-nanosecond timescales were once limited to niche applications but are now rapidly becoming mainstream in consumer devices.
no code implementations • 15 Oct 2022 • Sizhe An, Yin Li, Umit Ogras
To bridge the gap, we present mRI, a multi-modal 3D human pose estimation dataset with mmWave, RGB-D, and Inertial Sensors.
1 code implementation • 5 Oct 2022 • Yan-Mong Chan, Natascha Manger, Yin Li, Chao-Chin Yang, Zhaohuan Zhu, Philip J. Armitage, Shirley Ho
The simulation data are used to train a U-Net deep learning model to predict gridded three-dimensional representations of the particle density and velocity fields, given as input the corresponding fluid fields.
no code implementations • 24 Jul 2022 • Bhavya Goyal, Jean-François Lalonde, Yin Li, Mohit Gupta
This creates a trade-off between these two kinds of image degradations: motion blur (due to long exposure) vs. noise (due to short exposure), also referred as a dual image corruption pair in this paper.
no code implementations • 28 Jun 2022 • Chirag Modi, Yin Li, David Blei
We show that after a short initial warm-up and training phase, VBS generates better quality of samples than simple VI approaches and reduces the correlation length in the sampling phase by a factor of 10-50 over using only HMC to explore the posterior of initial conditions in 64$^3$ and 128$^3$ dimensional problems, with larger gains for high signal-to-noise data observations.
no code implementations • 13 Jun 2022 • Zhuoran Yu, Yin Li, Yong Jae Lee
However, it has been shown that softmax-based confidence scores in deep networks can be arbitrarily high for samples far from the training data, and thus, the pseudo-labels for even high-confidence unlabeled samples may still be unreliable.
1 code implementation • 9 Jun 2022 • Drew Jamieson, Yin Li, Renan Alves de Oliveira, Francisco Villaescusa-Navarro, Shirley Ho, David N. Spergel
We build a field level emulator for cosmic structure formation that is accurate in the nonlinear regime.
1 code implementation • 9 Jun 2022 • Drew Jamieson, Yin Li, Siyu He, Francisco Villaescusa-Navarro, Shirley Ho, Renan Alves de Oliveira, David N. Spergel
We find our model generalizes well to these well understood scenarios, demonstrating that the networks have inferred general physical principles and learned the nonlinear mode couplings from the complex, random Gaussian training data.
no code implementations • 3 May 2022 • Fangzhou Mu, Sicheng Mo, Jiayong Peng, Xiaochun Liu, Ji Hyun Nam, Siddeshwar Raghavan, Andreas Velten, Yin Li
Computational approach to imaging around the corner, or non-line-of-sight (NLOS) imaging, is becoming a reality thanks to major advances in imaging hardware and reconstruction algorithms.
1 code implementation • 16 Feb 2022 • Chenlin Zhang, Jianxin Wu, Yin Li
Self-attention based Transformer models have demonstrated impressive results for image classification and object detection, and more recently for video understanding.
Ranked #2 on
audio-visual event localization
on UnAV-100
no code implementations • 4 Jan 2022 • Francisco Villaescusa-Navarro, Shy Genel, Daniel Anglés-Alcázar, Lucia A. Perez, Pablo Villanueva-Domingo, Digvijay Wadekar, Helen Shao, Faizan G. Mohammad, Sultan Hassan, Emily Moser, Erwin T. Lau, Luis Fernando Machado Poletti Valle, Andrina Nicola, Leander Thiele, Yongseok Jo, Oliver H. E. Philcox, Benjamin D. Oppenheimer, Megan Tillman, ChangHoon Hahn, Neerav Kaushal, Alice Pisani, Matthew Gebhardt, Ana Maria Delgado, Joyce Caliendo, Christina Kreisch, Kaze W. K. Wong, William R. Coulton, Michael Eickenberg, Gabriele Parimbelli, Yueying Ni, Ulrich P. Steinwandel, Valentina La Torre, Romeel Dave, Nicholas Battaglia, Daisuke Nagai, David N. Spergel, Lars Hernquist, Blakesley Burkhart, Desika Narayanan, Benjamin Wandelt, Rachel S. Somerville, Greg L. Bryan, Matteo Viel, Yin Li, Vid Irsic, Katarina Kraljic, Mark Vogelsberger
The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning.
no code implementations • CVPR 2022 • ran Xu, Fangzhou Mu, Jayoung Lee, Preeti Mukherjee, Somali Chaterji, Saurabh Bagchi, Yin Li
In this paper, we ask, and answer, the wide-ranging question across all MBODFs: How to expose the right set of execution branches and then how to schedule the optimal one at inference time?
no code implementations • 24 Dec 2021 • Jayoung Lee, Pengcheng Wang, ran Xu, Venkat Dasari, Noah Weston, Yin Li, Saurabh Bagchi, Somali Chaterji
First, the system does not consider energy consumption of the models while making a decision on which model to run.
1 code implementation • CVPR 2022 • Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, Jianfeng Gao
However, we show that directly applying such models to recognize image regions for object detection leads to poor performance due to a domain shift: CLIP was trained to match an image as a whole to a text description, without capturing the fine-grained alignment between image regions and text spans.
Ranked #12 on
Open Vocabulary Object Detection
on MSCOCO
(using extra training data)
2 code implementations • 2 Dec 2021 • Matthew Dutson, Yin Li, Mohit Gupta
Video data is often repetitive; for example, the contents of adjacent frames are usually strongly correlated.
no code implementations • CVPR 2022 • Fangzhou Mu, Jian Wang, Yicheng Wu, Yin Li
Our key intuition is that style transfer and view synthesis have to be jointly modeled for this task.
1 code implementation • 11 Nov 2021 • David Schaurecker, Yin Li, Jeremy Tinker, Shirley Ho, Alexandre Refregier
Generative deep learning methods built upon Convolutional Neural Networks (CNNs) provide a great tool for predicting non-linear structure in cosmology.
1 code implementation • 22 Sep 2021 • Francisco Villaescusa-Navarro, Shy Genel, Daniel Angles-Alcazar, Leander Thiele, Romeel Dave, Desika Narayanan, Andrina Nicola, Yin Li, Pablo Villanueva-Domingo, Benjamin Wandelt, David N. Spergel, Rachel S. Somerville, Jose Manuel Zorrilla Matilla, Faizan G. Mohammad, Sultan Hassan, Helen Shao, Digvijay Wadekar, Michael Eickenberg, Kaze W. K. Wong, Gabriella Contardo, Yongseok Jo, Emily Moser, Erwin T. Lau, Luis Fernando Machado Poletti Valle, Lucia A. Perez, Daisuke Nagai, Nicholas Battaglia, Mark Vogelsberger
We present the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) Multifield Dataset, CMD, a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from 2, 000 distinct simulated universes at several cosmic times.
no code implementations • 21 Sep 2021 • Francisco Villaescusa-Navarro, Shy Genel, Daniel Angles-Alcazar, David N. Spergel, Yin Li, Benjamin Wandelt, Leander Thiele, Andrina Nicola, Jose Manuel Zorrilla Matilla, Helen Shao, Sultan Hassan, Desika Narayanan, Romeel Dave, Mark Vogelsberger
We train neural networks to perform likelihood-free inference from $(25\, h^{-1}{\rm Mpc})^2$ 2D maps containing the total mass surface density from thousands of hydrodynamic simulations of the CAMELS project.
no code implementations • 20 Sep 2021 • Francisco Villaescusa-Navarro, Daniel Anglés-Alcázar, Shy Genel, David N. Spergel, Yin Li, Benjamin Wandelt, Andrina Nicola, Leander Thiele, Sultan Hassan, Jose Manuel Zorrilla Matilla, Desika Narayanan, Romeel Dave, Mark Vogelsberger
Although our maps only cover a small area of $(25~h^{-1}{\rm Mpc})^2$, and the different fields are contaminated by astrophysical effects in very different ways, our networks can infer the values of $\Omega_{\rm m}$ and $\sigma_8$ with a few percent level precision for most of the fields.
no code implementations • 16 Sep 2021 • Jiayong Peng, Fangzhou Mu, Ji Hyun Nam, Siddeshwar Raghavan, Yin Li, Andreas Velten, Zhiwei Xiong
Non-line-of-sight (NLOS) imaging is based on capturing the multi-bounce indirect reflections from the hidden objects.
1 code implementation • ICCV 2021 • Yiwu Zhong, Jing Shi, Jianwei Yang, Chenliang Xu, Yin Li
To bridge the gap between images and texts, we leverage an off-the-shelf object detector to identify and localize object instances, match labels of detected regions to concepts parsed from captions, and thus create "pseudo" labels for learning scene graph.
no code implementations • 3 Aug 2021 • Chen-Lin Zhang, Yin Li, Jianxin Wu
Modern deep learning models require large amounts of accurately annotated data, which is often difficult to satisfy.
no code implementations • 28 Jun 2021 • Yunsong Zhao, Yin Li, Zhihan Chen, Tianchong Qiu, Guojin Liu
Using a multi-scale convolution algorithm, the input dimensionality reduction features were mined to obtain shallow features, which then served as inputs into a multi-scale graph convolution algorithm to construct the internal relationships between eigenvalues at different scales.
no code implementations • 2 Jun 2021 • Ji Won Park, Ashley Villar, Yin Li, Yan-Fei Jiang, Shirley Ho, Joshua Yao-Yu Lin, Philip J. Marshall, Aaron Roodman
Among the most extreme objects in the Universe, active galactic nuclei (AGN) are luminous centers of galaxies where a black hole feeds on surrounding matter.
no code implementations • 20 May 2021 • Miao Liu, Lingni Ma, Kiran Somasundaram, Yin Li, Kristen Grauman, James M. Rehg, Chao Li
Given a video captured from a first person perspective and the environment context of where the video is recorded, can we recognize what the person is doing and identify where the action occurs in the 3D space?
no code implementations • 3 May 2021 • Yueying Ni, Yin Li, Patrick Lachance, Rupert A. C. Croft, Tiziana Di Matteo, Simeon Bird, Yu Feng
In this work, we expand and test the capabilities of our recently developed super-resolution (SR) model to generate high-resolution (HR) realizations of the full phase-space matter distribution, including both displacement and velocity, from computationally cheap low-resolution (LR) cosmological N-body simulations.
no code implementations • 7 Apr 2021 • Yin Li, Dhrubajyoti Ghosh, Peeyush Gupta, Sharad Mehrotra, Nisha Panwar, Shantanu Sharma
This paper proposes Prism, a secret sharing based approach to compute private set operations (i. e., intersection and union), as well as aggregates over outsourced databases belonging to multiple owners.
6 code implementations • 7 Feb 2021 • Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh
The scalability of Nystr\"{o}mformer enables application to longer sequences with thousands of tokens.
Ranked #13 on
Semantic Textual Similarity
on MRPC
(F1 metric)
no code implementations • ICCV 2021 • Jing Shi, Yiwu Zhong, Ning Xu, Yin Li, Chenliang Xu
We investigate the weakly-supervised scene graph generation, which is a challenging task since no correspondence of label and object is provided.
no code implementations • 10 Dec 2020 • Chang Chen, Yin Li, Francisco Villaescusa-Navarro, Shirley Ho, Anthony Pullen
Understanding the physics of large cosmological surveys down to small (nonlinear) scales will significantly improve our knowledge of the Universe.
2 code implementations • CVPR 2021 • Bin Li, Yin Li, Kevin W. Eliceiri
We propose a MIL-based method for WSI classification and tumor detection that does not require localized annotations.
1 code implementation • 21 Oct 2020 • ran Xu, Chen-Lin Zhang, Pengcheng Wang, Jayoung Lee, Subrata Mitra, Somali Chaterji, Yin Li, Saurabh Bagchi
In this paper we introduce ApproxDet, an adaptive video object detection framework for mobile devices to meet accuracy-latency requirements in the face of changing content and resource contention scenarios.
no code implementations • 13 Oct 2020 • Yin Li, Yueying Ni, Rupert A. C. Croft, Tiziana Di Matteo, Simeon Bird, Yu Feng
Cosmological simulations of galaxy formation are limited by finite computational resources.
1 code implementation • 1 Oct 2020 • Francisco Villaescusa-Navarro, Daniel Anglés-Alcázar, Shy Genel, David N. Spergel, Rachel S. Somerville, Romeel Dave, Annalisa Pillepich, Lars Hernquist, Dylan Nelson, Paul Torrey, Desika Narayanan, Yin Li, Oliver Philcox, Valentina La Torre, Ana Maria Delgado, Shirley Ho, Sultan Hassan, Blakesley Burkhart, Digvijay Wadekar, Nicholas Battaglia, Gabriella Contardo
We present the Cosmology and Astrophysics with MachinE Learning Simulations --CAMELS-- project.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
1 code implementation • ECCV 2020 • Yiwu Zhong, Li-Wei Wang, Jianshu Chen, Dong Yu, Yin Li
We address the challenging problem of image captioning by revisiting the representation of image scene graph.
1 code implementation • CVPR 2021 • Liwei Wang, Jing Huang, Yin Li, Kun Xu, Zhengyuan Yang, Dong Yu
Our core innovation is the learning of a region-phrase score function, based on which an image-sentence score function is further constructed.
no code implementations • 31 May 2020 • Yin Li, Miao Liu, James M. Rehg
Moving beyond the dataset, we propose a novel deep model for joint gaze estimation and action recognition in FPV.
1 code implementation • CVPR 2020 • Zixuan Huang, Yin Li
Our results compare favorably to state-of-the-art methods on classification tasks, and our method outperforms previous approaches on the localization of object parts.
no code implementations • 27 Apr 2020 • Peeyush Gupta, Yin Li, Sharad Mehrotra, Nisha Panwar, Shantanu Sharma, Sumaya Almanee
Despite exciting progress on cryptography, secure and efficient query processing over outsourced data remains an open challenge.
no code implementations • ICLR 2020 • Fangzhou Mu, YIngyu Liang, Yin Li
We address the challenging problem of deep representation learning--the efficient adaption of a pre-trained deep network to different tasks.
1 code implementation • 29 Nov 2019 • Zachary Slepian, Yin Li, Marcel Schmittfull, Zvonimir Vlah
In analysing these datasets recomputation of these integrals a substantial number of times, for instance to update perturbation theory predictions or covariance matrices as the input linear power spectrum is changed, will be one piece in a Monte Carlo Markov Chain cosmological parameter search: thus the overall savings from our method should be significant.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
1 code implementation • ECCV 2020 • Miao Liu, Siyu Tang, Yin Li, James Rehg
Motivated by this, we adopt intentional hand movement as a future representation and propose a novel deep network that jointly models and predicts the egocentric hand motion, interaction hotspots and future action.
3 code implementations • 11 Sep 2019 • Francisco Villaescusa-Navarro, ChangHoon Hahn, Elena Massara, Arka Banerjee, Ana Maria Delgado, Doogesh Kodi Ramanah, Tom Charnock, Elena Giusarma, Yin Li, Erwan Allys, Antoine Brochard, Chi-Ting Chiang, Siyu He, Alice Pisani, Andrej Obuljen, Yu Feng, Emanuele Castorina, Gabriella Contardo, Christina D. Kreisch, Andrina Nicola, Roman Scoccimarro, Licia Verde, Matteo Viel, Shirley Ho, Stephane Mallat, Benjamin Wandelt, David N. Spergel
The Quijote simulations are a set of 44, 100 full N-body simulations spanning more than 7, 000 cosmological models in the $\{\Omega_{\rm m}, \Omega_{\rm b}, h, n_s, \sigma_8, M_\nu, w \}$ hyperplane.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
no code implementations • 8 Aug 2019 • Deepan Das, Noor Mohammed Ghouse, Shashank Verma, Yin Li
To accomplish this task, our architecture makes use of the rich semantic information available in a joint embedding space of multi-modal data.
no code implementations • 5 Apr 2019 • Miao Liu, Xin Chen, Yun Zhang, Yin Li, James M. Rehg
To this end, we make use of attention modules that learn to highlight regions in the video and aggregate features for recognition.
Ranked #39 on
Action Recognition
on UCF101
no code implementations • NeurIPS 2018 • Yin Li, Abhinav Gupta
Our method further learns to propagate information across all vertices on the graph, and is able to project the learned graph representation back into 2D grids.
1 code implementation • 15 Nov 2018 • Siyu He, Yin Li, Yu Feng, Shirley Ho, Siamak Ravanbakhsh, Wei Chen, Barnabás Póczos
We build a deep neural network, the Deep Density Displacement Model (hereafter D$^3$M), to predict the non-linear structure formation of the Universe from simple linear perturbation theory.
1 code implementation • 14 Nov 2018 • Yin Li, Sukhdeep Singh, Byeonghee Yu, Yu Feng, Uros Seljak
We verify the analytic covariance against the sample covariance from the galaxy mock simulations in two test cases: (1) the power spectrum multipole covariance, and (2) the joint covariance of the projected correlation function and the correlation function multipoles.
Cosmology and Nongalactic Astrophysics
no code implementations • ECCV 2018 • Keizo Kato, Yin Li, Abhinav Gupta
The world of human-object interactions is rich.
no code implementations • ECCV 2018 • Yin Li, Miao Liu, James M. Rehg
We address the task of jointly determining what a person is doing and where they are looking based on the analysis of video captured by a headworn camera.
no code implementations • ACL 2018 • Yitao Cai, Yin Li, Xiaojun Wan
In this paper, we focus on the task of pun location, which aims to identify the pun word in a given short text.
no code implementations • CVPR 2018 • Abhijit Kundu, Yin Li, James M. Rehg
Our method produces a compact 3D representation of the scene, which can be readily used for applications like autonomous driving.
Ranked #3 on
Vehicle Pose Estimation
on KITTI Cars Hard
(using extra training data)
no code implementations • 10 May 2018 • Adithyavairavan Murali, Yin Li, Dhiraj Gandhi, Abhinav Gupta
We believe this is the first attempt at learning to grasp with only tactile sensing and without any prior object knowledge.
no code implementations • 8 Jan 2018 • Yupei Wang, Xin Zhao, Yin Li, Kaiqi Huang
These ConvNet based edge detectors have approached human level performance on standard benchmarks.
2 code implementations • 15 Dec 2017 • Nick Hand, Yu Feng, Florian Beutler, Yin Li, Chirag Modi, Uros Seljak, Zachary Slepian
The package is extensively documented at http://nbodykit. readthedocs. io, which also includes an interactive set of example recipes for new users to explore.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
1 code implementation • 11 Apr 2017 • Liwei Wang, Yin Li, Jing Huang, Svetlana Lazebnik
Image-language matching tasks have recently attracted a lot of attention in the computer vision field.
no code implementations • 7 Apr 2017 • Nick Hand, Yin Li, Zachary Slepian, Uros Seljak
Here, we present a faster, optimal means of using FFTs for this measurement.
Cosmology and Nongalactic Astrophysics
no code implementations • CVPR 2016 • Liwei Wang, Yin Li, Svetlana Lazebnik
This paper proposes a method for learning joint embeddings of images and text using a two-branch neural network with multiple layers of linear projections followed by nonlinearities.
Ranked #15 on
Image Retrieval
on Flickr30K 1K test
no code implementations • CVPR 2016 • Yin Li, Manohar Paluri, James M. Rehg, Piotr Dollár
In this work we present a simple yet effective approach for training edge detectors without human supervision.
no code implementations • CVPR 2015 • Yin Li, Zhefan Ye, James M. Rehg
We propose to utilize these mid-level egocentric cues for egocentric action recognition.
no code implementations • CVPR 2015 • Jia Xu, Lopamudra Mukherjee, Yin Li, Jamieson Warner, James M. Rehg, Vikas Singh
Motivated by these applications, this paper focuses on the problem of egocentric video summarization.
1 code implementation • CVPR 2014 • Yin Li, Xiaodi Hou, Christof Koch, James M. Rehg, Alan L. Yuille
The dataset design bias does not only create the discomforting disconnection between fixations and salient object segmentation, but also misleads the algorithm designing.