Search Results for author: Zhenqiang Li

Found 8 papers, 2 papers with code

Neural Routing by Memory

no code implementations NeurIPS 2021 Kaipeng Zhang, Zhenqiang Li, Zhifeng Li, Wei Liu, Yoichi Sato

However, they use the same procedure sequence for all inputs, regardless of the intermediate features. This paper proffers a simple yet effective idea of constructing parallel procedures and assigning similar intermediate features to the same specialized procedures in a divide-and-conquer fashion.

Ego4D: Around the World in 3,000 Hours of Egocentric Video

no code implementations13 Oct 2021 Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Ilija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Abrham Gebreselasie, Cristina Gonzalez, James Hillis, Xuhua Huang, Yifei HUANG, Wenqi Jia, Weslie Khoo, Jachym Kolar, Satwik Kottur, Anurag Kumar, Federico Landini, Chao Li, Yanghao Li, Zhenqiang Li, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Yuchen Wang, Xindi Wu, Takuma Yagi, Ziwei Zhao, Yunyi Zhu, Pablo Arbelaez, David Crandall, Dima Damen, Giovanni Maria Farinella, Christian Fuegen, Bernard Ghanem, Vamsi Krishna Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Kitani, Haizhou Li, Richard Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato, Jianbo Shi, Mike Zheng Shou, Antonio Torralba, Lorenzo Torresani, Mingfei Yan, Jitendra Malik

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.

De-identification

Spatio-Temporal Perturbations for Video Attribution

no code implementations1 Sep 2021 Zhenqiang Li, Weimin WANG, Zuoyue Li, Yifei HUANG, Yoichi Sato

The attribution method provides a direction for interpreting opaque neural networks in a visual way by identifying and visualizing the input regions/pixels that dominate the output of a network.

Video Understanding

Sat2Vid: Street-view Panoramic Video Synthesis from a Single Satellite Image

no code implementations ICCV 2021 Zuoyue Li, Zhenqiang Li, Zhaopeng Cui, Rongjun Qin, Marc Pollefeys, Martin R. Oswald

For geometrical and temporal consistency, our approach explicitly creates a 3D point cloud representation of the scene and maintains dense 3D-2D correspondences across frames that reflect the geometric scene configuration inferred from the satellite view.

Image Generation

Towards Visually Explaining Video Understanding Networks with Perturbation

1 code implementation1 May 2020 Zhenqiang Li, Weimin WANG, Zuoyue Li, Yifei HUANG, Yoichi Sato

''Making black box models explainable'' is a vital problem that accompanies the development of deep learning networks.

Video Understanding

Manipulation-skill Assessment from Videos with Spatial Attention Network

no code implementations9 Jan 2019 Zhenqiang Li, Yifei Huang, Minjie Cai, Yoichi Sato

Recent advances in computer vision have made it possible to automatically assess from videos the manipulation skills of humans in performing a task, which breeds many important applications in domains such as health rehabilitation and manufacturing.

Mutual Context Network for Jointly Estimating Egocentric Gaze and Actions

no code implementations7 Jan 2019 Yifei Huang, Zhenqiang Li, Minjie Cai, Yoichi Sato

In this work, we address two coupled tasks of gaze prediction and action recognition in egocentric videos by exploring their mutual context.

Action Recognition Gaze Prediction

Predicting Gaze in Egocentric Video by Learning Task-dependent Attention Transition

2 code implementations ECCV 2018 Yifei Huang, Minjie Cai, Zhenqiang Li, Yoichi Sato

We present a new computational model for gaze prediction in egocentric videos by exploring patterns in temporal shift of gaze fixations (attention transition) that are dependent on egocentric manipulation tasks.

Gaze Prediction Saliency Prediction

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