activity
20162026
most citedLearning Spatio-Temporal Representation with Pseudo-3D Residual Networks

251 citations · 442 across the 40 of their papers we have counts for

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Showing 2019Show all

6 papers · 1 filter

cs.CV2019★ 3 cited

Scheduled Differentiable Architecture Search for Visual Recognition

Zhaofan Qiu, Ting Yao, Yiheng Zhang +2

Convolutional Neural Networks (CNN) have been regarded as a capable class of models for visual recognition problems. Nevertheless, it is not trivial to develop generic and powerful…

cs.CV2019

Gaussian Temporal Awareness Networks for Action Localization

Fuchen Long, Ting Yao, Zhaofan Qiu +3

Temporally localizing actions in a video is a fundamental challenge in video understanding. Most existing approaches have often drawn inspiration from image object detection and ex…

cs.CV2019★ 12 cited

Customizable Architecture Search for Semantic Segmentation

Yiheng Zhang, Zhaofan Qiu, Jingen Liu +3

In this paper, we propose a Customizable Architecture Search (CAS) approach to automatically generate a network architecture for semantic image segmentation. The generated network…

cs.CV2019★ 2 cited

vireoJD-MM at Activity Detection in Extended Videos

Fuchen Long, Qi Cai, Zhaofan Qiu +4

This notebook paper presents an overview and comparative analysis of our system designed for activity detection in extended videos (ActEV-PC) in ActivityNet Challenge 2019. Specifi…

cs.CV2019★ 8 cited

Trimmed Action Recognition, Dense-Captioning Events in Videos, and Spatio-temporal Action Localization with Focus on ActivityNet Challenge 2019

Zhaofan Qiu, Dong Li, Yehao Li +3

This notebook paper presents an overview and comparative analysis of our systems designed for the following three tasks in ActivityNet Challenge 2019: trimmed action recognition, d…

cs.CV2019★ 2 cited

Learning Spatio-Temporal Representation with Local and Global Diffusion

Zhaofan Qiu, Ting Yao, Chong-Wah Ngo +2

Convolutional Neural Networks (CNN) have been regarded as a powerful class of models for visual recognition problems. Nevertheless, the convolutional filters in these networks are…