activity
20182021
most citedComputation Reallocation for Object Detection

30 citations · 86 across the 12 of their papers we have counts for

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14 papers · 1 filter

cs.CV20212 cited

Transferable Knowledge-Based Multi-Granularity Aggregation Network for Temporal Action Localization: Submission to ActivityNet Challenge 2021

Haisheng Su, Peiqin Zhuang, Yukun Li +4

This technical report presents an overview of our solution used in the submission to 2021 HACS Temporal Action Localization Challenge on both Supervised Learning Track and Weakly-S…

cs.CV202113 cited

Temporal Context Aggregation Network for Temporal Action Proposal Refinement

Zhiwu Qing, Haisheng Su, Weihao Gan +7

Temporal action proposal generation aims to estimate temporal intervals of actions in untrimmed videos, which is a challenging yet important task in the video understanding field.…

cs.CV20213 cited

Real-Time Visual Object Tracking via Few-Shot Learning

Jinghao Zhou, Bo Li, Peng Wang +5

Visual Object Tracking (VOT) can be seen as an extended task of Few-Shot Learning (FSL). While the concept of FSL is not new in tracking and has been previously applied by prior wo…

cs.CV2021

Higher Performance Visual Tracking with Dual-Modal Localization

Jinghao Zhou, Bo Li, Lei Qiao +5

Visual Object Tracking (VOT) has synchronous needs for both robustness and accuracy. While most existing works fail to operate simultaneously on both, we investigate in this work t…

cs.CV20218 cited

Learning Statistical Texture for Semantic Segmentation

Lanyun Zhu, Deyi Ji, Shiping Zhu +3

Existing semantic segmentation works mainly focus on learning the contextual information in high-level semantic features with CNNs. In order to maintain a precise boundary, low-lev…

cs.CV20201 cited

Context-Aware Graph Convolution Network for Target Re-identification

Deyi Ji, Haoran Wang, Hanzhe Hu +3

Most existing re-identification methods focus on learning robust and discriminative features with deep convolution networks. However, many of them consider content similarity separ…