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20192025
most citedClass-wise Dynamic Graph Convolution for Semantic Segmentation

16 citations · 76 across the 17 of their papers we have counts for

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Showing 2020Show all

6 papers · 1 filter

cs.CV2020★ 1 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…

cs.CV2020★ 3 cited

SAMOT: Switcher-Aware Multi-Object Tracking and Still Another MOT Measure

Weitao Feng, Zhihao Hu, Baopu Li +3

Multi-Object Tracking (MOT) is a popular topic in computer vision. However, identity issue, i.e., an object is wrongly associated with another object of a different identity, still…

cs.CV2020★ 9 cited

Collaborative Distillation in the Parameter and Spectrum Domains for Video Action Recognition

Haisheng Su, Jing Su, Dongliang Wang +5

Recent years have witnessed the significant progress of action recognition task with deep networks. However, most of current video networks require large memory and computational r…

cs.CV2020

BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation

Haisheng Su, Weihao Gan, Wei Wu +2

Generating human action proposals in untrimmed videos is an important yet challenging task with wide applications. Current methods often suffer from the noisy boundary locations an…

cs.CV2020★ 16 cited

Class-wise Dynamic Graph Convolution for Semantic Segmentation

Hanzhe Hu, Deyi Ji, Weihao Gan +3

Recent works have made great progress in semantic segmentation by exploiting contextual information in a local or global manner with dilated convolutions, pyramid pooling or self-a…

cs.CV2020★ 7 cited

Hierarchical Feature Embedding for Attribute Recognition

Jie Yang, Jiarou Fan, Yiru Wang +4

Attribute recognition is a crucial but challenging task due to viewpoint changes, illumination variations and appearance diversities, etc. Most of previous work only consider the a…