most citedComplementary Pseudo Labels For Unsupervised Domain Adaptation On Person Re-identification

87 citations · 136 across the 4 of their papers we have counts for

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cs.CV2023

ContentCTR: Frame-level Live Streaming Click-Through Rate Prediction with Multimodal Transformer

Jiaxin Deng, Dong Shen, Shiyao Wang +4

In recent years, live streaming platforms have gained immense popularity as they allow users to broadcast their videos and interact in real-time with hosts and peers. Due to the dy…

cs.CV2023

Generation-Guided Multi-Level Unified Network for Video Grounding

Xing Cheng, Xiangyu Wu, Dong Shen +2

Video grounding aims to locate the timestamps best matching the query description within an untrimmed video. Prevalent methods can be divided into moment-level and clip-level frame…

cs.CV20214 cited

MlTr: Multi-label Classification with Transformer

Xing Cheng, Hezheng Lin, Xiangyu Wu +5

The task of multi-label image classification is to recognize all the object labels presented in an image. Though advancing for years, small objects, similar objects and objects wit…

cs.CV202117 cited

CAT: Cross Attention in Vision Transformer

Hezheng Lin, Xing Cheng, Xiangyu Wu +5

Since Transformer has found widespread use in NLP, the potential of Transformer in CV has been realized and has inspired many new approaches. However, the computation required for…

cs.CV202128 cited

ES-Net: Erasing Salient Parts to Learn More in Re-Identification

Dong Shen, Shuai Zhao, Jinming Hu +3

As an instance-level recognition problem, re-identification (re-ID) requires models to capture diverse features. However, with continuous training, re-ID models pay more and more a…

cs.CV202187 cited

Complementary Pseudo Labels For Unsupervised Domain Adaptation On Person Re-identification

Hao Feng, Minghao Chen, Jinming Hu +3

In recent years, supervised person re-identification (re-ID) models have received increasing studies. However, these models trained on the source domain always suffer dramatic perf…