116 citations · 264 across the 41 of their papers we have counts for
6 papers · 1 filter
Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks
Zhiyang Chen, Yousong Zhu, Zhaowen Li +8
Visual tasks vary a lot in their output formats and concerned contents, therefore it is hard to process them with an identical structure. One main obstacle lies in the high-dimensi…
Transfering Low-Frequency Features for Domain Adaptation
Zhaowen Li, Xu Zhao, Chaoyang Zhao +2
Previous unsupervised domain adaptation methods did not handle the cross-domain problem from the perspective of frequency for computer vision. The images or feature maps of differe…
Plug-and-Play Pseudo Label Correction Network for Unsupervised Person Re-identification
Tianyi Yan, Kuan Zhu, Haiyun guo +3
Clustering-based methods, which alternate between the generation of pseudo labels and the optimization of the feature extraction network, play a dominant role in both unsupervised…
UniVIP: A Unified Framework for Self-Supervised Visual Pre-training
Zhaowen Li, Yousong Zhu, Fan Yang +9
Self-supervised learning (SSL) holds promise in leveraging large amounts of unlabeled data. However, the success of popular SSL methods has limited on single-centric-object images…
PASS: Part-Aware Self-Supervised Pre-Training for Person Re-Identification
Kuan Zhu, Haiyun Guo, Tianyi Yan +3
In person re-identification (ReID), very recent researches have validated pre-training the models on unlabelled person images is much better than on ImageNet. However, these resear…
Pruning-aware Sparse Regularization for Network Pruning
Nanfei Jiang, Xu Zhao, Chaoyang Zhao +3
Structural neural network pruning aims to remove the redundant channels in the deep convolutional neural networks (CNNs) by pruning the filters of less importance to the final outp…