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20162024
most citedBEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers

115 citations · 139 across the 19 of their papers we have counts for

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9 papers · 1 filter

cs.CV2023

Generic-to-Specific Distillation of Masked Autoencoders

Wei Huang, Zhiliang Peng, Li Dong +3

Large vision Transformers (ViTs) driven by self-supervised pre-training mechanisms achieved unprecedented progress. Lightweight ViT models limited by the model capacity, however, b…

cs.CV2023

Spectral Aware Softmax for Visible-Infrared Person Re-Identification

Lei Tan, Pingyang Dai, Qixiang Ye +3

Visible-infrared person re-identification (VI-ReID) aims to match specific pedestrian images from different modalities. Although suffering an extra modality discrepancy, existing m…

cs.CV2023

Unsupervised Domain Adaptation on Person Re-Identification via Dual-level Asymmetric Mutual Learning

Qiong Wu, Jiahan Li, Pingyang Dai +4

Unsupervised domain adaptation person re-identification (Re-ID) aims to identify pedestrian images within an unlabeled target domain with an auxiliary labeled source-domain dataset…

cs.CV2022115 cited

BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers

Zhiliang Peng, Li Dong, Hangbo Bao +2

Masked image modeling (MIM) has demonstrated impressive results in self-supervised representation learning by recovering corrupted image patches. However, most existing studies ope…

cs.CV2022

Point-to-Box Network for Accurate Object Detection via Single Point Supervision

Pengfei Chen, Xuehui Yu, Xumeng Han +7

Object detection using single point supervision has received increasing attention over the years. However, the performance gap between point supervised object detection (PSOD) and…

cs.CV20222 cited

CrossRectify: Leveraging Disagreement for Semi-supervised Object Detection

Chengcheng Ma, Xingjia Pan, Qixiang Ye +3

Semi-supervised object detection has recently achieved substantial progress. As a mainstream solution, the self-labeling-based methods train the detector on both labeled data and u…