28 citations · 69 across the 12 of their papers we have counts for
13 papers
UniRefiner: Teaching Pre-trained ViTs to Self-Dispose Dross via Contrastive Register
Congpei Qiu, Zhaoyu Hu, Wei Ke +3
Representation learning with Vision Transformers (ViTs) has advanced rapidly, yet the utility of large-scale models in spatially sensitive tasks is hindered by spurious tokens. Pri…
Crowd Counting with Sparse Annotation
Shiwei Zhang, Zhengzheng Wang, Qing Liu +3
This paper presents a new annotation method called Sparse Annotation (SA) for crowd counting, which reduces human labeling efforts by sparsely labeling individuals in an image. We…
De-coupling and De-positioning Dense Self-supervised Learning
Congpei Qiu, Tong Zhang, Wei Ke +2
Dense Self-Supervised Learning (SSL) methods address the limitations of using image-level feature representations when handling images with multiple objects. Although the dense fea…
Spatiotemporal Self-supervised Learning for Point Clouds in the Wild
Yanhao Wu, Tong Zhang, Wei Ke +2
Self-supervised learning (SSL) has the potential to benefit many applications, particularly those where manually annotating data is cumbersome. One such situation is the semantic s…
CoupAlign: Coupling Word-Pixel with Sentence-Mask Alignments for Referring Image Segmentation
Zicheng Zhang, Yi Zhu, Jianzhuang Liu +2
Referring image segmentation aims at localizing all pixels of the visual objects described by a natural language sentence. Previous works learn to straightforwardly align the sente…
Leverage Your Local and Global Representations: A New Self-Supervised Learning Strategy
Tong Zhang, Congpei Qiu, Wei Ke +2
Self-supervised learning (SSL) methods aim to learn view-invariant representations by maximizing the similarity between the features extracted from different crops of the same imag…