28 citations · 69 across the 12 of their papers we have counts for
11 papers · 1 filter
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…
Discovery-and-Selection: Towards Optimal Multiple Instance Learning for Weakly Supervised Object Detection
Shiwei Zhang, Wei Ke, Lin Yang
Weakly supervised object detection (WSOD) is a challenging task that requires simultaneously learn object classifiers and estimate object locations under the supervision of image c…
Multiple Anchor Learning for Visual Object Detection
Wei Ke, Tianliang Zhang, Zeyi Huang +3
Classification and localization are two pillars of visual object detectors. However, in CNN-based detectors, these two modules are usually optimized under a fixed set of candidate…
DDNet: Cartesian-polar Dual-domain Network for the Joint Optic Disc and Cup Segmentation
Qing Liu, Xiaopeng Hong, Wei Ke +2
Existing joint optic disc and cup segmentation approaches are developed either in Cartesian or polar coordinate system. However, due to the subtle optic cup, the contextual informa…
Improving Object Detection with Inverted Attention
Zeyi Huang, Wei Ke, Dong Huang
Improving object detectors against occlusion, blur and noise is a critical step to deploy detectors in real applications. Since it is not possible to exhaust all image defects thro…
SRN: Side-output Residual Network for Object Reflection Symmetry Detection and Beyond
Wei Ke, Jie Chen, Jianbin Jiao +2
In this paper, we establish a baseline for object reflection symmetry detection in complex backgrounds by presenting a new benchmark and an end-to-end deep learning approach, openi…