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20182022
most citedCoupAlign: Coupling Word-Pixel with Sentence-Mask Alignments for Referring Image Segmentation

16 citations · 33 across the 5 of their papers we have counts for

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6 papers · 1 filter

cs.CV202216 cited

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…

cs.CV20221 cited

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…

cs.CV20193 cited

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…

cs.CV20197 cited

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…

cs.CV20196 cited

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…

cs.CV2018

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…