activity
20172021
most citedPose-driven Deep Convolutional Model for Person Re-identification

38 citations · 66 across the 5 of their papers we have counts for

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9 papers · 1 filter

cs.CV20215 cited

Greedy Gradient Ensemble for Robust Visual Question Answering

Xinzhe Han, Shuhui Wang, Chi Su +2

Language bias is a critical issue in Visual Question Answering (VQA), where models often exploit dataset biases for the final decision without considering the image information. As…

cs.CV2020

Label Decoupling Framework for Salient Object Detection

Jun Wei, Shuhui Wang, Zhe Wu +3

To get more accurate saliency maps, recent methods mainly focus on aggregating multi-level features from fully convolutional network (FCN) and introducing edge information as auxil…

cs.CV20202 cited

Correlating Edge, Pose with Parsing

Ziwei Zhang, Chi Su, Liang Zheng +1

According to existing studies, human body edge and pose are two beneficial factors to human parsing. The effectiveness of each of the high-level features (edge and pose) is confirm…

cs.CV2020

Gradually Vanishing Bridge for Adversarial Domain Adaptation

Shuhao Cui, Shuhui Wang, Junbao Zhuo +3

In unsupervised domain adaptation, rich domain-specific characteristics bring great challenge to learn domain-invariant representations. However, domain discrepancy is considered t…

cs.CV201921 cited

SIXray : A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Images

Caijing Miao, Lingxi Xie, Fang Wan +4

In this paper, we present a large-scale dataset and establish a baseline for prohibited item discovery in Security Inspection X-ray images. Our dataset, named SIXray, consists of 1…

cs.CV2018

Iterative Reorganization with Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning

Chen Wei, Lingxi Xie, Xutong Ren +5

Learning visual features from unlabeled image data is an important yet challenging task, which is often achieved by training a model on some annotation-free information. We conside…