activity
20172022
most citedSimultaneously Localize, Segment and Rank the Camouflaged Objects

50 citations · 177 across the 15 of their papers we have counts for

collaborators

17 papers

cs.CV20224 cited

Energy-Based Residual Latent Transport for Unsupervised Point Cloud Completion

Ruikai Cui, Shi Qiu, Saeed Anwar +2

Unsupervised point cloud completion aims to infer the whole geometry of a partial object observation without requiring partial-complete correspondence. Differing from existing dete…

cs.CV20222 cited

Salient Object Detection via Bounding-box Supervision

Mengqi He, Jing Zhang, Wenxin Yu

The success of fully supervised saliency detection models depends on a large number of pixel-wise labeling. In this paper, we work on bounding-box based weakly-supervised saliency…

cs.CV20211 cited

Inferring the Class Conditional Response Map for Weakly Supervised Semantic Segmentation

Weixuan Sun, Jing Zhang, Nick Barnes

Image-level weakly supervised semantic segmentation (WSSS) relies on class activation maps (CAMs) for pseudo labels generation. As CAMs only highlight the most discriminative regio…

cs.LG202110 cited

Dense Uncertainty Estimation

Jing Zhang, Yuchao Dai, Mochu Xiang +7

Deep neural networks can be roughly divided into deterministic neural networks and stochastic neural networks.The former is usually trained to achieve a mapping from input space to…

cs.CV202115 cited

Confidence-Aware Learning for Camouflaged Object Detection

Jiawei Liu, Jing Zhang, Nick Barnes

Confidence-aware learning is proven as an effective solution to prevent networks becoming overconfident. We present a confidence-aware camouflaged object detection framework using…

cs.CV202150 cited

Simultaneously Localize, Segment and Rank the Camouflaged Objects

Yunqiu Lv, Jing Zhang, Yuchao Dai +4

Camouflage is a key defence mechanism across species that is critical to survival. Common strategies for camouflage include background matching, imitating the color and pattern of…