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20162022
most citedTowards Defending against Adversarial Examples via Attack-Invariant Features

15 citations · 60 across the 11 of their papers we have counts for

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12 papers · 1 filter

cs.CV20224 cited

Neighbour Consistency Guided Pseudo-Label Refinement for Unsupervised Person Re-Identification

De Cheng, Haichun Tai, Nannan Wang +2

Unsupervised person re-identification (ReID) aims at learning discriminative identity features for person retrieval without any annotations. Recent advances accomplish this task by…

cs.CV20221 cited

Robust Single Image Dehazing Based on Consistent and Contrast-Assisted Reconstruction

De Cheng, Yan Li, Dingwen Zhang +3

Single image dehazing as a fundamental low-level vision task, is essential for the development of robust intelligent surveillance system. In this paper, we make an early effort to…

cs.CV20226 cited

Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margin

Hangyu Li, Nannan Wang, Xi Yang +2

Only parts of unlabeled data are selected to train models for most semi-supervised learning methods, whose confidence scores are usually higher than the pre-defined threshold (i.e.…

cs.CV20226 cited

Semi-parametric Makeup Transfer via Semantic-aware Correspondence

Mingrui Zhu, Yun Yi, Nannan Wang +2

The large discrepancy between the source non-makeup image and the reference makeup image is one of the key challenges in makeup transfer. Conventional approaches for makeup transfe…

cs.CV20213 cited

Single Image Dehazing with An Independent Detail-Recovery Network

Yan Li, De Cheng, Jiande Sun +3

Single image dehazing is a prerequisite which affects the performance of many computer vision tasks and has attracted increasing attention in recent years. However, most existing d…

cs.CV20217 cited

Support-Set Based Cross-Supervision for Video Grounding

Xinpeng Ding, Nannan Wang, Shiwei Zhang +5

Current approaches for video grounding propose kinds of complex architectures to capture the video-text relations, and have achieved impressive improvements. However, it is hard to…