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20202026
most citedReduce Information Loss in Transformers for Pluralistic Image Inpainting

9 citations · 54 across the 32 of their papers we have counts for

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Showing 2022Show all

6 papers · 1 filter

cs.CV2022★ 5 cited

X-Paste: Revisiting Scalable Copy-Paste for Instance Segmentation using CLIP and StableDiffusion

Hanqing Zhao, Dianmo Sheng, Jianmin Bao +9

Copy-Paste is a simple and effective data augmentation strategy for instance segmentation. By randomly pasting object instances onto new background images, it creates new training…

cs.CV2022★ 2 cited

UIA-ViT: Unsupervised Inconsistency-Aware Method based on Vision Transformer for Face Forgery Detection

Wanyi Zhuang, Qi Chu, Zhentao Tan +5

Intra-frame inconsistency has been proved to be effective for the generalization of face forgery detection. However, learning to focus on these inconsistency requires extra pixel-l…

cs.CV2022★ 2 cited

Counterfactual Intervention Feature Transfer for Visible-Infrared Person Re-identification

Xulin Li, Yan Lu, Bin Liu +7

Graph-based models have achieved great success in person re-identification tasks recently, which compute the graph topology structure (affinities) among different people first and…

cs.CV2022

Towards Intrinsic Common Discriminative Features Learning for Face Forgery Detection using Adversarial Learning

Wanyi Zhuang, Qi Chu, Haojie Yuan +3

Existing face forgery detection methods usually treat face forgery detection as a binary classification problem and adopt deep convolution neural networks to learn discriminative f…

cs.CV2022★ 9 cited

Reduce Information Loss in Transformers for Pluralistic Image Inpainting

Qiankun Liu, Zhentao Tan, Dongdong Chen +6

Transformers have achieved great success in pluralistic image inpainting recently. However, we find existing transformer based solutions regard each pixel as a token, thus suffer f…

cs.CV2022★ 5 cited

Online Multi-Object Tracking with Unsupervised Re-Identification Learning and Occlusion Estimation

Qiankun Liu, Dongdong Chen, Qi Chu +4

Occlusion between different objects is a typical challenge in Multi-Object Tracking (MOT), which often leads to inferior tracking results due to the missing detected objects. The c…