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20162024
most citedFocusing Attention: Towards Accurate Text Recognition in Natural Images

584 citations · 1.9k across the 94 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

cs.CV2023★ 4 cited

Single Domain Dynamic Generalization for Iris Presentation Attack Detection

Yachun Li, Jingjing Wang, Yuhui Chen +2

Iris presentation attack detection (PAD) has achieved great success under intra-domain settings but easily degrades on unseen domains. Conventional domain generalization methods mi…

cs.CV2023★ 1 cited

Multi-view Adversarial Discriminator: Mine the Non-causal Factors for Object Detection in Unseen Domains

Mingjun Xu, Lingyun Qin, Weijie Chen +2

Domain shift degrades the performance of object detection models in practical applications. To alleviate the influence of domain shift, plenty of previous work try to decouple and…

cs.CV2023★ 2 cited

Rethinking the Approximation Error in 3D Surface Fitting for Point Cloud Normal Estimation

Hang Du, Xuejun Yan, Jingjing Wang +2

Most existing approaches for point cloud normal estimation aim to locally fit a geometric surface and calculate the normal from the fitted surface. Recently, learning-based methods…

cs.LG2023★ 29 cited

One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale

Fan Bao, Shen Nie, Kaiwen Xue +7

This paper proposes a unified diffusion framework (dubbed UniDiffuser) to fit all distributions relevant to a set of multi-modal data in one model. Our key insight is -- learning d…

cs.CV2023★ 1 cited

1st Place Solution for ECCV 2022 OOD-CV Challenge Object Detection Track

Wei Zhao, Binbin Chen, Weijie Chen +4

OOD-CV challenge is an out-of-distribution generalization task. To solve this problem in object detection track, we propose a simple yet effective Generalize-then-Adapt (G&A) frame…

cs.CV2023

1st Place Solution for ECCV 2022 OOD-CV Challenge Image Classification Track

Yilu Guo, Xingyue Shi, Weijie Chen +4

OOD-CV challenge is an out-of-distribution generalization task. In this challenge, our core solution can be summarized as that Noisy Label Learning Is A Strong Test-Time Domain Ada…