most citedAdaptive Context Selection for Polyp Segmentation

12 citations · 19 across the 5 of their papers we have counts for

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cs.CV2024

Towards Flexible 3D Perception: Object-Centric Occupancy Completion Augments 3D Object Detection

Chaoda Zheng, Feng Wang, Naiyan Wang +2

While 3D object bounding box (bbox) representation has been widely used in autonomous driving perception, it lacks the ability to capture the precise details of an object's intrins…

cs.CV20243 cited

Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities

Xu Yan, Haiming Zhang, Yingjie Cai +13

The rise of large foundation models, trained on extensive datasets, is revolutionizing the field of AI. Models such as SAM, DALL-E2, and GPT-4 showcase their adaptability by extrac…

cs.CV20233 cited

LATR: 3D Lane Detection from Monocular Images with Transformer

Yueru Luo, Chaoda Zheng, Xu Yan +4

3D lane detection from monocular images is a fundamental yet challenging task in autonomous driving. Recent advances primarily rely on structural 3D surrogates (e.g., bird's eye vi…

cs.CV20231 cited

WeakPolyp: You Only Look Bounding Box for Polyp Segmentation

Jun Wei, Yiwen Hu, Shuguang Cui +2

Limited by expensive pixel-level labels, polyp segmentation models are plagued by data shortage and suffer from impaired generalization. In contrast, polyp bounding box annotations…

cs.CV202312 cited

Adaptive Context Selection for Polyp Segmentation

Ruifei Zhang, Guanbin Li, Zhen Li +3

Accurate polyp segmentation is of great significance for the diagnosis and treatment of colorectal cancer. However, it has always been very challenging due to the diverse shape and…