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20192021
most citedLocal-to-Global Self-Attention in Vision Transformers

22 citations · 31 across the 4 of their papers we have counts for

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9 papers · 1 filter

cs.CV2023

Distribution-Aware Calibration for Object Detection with Noisy Bounding Boxes

Donghao Zhou, Jialin Li, Jinpeng Li +7

Large-scale well-annotated datasets are of great importance for training an effective object detector. However, obtaining accurate bounding box annotations is laborious and demandi…

cs.CV2023

3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation

Shizhan Gong, Yuan Zhong, Wenao Ma +5

Despite that the segment anything model (SAM) achieved impressive results on general-purpose semantic segmentation with strong generalization ability on daily images, its demonstra…

cs.CV20231 cited

VSTAR: A Video-grounded Dialogue Dataset for Situated Semantic Understanding with Scene and Topic Transitions

Yuxuan Wang, Zilong Zheng, Xueliang Zhao +3

Video-grounded dialogue understanding is a challenging problem that requires machine to perceive, parse and reason over situated semantics extracted from weakly aligned video and d…

cs.CV2023

PointPatchMix: Point Cloud Mixing with Patch Scoring

Yi Wang, Jiaze Wang, Jinpeng Li +4

Data augmentation is an effective regularization strategy for mitigating overfitting in deep neural networks, and it plays a crucial role in 3D vision tasks, where the point cloud…

cs.CV202122 cited

Local-to-Global Self-Attention in Vision Transformers

Jinpeng Li, Yichao Yan, Shengcai Liao +2

Transformers have demonstrated great potential in computer vision tasks. To avoid dense computations of self-attentions in high-resolution visual data, some recent Transformer mode…

cs.CV20211 cited

When Liebig's Barrel Meets Facial Landmark Detection: A Practical Model

Haibo Jin, Jinpeng Li, Shengcai Liao +1

In recent years, significant progress has been made in the research of facial landmark detection. However, few prior works have thoroughly discussed about models for practical appl…