activity
20152023
most citedTransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

4k citations · 4.5k across the 21 of their papers we have counts for

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18 papers · 1 filter

cs.CV2023

Spatial-Temporal Knowledge-Embedded Transformer for Video Scene Graph Generation

Tao Pu, Tianshui Chen, Hefeng Wu +2

Video scene graph generation (VidSGG) aims to identify objects in visual scenes and infer their relationships for a given video. It requires not only a comprehensive understanding…

cs.CV2023

Learning to In-paint: Domain Adaptive Shape Completion for 3D Organ Segmentation

Mingjin Chen, Yongkang He, Yongyi Lu +1

We aim at incorporating explicit shape information into current 3D organ segmentation models. Different from previous works, we formulate shape learning as an in-painting task, whi…

cs.CV2023

Data-Centric Diet: Effective Multi-center Dataset Pruning for Medical Image Segmentation

Yongkang He, Mingjin Chen, Zhijing Yang +1

This paper seeks to address the dense labeling problems where a significant fraction of the dataset can be pruned without sacrificing much accuracy. We observe that, on standard me…

cs.CV2023★ 3 cited

Open-World Pose Transfer via Sequential Test-Time Adaption

Junyang Chen, Xiaoyu Xian, Zhijing Yang +5

Pose transfer aims to transfer a given person into a specified posture, has recently attracted considerable attention. A typical pose transfer framework usually employs representat…

cs.CV2022★ 6 cited

Making Your First Choice: To Address Cold Start Problem in Vision Active Learning

Liangyu Chen, Yutong Bai, Siyu Huang +4

Active learning promises to improve annotation efficiency by iteratively selecting the most important data to be annotated first. However, we uncover a striking contradiction to th…

cs.CV2022

Exploring Negatives in Contrastive Learning for Unpaired Image-to-Image Translation

Yupei Lin, Sen Zhang, Tianshui Chen +3

Unpaired image-to-image translation aims to find a mapping between the source domain and the target domain. To alleviate the problem of the lack of supervised labels for the source…