3 papers
cs.CV2025
Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models
Yan Xie, Zequn Zeng, Hao Zhang +5
Concept Bottleneck Models (CBMs) try to make the decision-making process transparent by exploring an intermediate concept space between the input image and the output prediction. E…
cs.CV2025
Explaining Domain Shifts in Language: Concept erasing for Interpretable Image Classification
Zequn Zeng, Yudi Su, Jianqiao Sun +6
Concept-based models can map black-box representations to human-understandable concepts, which makes the decision-making process more transparent and then allows users to understan…
eess.IV2024
Block Modulating Video Compression: An Ultra Low Complexity Image Compression Encoder for Resource Limited Platforms
Siming Zheng, Yujia Xue, Waleed Tahir +6
We consider the image and video compression on resource limited platforms. An ultra low-cost image encoder, named Block Modulating Video Compression (BMVC) with an encoding complex…