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cs.CV2025
Interpretability-Aware Vision Transformer
Yao Qiang, Chengyin Li, Prashant Khanduri +1
Vision Transformers (ViTs) have become prominent models for solving various vision tasks. However, the interpretability of ViTs has not kept pace with their promising performance.…
cs.CV2024
AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation
Chengyin Li, Prashant Khanduri, Yao Qiang +3
Segment Anything Model (SAM) is one of the pioneering prompt-based foundation models for image segmentation and has been rapidly adopted for various medical imaging applications. H…
cs.CV2024
Fairness-aware Vision Transformer via Debiased Self-Attention
Yao Qiang, Chengyin Li, Prashant Khanduri +1
Vision Transformer (ViT) has recently gained significant attention in solving computer vision (CV) problems due to its capability of extracting informative features and modeling lo…