114 citations · 120 across the 9 of their papers we have counts for
9 papers
Saliency-R1: Enforcing Interpretable and Faithful Vision-language Reasoning via Saliency-map Alignment Reward
Shizhan Gong, Minda Hu, Qiyuan Zhang +2
Vision-language models (VLMs) have achieved remarkable success across diverse tasks. However, concerns about their trustworthiness persist, particularly regarding tendencies to lea…
Concepts from Representations: Post-hoc Concept Bottleneck Models via Sparse Decomposition of Visual Representations
Shizhan Gong, Xiaofan Zhang, Qi Dou
Deep learning has achieved remarkable success in image recognition, yet their inherent opacity poses challenges for deployment in critical domains. Concept-based interpretations ai…
Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge
Kang Wang, Chen Qin, Zhang Shi +46
Deep learning models have achieved state-of-the-art performance in automated Cardiac Magnetic Resonance (CMR) analysis. However, the efficacy of these models is highly dependent on…
Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models
Shizhan Gong, Yankai Jiang, Qi Dou +1
Vision-language models, such as CLIP, have achieved significant success in aligning visual and textual representations, becoming essential components of many multi-modal large lang…
pFedFair: Towards Optimal Group Fairness-Accuracy Trade-off in Heterogeneous Federated Learning
Haoyu Lei, Shizhan Gong, Qi Dou +1
Federated learning (FL) algorithms commonly aim to maximize clients' accuracy by training a model on their collective data. However, in several FL applications, the model's decisio…
A Super-pixel-based Approach to the Stable Interpretation of Neural Networks
Shizhan Gong, Jingwei Zhang, Qi Dou +1
Saliency maps are widely used in the computer vision community for interpreting neural network classifiers. However, due to the randomness of training samples and optimization algo…
Structured Gradient-based Interpretations via Norm-Regularized Adversarial Training
Shizhan Gong, Qi Dou, Farzan Farnia
Gradient-based saliency maps have been widely used to explain the decisions of deep neural network classifiers. However, standard gradient-based interpretation maps, including the…
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…
Diffusion Model based Semi-supervised Learning on Brain Hemorrhage Images for Efficient Midline Shift Quantification
Shizhan Gong, Cheng Chen, Yuqi Gong +5
Brain midline shift (MLS) is one of the most critical factors to be considered for clinical diagnosis and treatment decision-making for intracranial hemorrhage. Existing computatio…