4 papers
Bridging Vision and Language Concepts through Optimal Transport Semantic Flow
Chenyang Zhang, Anqi Dong, Guangming Zhu +4
Concept Bottleneck Models (CBMs) promise transparent reasoning by predicting through human-interpretable concepts, yet their effectiveness fundamentally depends on how well visual…
FSD-CAP: Fractional Subgraph Diffusion with Class-Aware Propagation for Graph Feature Imputation
Xin Qiao, Shijie Sun, Anqi Dong +5
Imputing missing node features in graphs is challenging, particularly under high missing rates. Existing methods based on latent representations or global diffusion often fail to p…
Intervening in Black Box: Concept Bottleneck Model for Enhancing Human Neural Network Mutual Understanding
Nuoye Xiong, Anqi Dong, Ning Wang +5
Recent advances in deep learning have led to increasingly complex models with deeper layers and more parameters, reducing interpretability and making their decisions harder to unde…
VoteSplat: Hough Voting Gaussian Splatting for 3D Scene Understanding
Minchao Jiang, Shunyu Jia, Jiaming Gu +4
3D Gaussian Splatting (3DGS) has become horsepower in high-quality, real-time rendering for novel view synthesis of 3D scenes. However, existing methods focus primarily on geometri…