4 papers
Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation
Tiansheng Wen, Yifei Wang, Zequn Zeng +7
Many large-scale systems rely on high-quality deep representations (embeddings) to facilitate tasks like retrieval, search, and generative modeling. Matryoshka Representation Learn…
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…
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…
HICEScore: A Hierarchical Metric for Image Captioning Evaluation
Zequn Zeng, Jianqiao Sun, Hao Zhang +5
Image captioning evaluation metrics can be divided into two categories, reference-based metrics and reference-free metrics. However, reference-based approaches may struggle to eval…