1 citations · 1 across the 4 of their papers we have counts for
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Category-Prompt Refined Feature Learning for Long-Tailed Multi-Label Image Classification
Jiexuan Yan, Sheng Huang, Nankun Mu +2
Real-world data consistently exhibits a long-tailed distribution, often spanning multiple categories. This complexity underscores the challenge of content comprehension, particular…
SAM-MIL: A Spatial Contextual Aware Multiple Instance Learning Approach for Whole Slide Image Classification
Heng Fang, Sheng Huang, Wenhao Tang +2
Multiple Instance Learning (MIL) represents the predominant framework in Whole Slide Image (WSI) classification, covering aspects such as sub-typing, diagnosis, and beyond. Current…
Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classification
Wenhao Tang, Sheng Huang, Xiaoxian Zhang +3
The whole slide image (WSI) classification is often formulated as a multiple instance learning (MIL) problem. Since the positive tissue is only a small fraction of the gigapixel WS…
Deformable Kernel Expansion Model for Efficient Arbitrary-shaped Scene Text Detection
Tao He, Sheng Huang, Wenhao Tang +1
Scene text detection is a challenging computer vision task due to the high variation in text shapes and ratios. In this work, we propose a scene text detector named Deformable Kern…
Boosting Multi-Label Image Classification with Complementary Parallel Self-Distillation
Jiazhi Xu, Sheng Huang, Fengtao Zhou +3
Multi-Label Image Classification (MLIC) approaches usually exploit label correlations to achieve good performance. However, emphasizing correlation like co-occurrence may overlook…
Regression-based Hypergraph Learning for Image Clustering and Classification
Sheng Huang, Dan Yang, Bo Liu +1
Inspired by the recently remarkable successes of Sparse Representation (SR), Collaborative Representation (CR) and sparse graph, we present a novel hypergraph model named Regressio…