6 papers
Blind Quality Enhancement of Compressed Video via Fine-Grained Degradation-Guided Sequential Inference
Li Yu, Yingbo Zhao, Shiyu Wu +3
Existing studies on quality enhancement for compressed video (QECV) predominantly rely on known quantization parameters (QPs), training separate enhancement models for each QP sett…
Cross-Domain Few-Shot Segmentation via Ordinary Differential Equations over Time Intervals
Huan Ni, Qingshan Liu, Xiaonan Niu +3
Cross-domain few-shot segmentation (CD-FSS) aims to segment unseen categories with very limited samples while alleviating the negative effects of domain shift between the source an…
Teaching Prompts to Coordinate: Hierarchical Layer-Grouped Prompt Tuning for Continual Learning
Shengqin Jiang, Tianqi Kong, Yuankai Qi +5
Prompt-based continual learning methods fine-tune only a small set of additional learnable parameters while keeping the pre-trained model's parameters frozen. It enables efficient…
Unlocking Prototype Potential: An Efficient Tuning Framework for Few-Shot Class-Incremental Learning
Shengqin Jiang, Xiaoran Feng, Yuankai Qi +6
Few-shot class-incremental learning (FSCIL) seeks to continuously learn new classes from very limited samples while preserving previously acquired knowledge. Traditional methods of…
Multiple Instance Learning Framework with Masked Hard Instance Mining for Gigapixel Histopathology Image Analysis
Wenhao Tang, Sheng Huang, Heng Fang +3
Digitizing pathological images into gigapixel Whole Slide Images (WSIs) has opened new avenues for Computational Pathology (CPath). As positive tissue comprises only a small fracti…
ProgRoCC: A Progressive Approach to Rough Crowd Counting
Shengqin Jiang, Linfei Li, Haokui Zhang +6
As the number of individuals in a crowd grows, enumeration-based techniques become increasingly infeasible and their estimates increasingly unreliable. We propose instead an estima…