1 citations · 1 across the 7 of their papers we have counts for
7 papers
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…
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…
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…
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…
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…
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…
Teacher Agent: A Knowledge Distillation-Free Framework for Rehearsal-based Video Incremental Learning
Shengqin Jiang, Yaoyu Fang, Haokui Zhang +4
Rehearsal-based video incremental learning often employs knowledge distillation to mitigate catastrophic forgetting of previously learned data. However, this method faces two major…