3 citations · 5 across the 12 of their papers we have counts for
7 papers · 1 filter
VDC-Agent: When Video Detailed Captioners Evolve Themselves via Agentic Self-Reflection
Qiang Wang, Xinyuan Gao, Yuhang He +5
Existing Video Detailed Captioning (VDC) methods predominantly rely on costly human annotations or distillation from powerful proprietary models, creating a dependency on external…
Consistent Supervised-Unsupervised Alignment for Generalized Category Discovery
Jizhou Han, Shaokun Wang, Yuhang He +5
Generalized Category Discovery (GCD) focuses on classifying known categories while simultaneously discovering novel categories from unlabeled data. However, previous GCD methods fa…
Unleashing the Potential of All Test Samples: Mean-Shift Guided Test-Time Adaptation
Jizhou Han, Chenhao Ding, SongLin Dong +3
Visual-language models (VLMs) like CLIP exhibit strong generalization but struggle with distribution shifts at test time. Existing training-free test-time adaptation (TTA) methods…
Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need
Qiang Wang, Xiang Song, Yuhang He +4
Deep neural networks (DNNs) often underperform in real-world, dynamic settings where data distributions change over time. Domain Incremental Learning (DIL) offers a solution by ena…
Beyond CLIP Generalization: Against Forward&Backward Forgetting Adapter for Continual Learning of Vision-Language Models
Songlin Dong, Chenhao Ding, Jiangyang Li +4
This study aims to address the problem of multi-domain task incremental learning~(MTIL), which requires that vision-language models~(VLMs) continuously acquire new knowledge while…
DualCP: Rehearsal-Free Domain-Incremental Learning via Dual-Level Concept Prototype
Qiang Wang, Yuhang He, SongLin Dong +4
Domain-Incremental Learning (DIL) enables vision models to adapt to changing conditions in real-world environments while maintaining the knowledge acquired from previous domains. G…
Learn by Reasoning: Analogical Weight Generation for Few-Shot Class-Incremental Learning
Jizhou Han, Chenhao Ding, Yuhang He +4
Few-shot class-incremental Learning (FSCIL) enables models to learn new classes from limited data while retaining performance on previously learned classes. Traditional FSCIL metho…