3 citations · 5 across the 16 of their papers we have counts for
15 papers · 1 filter
BPG: Balancing Plasticity and Generalization for Domain Incremental Learning
Qiang Wang, Songlin Dong, Shaokun Wang +5
Deep neural networks excel in various tasks but struggle to generalize across evolving data distributions, leading to significant performance degradation under domain shifts. Domai…
Learning Like Humans: Analogical Concept Learning for Generalized Category Discovery
Jizhou Han, Chenhao Ding, Yuhang He +4
Generalized Category Discovery (GCD) seeks to uncover novel categories in unlabeled data while preserving recognition of known categories, yet prevailing visual-only pipelines and…
Trajectory-Diversity-Driven Robust Vision-and-Language Navigation
Jiangyang Li, Cong Wan, SongLin Dong +4
Vision-and-Language Navigation (VLN) requires agents to navigate photo-realistic environments following natural language instructions. Current methods predominantly rely on imitati…
GOAL: Geometrically Optimal Alignment for Continual Generalized Category Discovery
Jizhou Han, Chenhao Ding, SongLin Dong +4
Continual Generalized Category Discovery (C-GCD) requires identifying novel classes from unlabeled data while retaining knowledge of known classes over time. Existing methods typic…
P2L-CA: An Effective Parameter Tuning Framework for Rehearsal-Free Multi-Label Class-Incremental Learning
Songlin Dong, Jiangyang Li, Chenhao Ding +4
Multi-label Class-Incremental Learning aims to continuously recognize novel categories in complex scenes where multiple objects co-occur. However, existing approaches often incur h…
Generative Latent Kernel Modeling for Blind Motion Deblurring
Chenhao Ding, Jiangtao Zhang, Zongsheng Yue +3
Deep prior-based approaches have demonstrated remarkable success in blind motion deblurring (BMD) recently. These methods, however, are often limited by the high non-convexity of t…
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…
Shared & Domain Self-Adaptive Experts with Frequency-Aware Discrimination for Continual Test-Time Adaptation
JianChao Zhao, Chenhao Ding, Songlin Dong +4
This paper focuses on the Continual Test-Time Adaptation (CTTA) task, aiming to enable an agent to continuously adapt to evolving target domains while retaining previously acquired…
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…
Diversity Covariance-Aware Prompt Learning for Vision-Language Models
Songlin Dong, Zhengdong Zhou, Chenhao Ding +3
Prompt tuning can further enhance the performance of visual-language models across various downstream tasks (e.g., few-shot learning), enabling them to better adapt to specific app…
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…
Space Rotation with Basis Transformation for Training-free Test-Time Adaptation
Chenhao Ding, Xinyuan Gao, Songlin Dong +5
With the development of visual-language models (VLM) in downstream task applications, test-time adaptation methods based on VLM have attracted increasing attention for their abilit…
LPT: Less-overfitting Prompt Tuning for Vision-Language Model
Chenhao Ding, Xinyuan Gao, Songlin Dong +5
Vision-language models (VLMs) have demonstrated exceptional generalization capabilities for downstream tasks. Due to its efficiency, prompt learning has gradually become a more eff…