17 papers
Exploring Dualistic Meta-Learning to Enhance Domain Generalization in Open Set Scenarios
Xiran Wang, Jian Zhang, Lei Qi +2
Domain generalization learns from multiple source domains to generalize to unseen target domains. However, it often neglects the realistic case of label mismatch between source and…
StableMind: Source-Free Cross-Subject fMRI Decoding with Regularized Adaptation
Jintao Guo, Lin Wang, Shumeng Li +5
Existing cross-subject fMRI decoding methods typically train a model on multiple scanned subjects and then adapt it to a new subject using substantial paired fMRI-image data. Howev…
One Token, Two Fates: A Unified Framework via Vision Token Manipulation Against MLLMs Hallucination
Zhan Fa, Yue Duan, Jian Zhang +2
Current training-free methods tackle MLLM hallucination with separate strategies: either enhancing visual signals or suppressing text inertia. However, these separate methods are i…
Duala: Dual-Level Alignment of Subjects and Stimuli for Cross-Subject fMRI Decoding
Shumeng Li, Jintao Guo, Jian Zhang +3
Cross-subject visual decoding aims to reconstruct visual experiences from brain activity across individuals, enabling more scalable and practical brain-computer interfaces. However…
Decomposing and Composing: Towards Efficient Vision-Language Continual Learning via Rank-1 Expert Pool in a Single LoRA
Zhan Fa, Yue Duan, Jian Zhang +3
Continual learning (CL) in vision-language models (VLMs) faces significant challenges in improving task adaptation and avoiding catastrophic forgetting. Existing methods usually ha…
MAGIC: Achieving Superior Model Merging via Magnitude Calibration
Yayuan Li, Jian Zhang, Jintao Guo +4
The proliferation of pre-trained models has given rise to a wide array of specialised, fine-tuned models. Model merging aims to merge the distinct capabilities of these specialised…