13 citations · 25 across the 14 of their papers we have counts for
14 papers
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…
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…
When Shared Knowledge Hurts: Spectral Over-Accumulation in Model Merging
Yayuan Li, Ze Peng, Jian Zhang +3
Model merging combines multiple fine-tuned models into a single model by adding their weight updates, providing a lightweight alternative to retraining. Existing methods primarily…
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…
On the Implicit Adversariality of Catastrophic Forgetting in Deep Continual Learning
Ze Peng, Jian Zhang, Jintao Guo +3
Continual learning seeks the human-like ability to accumulate new skills in machine intelligence. Its central challenge is catastrophic forgetting, whose underlying cause has not b…
Unified Multimodal Understanding and Generation Models: Advances, Challenges, and Opportunities
Shanshan Zhao, Xinjie Zhang, Jintao Guo +9
Recent years have seen remarkable progress in both multimodal understanding models and image generation models. Despite their respective successes, these two domains have evolved i…
Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation
Zihan Cheng, Jintao Guo, Jian Zhang +4
To segment medical images with distribution shifts, domain generalization (DG) has emerged as a promising setting to train models on source domains that can generalize to unseen ta…
Text and Image Are Mutually Beneficial: Enhancing Training-Free Few-Shot Classification with CLIP
Yayuan Li, Jintao Guo, Lei Qi +2
Contrastive Language-Image Pretraining (CLIP) has been widely used in vision tasks. Notably, CLIP has demonstrated promising performance in few-shot learning (FSL). However, existi…
START: A Generalized State Space Model with Saliency-Driven Token-Aware Transformation
Jintao Guo, Lei Qi, Yinghuan Shi +1
Domain Generalization (DG) aims to enable models to generalize to unseen target domains by learning from multiple source domains. Existing DG methods primarily rely on convolutiona…
SETA: Semantic-Aware Token Augmentation for Domain Generalization
Jintao Guo, Lei Qi, Yinghuan Shi +1
Domain generalization (DG) aims to enhance the model robustness against domain shifts without accessing target domains. A prevalent category of methods for DG is data augmentation,…
Learning Generalizable Models via Disentangling Spurious and Enhancing Potential Correlations
Na Wang, Lei Qi, Jintao Guo +2
Domain generalization (DG) intends to train a model on multiple source domains to ensure that it can generalize well to an arbitrary unseen target domain. The acquisition of domain…
DomainDrop: Suppressing Domain-Sensitive Channels for Domain Generalization
Jintao Guo, Lei Qi, Yinghuan Shi
Deep Neural Networks have exhibited considerable success in various visual tasks. However, when applied to unseen test datasets, state-of-the-art models often suffer performance de…
ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalization
Jintao Guo, Na Wang, Lei Qi +1
Domain generalization (DG) aims to learn a model that generalizes well to unseen target domains utilizing multiple source domains without re-training. Most existing DG works are ba…
PLACE dropout: A Progressive Layer-wise and Channel-wise Dropout for Domain Generalization
Jintao Guo, Lei Qi, Yinghuan Shi +1
Domain generalization (DG) aims to learn a generic model from multiple observed source domains that generalizes well to arbitrary unseen target domains without further training. Th…