Search Results for author:
Found 9 papers, 8 papers with code
Unified Multimodal Understanding and Generation Models: Advances, Challenges, and Opportunities
Despite their respective successes, these two domains have evolved independently, leading to distinct architectural paradigms: While autoregressive-based architectures have dominated multimodal understanding, diffusion-based models have become the cornerstone of image generation.
Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation
To our best knowledge, Mamba-Sea is the first work to explore the generalization of Mamba for medical image segmentation, providing an advanced and promising Mamba-based architecture with strong robustness to domain shifts.
Text and Image Are Mutually Beneficial: Enhancing Training-Free Few-Shot Classification with CLIP
To address these issues, we build a mutual guidance mechanism, that introduces an Image-Guided-Text (IGT) component to rectify varying quality of text prompts through image representations, and a Text-Guided-Image (TGI) component to mitigate the anomalous match of image modality through text representations.
START: A Generalized State Space Model with Saliency-Driven Token-Aware Transformation
Existing DG methods primarily rely on convolutional neural networks (CNNs), which inherently learn texture biases due to their limited receptive fields, making them prone to overfitting source domains.
SETA: Semantic-Aware Token Augmentation for Domain Generalization
In this paper, we study the impact of prior CNN-based augmentation methods on token-based models, revealing their performance is suboptimal due to the lack of incentivizing the model to learn holistic shape information.
Learning Generalizable Models via Disentangling Spurious and Enhancing Potential Correlations
2) From the feature perspective, the simple Tail Interaction module implicitly enhances potential correlations among all samples from all source domains, facilitating the acquisition of domain-invariant representations across multiple domains for the model.
DomainDrop: Suppressing Domain-Sensitive Channels for Domain Generalization
Deep Neural Networks have exhibited considerable success in various visual tasks.
ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalization
However, the local operation of the convolution kernel makes the model focus too much on local representations (e. g., texture), which inherently causes the model more prone to overfit to the source domains and hampers its generalization ability.
PLACE dropout: A Progressive Layer-wise and Channel-wise Dropout for Domain Generalization
Particularly, the proposed method can generate a variety of data variants to better deal with the overfitting issue.
