SAM-DCE: Addressing Token Uniformity and Semantic Over-Smoothing in Medical Segmentation
The Segment Anything Model (SAM) demonstrates impressive zero-shot segmentation ability on natural images but encounters difficulties in medical imaging due to domain shifts, anatomical variability, and its reliance on user-provided prompts. Recent prompt-free adaptations alleviate the need for expert intervention, yet still suffer from limited robustness and adaptability, often overlooking the issues of semantic over-smoothing and token uniformity. We propose SAM-DCE, which balances local discrimination and global semantics while mitigating token uniformity, enhancing inter-class separability, and enriching mask decoding with fine-grained, consistent representations. Extensive experiments on diverse medical benchmarks validate its effectiveness.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Addressing Token Uniformity in Transformers via Singular Value Transformation
Token uniformity is commonly observed in transformer-based models, in which different tokens share a large proportion of similar information after going through stacked multiple self-attention layers in a transformer. In…
Semantic Textual SimilarityMitigating Over-smoothing in Transformers via Regularized Nonlocal Functionals
Transformers have achieved remarkable success in a wide range of natural language processing and computer vision applications. However, the representation capacity of a deep transformer model is degraded due to the over-…
Mitigating Over-smoothing in Transformers via Regularized Nonlocal Functionals
Transformers have achieved remarkable success in a wide range of natural language processing and computer vision applications. However, the representation capacity of a deep transformer model is degraded due to the over-…
Image SegmentationLanguage ModelingLanguage ModellingSemantic SegmentationEvoMoE: Expert Evolution in Mixture of Experts for Multimodal Large Language Models
Recent advancements have shown that the Mixture of Experts (MoE) approach significantly enhances the capacity of large language models (LLMs) and improves performance on downstream tasks. Building on these promising resu…
Mixture-of-ExpertsMMETextVQASemantic Label Smoothing for Sequence to Sequence Problems
Label smoothing has been shown to be an effective regularization strategy in classification, that prevents overfitting and helps in label de-noising. However, extending such methods directly to seq2seq settings, such as …
Machine TranslationTranslation