arXiv:2608. 09052v1 Announce Type: cross Abstract: Semi-supervised adaptation of vision foundation models (VFMs) commonly freezes the pretrained backbone and updates lightweight modules such as LoRA.
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arXiv:2609.14350v1 Announce Type: new Abstract: Medical vision-language models (VLMs) allow a single model to perform clinical image analysis tasks ranging from diagnosis classification to report gen...
VICAL is a framework for long‑tailed visual recognition that focuses on reducing prediction variance rather than increasing expert diversity. It combines Self‑Consistency Learning, which smooths the loss landscape and mitigates overfitting on tail classes, with Deep Ensemble Distillation, which encourages low‑frequency semantic agreement across experts. Experiments on CIFAR‑LT, ImageNet‑LT, and iNaturalist 2018 demonstrate that VICAL consistently outperforms state‑of‑the‑art methods.
The paper introduces REFINE, a multimodal label‑noise detection framework that leverages both fused and unimodal representations to identify trustworthy examples in noisy supervision settings. By constructing discriminative eigenvectors for target and background classes, REFINE selects trusted representation spaces and measures alignment of instance representations within these spaces, combining the resulting subsets to provide cleaner supervision for the multimodal classifier. Experiments across various tasks show that REFINE outperforms baseline methods, improving model generalization while reducing the impact of mislabeled data.
arXiv:2608. 03432v1 Announce Type: new Abstract: Refurbishment-based noisy-label learning mixes an observed label with a model-derived pseudo target, typically using one sample-wise cleanliness score to control both branches.
arXiv:2607. 24453v1 Announce Type: cross Abstract: Learning from minimal human supervision is a long-standing goal in medical image analysis, where dense expert annotations are costly.
arXiv:2507.00754v3 Announce Type: replace Abstract: The integration of Large Language Model (LLMs) blocks with Vision Transformers (ViTs) holds immense promise for vision-only tasks by leveraging the...