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Vision-language models (VLMs) achieve strong zero-shot transferability but remain vulnerable to target-domain shifts at inference time. Test-time adaptation (TTA) offers a practical remedy, yet most existing VLM-TTA methods follow a prediction-side adaptation paradigm. They use test samples to adjust logits, prototypes...
In this paper, we introduce layer-wise curriculum learning for efficient LLM compression. The proposed method facilitates the knowledge transfer from the teacher model to the student model, utilizing a curriculum learning approach that begins with easier optimization tasks and progressively tackles harder ones. In orde...
OverRep is proposed, an Overcomplete Reparameterization framework for structured LLM pruning that temporarily overparameterizes the recovery module during training to absorb complex knowledge distilled from the original model.
This work addresses limitations in transfer learning for vision-language models through transformation-aware prompt conditioning and a re-calibrated contrastive loss, and treats same-class samples as positives rather than distinct instances, enabling the model to learn domain-specific features more effectively.
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