arXiv AI
Re-calibrated Contrastive Loss for Transformation-Aware Prompt Conditioning in Vision-Language Models
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Domain Adaptation with a Single Vision-Language Embedding
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From Inference to Adaptation: A Unified Optimal Transport View of Vision Language Model
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GATE: Reliability-Gated Gaussian Evidence Fusion for Training-Free Test-Time Adaptation of Vision-Language Models
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ENCORE: Entropy-Guided Cropping and Attention Regularization for Robust Vision--Language Understanding
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MLLMCLIP: Feature-Level Distillation of MLLM for Robust Vision-Language Representations
MLLMCLIP introduces a heterogeneous distillation framework that transfers multimodal knowledge from a generative Multimodal Large Language Model (MLLM) teacher directly into a discriminative CLIP student, eliminating the need for synthetic hard negatives. The method uses an attention-based per-layer token selection and a CKA-based distillation loss to bridge architectural differences between the two models. As a result, MLLMCLIP achieves state‑of‑the‑art compositional accuracy and improves zero‑shot classification and image‑text retrieval performance.