aiqbench analysis
This research introduces MLLMCLIP, a distillation framework that transfers knowledge from generative Multimodal Large Language Models to discriminative CLIP students without relying on synthetic data generation. By addressing architectural mismatches through attention-based token selection and CKA losses, the method significantly improves compositional accuracy in vision-language tasks while maintaining strong zero-shot performance. The approach offers a more efficient pathway for enhancing existing pretrained models with advanced reasoning capabilities inherent to larger multimodal systems.
MLLMCLIP: Feature-Level Distillation of MLLM for Robust Vision-Language Representations
arXiv:2608.25575v1 Announce Type: cross Abstract: Pretrained vision-language models such as CLIP excel at zero-shot recognition but often fail at compositionality, particularly attribute-object and relational structures. Recent studies mitigate this issue by augmenting training with synthetic hard negatives generated by a cascade of large language models and text-to-image models, which incurs substantial pipeline overhead. We instead propose MLLMCLIP, a heterogeneous distillation framework that transfers multimodal knowledge directly from a generative Multimodal Large Language Model (MLLM) teacher into a discriminative CLIP student, bypassing synthetic data entirely. To bridge the architectural mismatch between the two paradigms, we introduce an attention-based per-layer token selection and a CKA-based distillation loss. Compared to prior CLIP-enhancement methods, MLLMCLIP achieves state-of-the-art compositional accuracy while delivering consistent gains on standard zero-shot classification and image-text retrieval, showing that feature-level distillation strengthens both compositional and general vision-language representation capability.
Abstract:Pretrained vision-language models such as CLIP excel at zero-shot recognition but often fail at compositionality, particularly attribute-object and relational structures. Recent studies mitigate this issue by augmenting training with synthetic hard negatives generated by a cascade of large language models and text-to-image models, which incurs substantial pipeline overhead. We instead propose MLLMCLIP, a heterogeneous distillation framework that transfers multimodal knowledge directly from a generative Multimodal Large Language Model (MLLM) teacher into a discriminative CLIP student, bypassing synthetic data entirely. To bridge the architectural mismatch between the two paradigms, we introduce an attention-based per-layer token selection and a CKA-based distillation loss. Compared to prior CLIP-enhancement methods, MLLMCLIP achieves state-of-the-art compositional accuracy while delivering consistent gains on standard zero-shot classification and image-text retrieval, showing that feature-level distillation strengthens both compositional and general vision-language representation capability.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2608.25575 [cs.CV]
(or arXiv:2608.25575v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.25575 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 231 words
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