Beyond Scalar Distances: Semantic Attribute Gradients from Frozen MLLMs for Visual Embeddings
arXiv:2606.15134v1 Announce Type: cross Abstract: Vision encoders for retrieval are typically trained with class-label supervision: each training pair reduces to a scalar that uniformly pushes the embedding apart or pulls it together, as if every visual attribute either differed or matched. A multimodal large language model (MLLM), shown the same pair, can articulate those attributes and use them to predict whether the images share a class. We propose \textbf{SAGA}, a framework that turns this language-grounded, attribute-aware perception into a training signal for the encoder itself. Specific
The proliferation of powerful Multimodal Large Language Models (MLLMs) enables more nuanced training signals for visual embeddings beyond simple scalar distances.
This work introduces a method to leverage the rich, language-grounded understanding of MLLMs to enhance the training of vision encoders, leading to more semantically aware and robust visual representations.
Vision encoders can now be trained with a more sophisticated, attribute-aware signal derived from MLLMs, potentially improving retrieval and understanding capabilities significantly over traditional class-label supervision.
- · AI researchers
- · Computer vision companies
- · Generative AI platforms
- · Data annotation services
More accurate and versatile visual search and content understanding systems emerge.
Improved visual embeddings can power advanced robotic perception and autonomous systems.
Enhanced visual intelligence could accelerate breakthroughs in scientific discovery requiring multimodal data analysis.
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Read at arXiv cs.AI