TEVI Framework Improves Vision-Language Alignment in CLIP Models Using Sparse Autoencoders
Researchers have proposed TEVI, a framework that uses captions and sparse autoencoders to selectively filter image embeddings in vision-language models like CLIP, improving alignment between image and text representations. The work addresses a known information imbalance problem, where images inherently contain more information than their accompanying captions can describe. Improved alignment translates to better retrieval performance across multiple benchmarks, with the largest gains seen on richer, more detailed captions.
TEVI (Text-Conditioned Editing of Visual Representations) is a newly proposed framework targeting a fundamental limitation of vision-language models such as CLIP: the misalignment between image and text embeddings caused by an information imbalance. Because images contain far more information than captions typically describe, joint embedding spaces can be noisy and poorly matched. TEVI addresses this by using sparse autoencoders to disentangle image embeddings, then training a masking module that selectively reconstructs only the portions of an embedding relevant to a given caption. In controlled experiments with synthetic captions, the framework successfully preserves caption-described attributes while discarding irrelevant ones. Applied to CLIP models trained on natural images, TEVI achieves improved retrieval performance on both coarse-grained short-caption benchmarks (MS COCO, Flickr) and fine-grained long-caption benchmarks (IIW, DOCCI), with stronger gains on richer captions and improved robustness on the RoCOCO benchmark. The paper, spanning 20 pages with 13 figures and 14 tables, was submitted to arXiv on June 5, 2026.
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- arXiv cs.CLCenter
TEVI: Text-Conditioned Editing of Visual Representations via Sparse Autoencoders for Improved Vision-Language Alignment
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