TEVI: Text-Conditioned Editing of Visual Representations via Sparse Autoencoders for Improved Vision-Language Alignment
arXiv:2606.07451v1 Announce Type: cross Abstract: Vision-language models such as CLIP are highly useful for diverse tasks due to their shared image-text embedding space. Despite this, the image and text embeddings are often poorly aligned, affecting downstream performance. Recent work has shown that this can be attributed to an information imbalance: images contain more information than their captions describe. In this work, we propose TEVI, a framework that uses captions as a signal for what to retain from image embeddings. Specifically, we use sparse autoencoders to disentangle image embeddi
The continuous evolution of vision-language models necessitates ongoing research into improving their core functionalities and addressing inherent limitations, such as alignment issues.
Improved vision-language alignment can significantly enhance the performance and applicability of AI systems across diverse tasks, from content generation to autonomous agents.
Approaches to refining shared image-text embedding spaces will evolve, potentially leading to more robust and reliable multimodal AI applications.
- · AI researchers
- · Multimodal AI developers
- · Companies leveraging vision-language models
- · Systems with poorly aligned vision-language embeddings
More accurate and efficient vision-language models will be developed.
This will enable more sophisticated and reliable AI agents and content creation tools.
Enhanced AI capabilities could accelerate the adoption of autonomous systems in various industries, leading to productivity gains but also shifts in human-computer interaction paradigms.
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Read at arXiv cs.LG