arXiv:2603.19862v2 Announce Type: replace-cross Abstract: Vision-Language Models like CLIP are extensively used for inter-modal tasks which involve both visual and text modalities. However, when the individual modality encoders are applied to inherently intra-modal tasks like image-to-image retrieval, their performance suffers from the intra-modal misalignment. In this paper we study intra-modal misalignment in CLIP with a focus on the role of the projectors that map pre-projection image and text embeddings into the shared embedding space. By analyzing the form of the cosine similarity applied
The continuous evolution of Vision-Language Models (VLMs) like CLIP necessitates ongoing research into their underlying architecture to improve performance across diverse tasks.
Improving the efficiency and effectiveness of VLMs for intra-modal tasks expands their applicability in areas like content moderation, image-to-image retrieval, and fine-grained visual search.
This research offers a method to enhance the performance of CLIP in intra-modal tasks by re-evaluating its projector design, potentially leading to more versatile and robust models.
- · AI researchers
- · developers of vision-language models
- · computer vision applications
- · e-commerce platforms
- · less efficient model architectures
CLIP and similar VLMs will become more adept at image-to-image tasks, reducing 'intra-modal misalignment'.
Enhanced intra-modal capabilities could broaden the commercial deployment of VLMs beyond their current inter-modal strengths.
More efficient and versatile VLMs might accelerate the development of advanced AI agents capable of nuanced visual understanding and retrieval.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG