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Marco Mistretta

3 accepted papers

2026

IsoCLIP: Decomposing CLIP Projectors for Efficient Intra-modal Alignment

CVPR 2026

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 misalign

Cited by 0SourcecodeScholar
2026

SpectralGCD: Spectral Concept Selection and Cross-modal Representation Learning for Generalized Category Discovery

ICLR 2026poster

Generalized Category Discovery (GCD) aims to identify novel categories in unlabeled data while leveraging a small labeled subset of known classes. Training a parametric classifier solely on image features often leads to overfitting to old classes, and recent multimodal approaches improve performance…

Cited by 0SourcecodeScholar
2025

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion

ICLR 2025poster

Pre-trained multi-modal Vision-Language Models like CLIP are widely used off-the-shelf for a variety of applications. In this paper, we show that the common practice of individually exploiting the text or image encoders of these powerful multi-modal models is highly suboptimal for intra-modal tasks…