Advancing Compositional Awareness in CLIP with Efficient Fine-Tuning

Amit Peleg, Naman Deep Singh, Matthias Hein

Keywords: Multi-modal learning, Compositionality, CLIP

TL;DR: A fine-tuning method for Compositionally-aware Learning in CLIP

Abstract:

Vision-language models like CLIP have demonstrated remarkable zero-shot capabilities in classification and retrieval.
However, these models often struggle with compositional reasoning - the ability to understand the relationships between concepts. A recent benchmark, SugarCrepe++, reveals that previous
works on improving compositionality have mainly improved lexical sensitivity but neglected semantic understanding. In addition, downstream retrieval performance often deteriorates, although one would expect that improving compositionality should enhance retrieval. In this work, we introduce CLIC (Compositionally-aware Learning in CLIP), a fine-tuning
method based on a novel training technique combining multiple images and their associated captions. CLIC improves compositionality across architectures as well as differently pre-trained CLIP models, both in terms of lexical and semantic understanding, and achieves consistent gains in retrieval performance. This even applies to the recent CLIPS, which achieves SOTA retrieval performance. Nevertheless, the short fine-tuning with CLIC leads to an improvement in retrieval and to the best compositional CLIP model on SugarCrepe++.
All our models and code are available at https://clic-compositional-clip.github.io.

Primary Area: Deep learning (e.g., architectures, generative models, optimization for deep networks, foundation models, LLMs)

Submission Number: 20424