Paper · arXiv 2402.15021
CLoVe: Encoding Compositional Language in Contrastive Vision-Language Models
Santiago Castro, Amir Ziai, Avneesh Saluja, Zhuoning Yuan, Rada Mihalcea
A new framework enhances compositional language encoding in Vision-Language Models while maintaining or improving object recognition, achieving significant gains on benchmark tests.
Abstract
Recent years have witnessed a significant increase in the performance of Vision and Language tasks. Foundational Vision-Language Models (VLMs), such as CLIP, have been leveraged in multiple settings and demonstrated remarkable performance across several tasks. Such models excel at object-centric recognition yet learn text representations that seem invariant to word order, failing to compose known concepts in novel ways. However, no evidence exists that any VLM, including large-scale single-stream models such as GPT-4V, identifies compositions successfully. In this paper, we introduce a framework to significantly improve the ability of existing models to encode compositional language, with over 10% absolute improvement on compositionality benchmarks, while maintaining or improving the performance on standard object-recognition and retrieval benchmarks. Our code and pre-trained models are publicly available at https://github.com/netflix/clove.