Reproduce “In-Context Learning Improves Compositional Understanding of Vision-Language Models” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Matteo Nulli, Anesa Ibrahimi, Avik Pal, Hoshe Lee, Ivona Najdenkoska
Researchers at the University of Amsterdam investigated how In-Context Learning (ICL) can enhance the compositional understanding of Vision-Language Models (VLMs). Their framework improved generative VLMs' performance on benchmarks like Winoground and ARO, demonstrating a method to interpret relationships between objects in images without additional training.
Key insights
- Different types of Vision-Language Models (VLMs), specifically contrastive and generative, exhibit distinct strengths and weaknesses in compositional understanding tasks.
- In-Context Learning (ICL) effectively enhances the compositional understanding capabilities of generative VLMs, utilizing both synthetically generated and manually annotated image-caption pairs as demonstrations.
- The effectiveness of ICL can be sensitive to domain specificity, as performance improvements seen on some benchmarks did not generalize to others with out-of-domain correspondences.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.