UniFine: A Unified and Fine-grained Approach for Zero-shot
  Vision-Language Understanding
Rui Sun Zhecan Wang Haoxuan You Noel Codella Kai-Wei Chang Shih-Fu Chang
Vision-language tasks, such as VQA, SNLI-VE, and VCR are challenging because
they require the model's reasoning ability to understand the semantics of the
visual world and natural language. Supervised methods working for
vision-language tasks have been well-studied. However, solving these tasks in a
zero-shot setting is less explored. Since Contrastive Language-Image
Pre-training (CLIP) has shown remarkable zero-shot performance on image-text
matching, previous works utilized its strong zero-shot ability by converting
vision-language tasks into an image-text matching problem, and they mainly
consider global-level matching (e.g., the whole image or sentence). However, we
find visual and textual fine-grained information, e.g., keywords in the
sentence and objects in the image, can be fairly informative for semantics
understanding. Inspired by this, we propose a unified framework to take
advantage of the fine-grained information for zero-shot vision-language
learning, covering multiple tasks such as VQA, SNLI-VE, and VCR. Our
experiments show that our framework outperforms former zero-shot methods on VQA
and achieves substantial improvement on SNLI-VE and VCR. Furthermore, our
ablation studies confirm the effectiveness and generalizability of our proposed
method. Code will be available at https://github.com/ThreeSR/UniFine