Using Visual Cropping to Enhance Fine-Detail Question Answering of BLIP-Family Models

TL;DR

This paper investigates how visual cropping, both human and automatic, can improve the performance of BLIP-family models on fine-detail visual question answering, especially in zero-shot settings, by focusing on relevant image regions.

Contribution

The study introduces automatic cropping strategies based on multi-modal embeddings and demonstrates their effectiveness in enhancing BLIP models' accuracy on fine-detail questions.

Findings

Cropping improves BLIP model performance significantly.

Automatic cropping methods are comparable to human cropping.

Performance gains are more notable in zero-shot models and with smaller bounding boxes.

Abstract

Visual Question Answering is a challenging task, as it requires seamless interaction between perceptual, linguistic, and background knowledge systems. While the recent progress of visual and natural language models like BLIP has led to improved performance on this task, we lack understanding of the ability of such models to perform on different kinds of questions and reasoning types. As our initial analysis of BLIP-family models revealed difficulty with answering fine-detail questions, we investigate the following question: Can visual cropping be employed to improve the performance of state-of-the-art visual question answering models on fine-detail questions? Given the recent success of the BLIP-family models, we study a zero-shot and a fine-tuned BLIP model. We define three controlled subsets of the popular VQA-v2 benchmark to measure whether cropping can help model performance. Besides human cropping, we devise two automatic cropping strategies based on multi-modal embedding by CLIP and BLIP visual QA model gradients. Our experiments demonstrate that the performance of BLIP model variants can be significantly improved through human cropping, and automatic cropping methods can produce comparable benefits. A deeper dive into our findings indicates that the performance enhancement is more pronounced in zero-shot models than in fine-tuned models and more salient with smaller bounding boxes than larger ones. We perform case studies to connect quantitative differences with qualitative observations across question types and datasets. Finally, we see that the cropping enhancement is robust, as we gain an improvement of 4.59% (absolute) in the general VQA-random task by simply inputting a concatenation of the original and gradient-based cropped images. We make our code available to facilitate further innovation on visual cropping methods for question answering.