Using Visual Cropping to Enhance Fine-Detail Question Answering of
  BLIP-Family Models
Jiarui Zhang Mahyar Khayatkhoei Prateek Chhikara Filip Ilievski
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.