Few-Shot Image Classification and Segmentation as Visual Question
  Answering Using Vision-Language Models
Tian Meng Yang Tao Ruilin Lyu Wuliang Yin
The task of few-shot image classification and segmentation (FS-CS) involves
classifying and segmenting target objects in a query image, given only a few
examples of the target classes. We introduce the Vision-Instructed Segmentation
and Evaluation (VISE) method that transforms the FS-CS problem into the Visual
Question Answering (VQA) problem, utilising Vision-Language Models (VLMs), and
addresses it in a training-free manner. By enabling a VLM to interact with
off-the-shelf vision models as tools, the proposed method is capable of
classifying and segmenting target objects using only image-level labels.
Specifically, chain-of-thought prompting and in-context learning guide the VLM
to answer multiple-choice questions like a human; vision models such as YOLO
and Segment Anything Model (SAM) assist the VLM in completing the task. The
modular framework of the proposed method makes it easily extendable. Our
approach achieves state-of-the-art performance on the Pascal-5i and COCO-20i
datasets.