  In this paper, we present our solution to a Multi-modal Algorithmic Reasoning
Task: SMART-101 Challenge. Different from the traditional visual
question-answering datasets, this challenge evaluates the abstraction,
deduction, and generalization abilities of neural networks in solving
visuolinguistic puzzles designed specifically for children in the 6-8 age
group. We employed a divide-and-conquer approach. At the data level, inspired
by the challenge paper, we categorized the whole questions into eight types and
utilized the llama-2-chat model to directly generate the type for each question
in a zero-shot manner. Additionally, we trained a yolov7 model on the icon45
dataset for object detection and combined it with the OCR method to recognize
and locate objects and text within the images. At the model level, we utilized
the BLIP-2 model and added eight adapters to the image encoder VIT-G to
adaptively extract visual features for different question types. We fed the
pre-constructed question templates as input and generated answers using the
flan-t5-xxl decoder. Under the puzzle splits configuration, we achieved an
accuracy score of 26.5 on the validation set and 24.30 on the private test set.
