# Multimodal Commonsense Knowledge Distillation for Visual Question Answering

Source: [Multimodal Commonsense Knowledge Distillation for Visual Question Answering](<https://www.sota2.com/research/paper/68fa758a1e209131cea6879d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:36:51.485Z

Authors: Shuo Yang, Siwen Luo, Soyeon Caren Han

Published: 2024-11-05T01:37:16.000Z

## Abstract

Existing Multimodal Large Language Models (MLLMs) and Visual Language Pretrained Models (VLPMs) have shown remarkable performances in the general Visual Question Answering (VQA). However, these models struggle with VQA questions that require external commonsense knowledge due to the challenges in generating high-quality prompts and the high computational costs of fine-tuning. In this work, we propose a novel graph-based multimodal commonsense knowledge distillation framework that constructs a unified relational graph over commonsense knowledge, visual objects and questions through a Graph Convolutional Network (GCN) following a teacher-student environment. This proposed framework is flexible with any type of teacher and student models without further fine-tuning, and has achieved competitive performances on the ScienceQA dataset.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2411.02722>)

[PDF](<https://arxiv.org/pdf/2411.02722.pdf>)