Better Supervised Fine-tuning for VQA: Integer-Only Loss

TL;DR

This paper introduces IOVQA, a novel fine-tuning method for vision language models that uses integer-only labels and a targeted loss to improve video quality assessment accuracy and consistency.

Contribution

The paper presents a new integer-only label construction and loss calculation strategy for fine-tuning VLMs, enhancing their performance in quantitative evaluation tasks.

Findings

Achieved 3rd place in VQualA 2025 GenAI-Bench challenge.

Significantly improved VQA accuracy and consistency.

Demonstrated effectiveness of integer-only labels in fine-tuning.

Abstract

With the rapid advancement of vision language models(VLM), their ability to assess visual content based on specific criteria and dimensions has become increasingly critical for applications such as video-theme consistency assessment and visual quality scoring. However, existing methods often suffer from imprecise results and inefficient loss calculation, which limit the focus of the model on key evaluation indicators. To address this, we propose IOVQA(Integer-only VQA), a novel fine-tuning approach tailored for VLMs to enhance their performance in video quality assessment tasks. The key innovation of IOVQA lies in its label construction and its targeted loss calculation mechanism. Specifically, during dataset curation, we constrain the model's output to integers within the range of [10,50], ensuring numerical stability, and convert decimal Overall_MOS to integer before using them as labels. We also introduce a target-mask strategy: when computing the loss, only the first two-digit-integer of the label is unmasked, forcing the model to learn the critical components of the numerical evaluation. After fine-tuning the Qwen2.5-VL model using the constructed dataset, experimental results demonstrate that the proposed method significantly improves the model's accuracy and consistency in the VQA task, ranking 3rd in VQualA 2025 GenAI-Bench AIGC Video Quality Assessment Challenge -- Track I. Our work highlights the effectiveness of merely leaving integer labels during fine-tuning, providing an effective idea for optimizing VLMs in quantitative evaluation scenarios.