Failure-Informed Image Self-Augmentation for Multimodal Large Language Model Self-Improvement

2026-08-04 · 14:28 UTC ·Chunyang Jiang, Pingping Zhang, Yuzhi Zhao... ·1 min read

Brief

Multimodal large language models (MLLMs) require costly annotated multimodal data; FISA addresses this by generating image augmentations informed by the model's own failures. The method creates answer-preserving, visually challenging perturbations, verifies their utility via self-examination, and filters with a dual-fidelity check to prevent semantic drift. Experiments on VQA benchmarks show consistent in-distribution and OOD gains; full paper text was not available, summary is based on the abstract.

Why it matters

Failure-Informed Image Self-Augmentation (FISA), proposed by Chunyang Jiang et al. (arXiv 2026-08-04, 2608.03733v1), constructs augmented images from the MLLM's own failure cases, producing visually challenging but answer-preserving image 'complications' and applying self-examination plus dual-fidelity filtering to avoid semantic distortion.

Key details

On visual question answering (VQA) benchmarks, FISA consistently improves performance in both in-distribution and out-of-distribution settings; the paper also reports that FISA is compatible with textual self-augmentation and yields synthesized samples that are more data-efficient than generic image-augmentation baselines.

Abstract

Multimodal large language models (MLLMs) have achieved remarkable performance across vision-language tasks, but their progress depends heavily on large-scale, high-quality multimodal data that are costly to annotate. Self-augmentation offers a promising alternative by enabling models to expand their own training data without external supervision. However, existing MLLM self-augmentation methods are largely text-centric, while image augmentation remains underexplored and typically relies on generic or handcrafted transformations that are weakly aligned with the model's actual incapability. We propose Failure-informed Image Self-Augmentation (\textbf{FISA}), a framework for MLLM self-improvement that constructs augmented images from the model's own failure cases. Our method generates visually challenging yet answer-preserving image complications, verifies their utility through self-examination, and applies dual fidelity filtering to avoid semantic distortion. Experiments on visual question answering benchmarks show that the proposed method consistently improves performance across both in-distribution and out-of-distribution settings. Further experiments validate the compatibility of FISA with existing textual self-augmentation approaches, the superior data efficiency of the synthesized samples over generic image augmentation baselines, and the practical effectiveness of the proposed filtering strategy.