ImageChain: Advancing Sequential Image-to-Text Reasoning in Multimodal Large Language Models

• URL: http://arxiv.org/abs/2502.19409v1
• Date: Wed, 26 Feb 2025 18:55:06 GMT
• Title: ImageChain: Advancing Sequential Image-to-Text Reasoning in Multimodal Large Language Models
• Authors: Danae Sánchez Villegas, Ingo Ziegler, Desmond Elliott,
• Abstract summary: ImageChain is a framework that enhances MLLMs with sequential reasoning capabilities over image data.<n>Our approach improves performance on the next-scene description task.<n>ImageChain achieves robust zero-shot out-of-domain performance in applications ranging from comics to robotics.
• Score: 12.265270657795275
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reasoning over sequences of images remains a challenge for multimodal large language models (MLLMs). While recent models incorporate multi-image data during pre-training, they still struggle to recognize sequential structures, often treating images independently. This work introduces ImageChain, a framework that enhances MLLMs with sequential reasoning capabilities over image data by modeling visual sequences as a multi-turn conversation. In ImageChain, images are interleaved with corresponding textual descriptions to form a controlled dialogue that explicitly captures temporal dependencies and narrative progression. Our method optimizes for the task of next-scene description, where the model generates a context-aware description of an upcoming scene based on preceding visual and textual cues. We demonstrate that our approach improves performance on the next-scene description task -- achieving an average improvement from 3.7% to 19% in SimRate, a metric that quantifies semantic similarity to human-annotated ground truths. Moreover, ImageChain achieves robust zero-shot out-of-domain performance in applications ranging from comics to robotics. Extensive experiments validate that instruction-tuning in a multimodal, multi-turn conversation design is key to bridging the gap between static image understanding and temporally-aware reasoning.

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