<ul data-eligibleForWebStory="true"><li>Research paper introduces ImageChain, enhancing multimodal large language models with sequential reasoning capabilities over image data.</li><li>ImageChain models visual sequences as a multi-turn conversation by interleaving images with corresponding textual descriptions.</li><li>Framework explicitly captures temporal dependencies and narrative progression in image data.</li><li>Optimizes for the task of next-scene description, where model generates context-aware descriptions based on preceding visual and textual cues.</li><li>Approach improves performance on next-scene description task, showing an average improvement from 3.7% to 19% in SimRate metric.</li><li>ImageChain demonstrates robust zero-shot out-of-domain performance in applications like comics and robotics.</li><li>Extensive experiments validate the importance of instruction-tuning in a multimodal, multi-turn conversation design for enhanced reasoning.</li></ul>

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

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