From Visual Feedback to Textual Reviews: A Multi-Agent Vision-Language Framework for Image-Grounded Review Assistance

Details

E-commerce platforms are flooded with visual feedback (photos, short videos) that lacks the textual context needed for informed purchasing decisions. Most users do not write detailed text reviews because it requires effort. This paper proposes bridging that gap with AI-assisted review drafting from images alone.\n\nThe proposed framework uses four specialized agents: product grounding (identifying what is in the image), visual sentiment estimation (predicting a rating), visual evidence generation (extracting relevant visual details), and review synthesis (composing the final draft). Intermediate representations — product entities, predicted ratings, evidence summaries — improve interpretability and traceability.\n\nExperiments on a curated subset of the Amazon Reviews Electronics dataset demonstrate that coherent, product-aware, sentiment-consistent review drafts can be generated from product photos. The authors claim this is the first study to formulate image-grounded review assistance as a multi-agent VLM reasoning problem.\n\nThe task is novel and practically motivated, but evaluation is on a single curated dataset and the paper is light on quantitative comparison to alternative approaches. The e-commerce application is commercially interesting but the technical contribution is incremental relative to existing VLM pipelines.