[Submitted on 3 Sep 2026]
Title:ArtSociety: Multi-Agent Multimodal Collaboration for Art Emotion Understanding
View a PDF of the paper titled ArtSociety: Multi-Agent Multimodal Collaboration for Art Emotion Understanding, by Jian Li and 7 other authors
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Abstract:The AffectiveArt Multidimensional Art Emotion Understanding task asks to jointly predict an artwork's fine-grained emotion (12 classes, 1549:1 head-to-tail ratio), binary valence/arousal, and five attribute-grounded descriptions -- sub-tasks that exhibit strong empirical trade-offs, so the single-model solutions we tried do not jointly optimize all of them well. We present ArtSociety, a multi-agent framework that assembles heterogeneous multimodal experts -- a DINOv2-Giant vision agent (A1), a scene-grounded CoT fine-tuned MLLM (A2), and three closed-source reasoning agents (A3-A5) -- and coordinates them with two training-free controllers: (i) a rare-class-aware voting arbiter that lowers the agreement threshold for tail emotions, exploiting decorrelated error patterns across agent families; and (ii) a description-first reasoning agent whose DESCRIBE-then-CLASSIFY chain of thought forces visual evidence before label commitment, yielding near-perfect grounded descriptions. A task-routing policy directs the hard emotion task to the full five-agent ensemble while assigning the near-saturated valence/arousal and generative description tasks to the single strongest reasoning agent. On the official test set (1,000 artworks), ArtSociety achieves an Overall Score of 0.8870 (Classification 0.7789, Description 0.9952). An eleven-variant ablation study reveals that, once method and scale saturate at around 0.76, the decisive gains come from agent collaboration and data-side supervision -- a 30B MoE model trained on older data does not outperform an 8B model trained on better data. Code is available at this https URL
Comments: Accepted at the ACM Multimedia 2026 Grand Challenge (AffectiveArt). 8 pages, 5 figures, 3 tables. Code: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
ACM classes: I.2.10; I.2.7; I.4.8; H.5.1
Cite as: arXiv:2609.13240 [cs.CV]
(or arXiv:2609.13240v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.13240
arXiv-issued DOI via DataCite
Related DOI:
https://doi.org/10.1145/3767308.3837723
DOI(s) linking to related resources
Submission history
From: Jian Li [view email] [v1] Thu, 3 Sep 2026 11:25:15 UTC (7,332 KB)
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