Zhang, JingZheng, LiangWang, MengGuo, Dan2025-05-232025-05-2397830317285490302-9743http://www.scopus.com/inward/record.url?scp=85209596290&partnerID=8YFLogxKhttps://hdl.handle.net/1885/733752356This paper develops small vision language models to understand visual art, which, given an art work, aims to identify its emotion category and explain this prediction with natural language. While small models are computationally efficient, their capacity is much limited compared with large models. To break this trade-off, this paper builds a small emotional vision language model (SEVLM) by emotion modeling and input-output feature alignment. On the one hand, based on valence-arousal-dominance (VAD) knowledge annotated by psychology experts, we introduce and fuse emotional features derived through VAD dictionary and a VAD head to align VAD vectors of predicted emotion explanation and the ground truth. This allows the vision language model to better understand and generate emotional texts, compared with using traditional text embeddings alone. On the other hand, we design a contrastive head to pull close embeddings of the image, its emotion class, and explanation, which aligns model outputs and inputs. On two public affective explanation datasets, we show that the proposed techniques consistently improve the visual art understanding performance of baseline SEVLMs. Importantly, the proposed model can be trained and evaluated on a single RTX 2080 Ti while exhibiting very strong performance: it not only outperforms the state-of-the-art small models but is also competitive compared with LLaVA 7B after fine-tuning and GPT4(V). The code is available at https://github.com/BetterZH/SEVLM-code.This work is supported by the National Natural Science Foundation of China (62272144, 72188101, 62020106007, and U20A20183), the Major Project of Anhui Province (202203a05020011), and the Fundamental Research Funds for the Central Universities (JZ2024HGTG0309).17en© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.Emotion understandingSmall vision language modelsValence-Arousal-Dominance (VAD) emotion modeling202510.1007/978-3-031-72855-6_2385209596290