Aesthetic Image Captioning with Saliency Enhanced MLLMs

• URL: http://arxiv.org/abs/2509.04378v3
• Date: Tue, 09 Sep 2025 08:09:40 GMT
• Title: Aesthetic Image Captioning with Saliency Enhanced MLLMs
• Authors: Yilin Tao, Jiashui Huang, Huaze Xu, Ling Shao,
• Abstract summary: Aesthetic Image Captioning (AIC) aims to generate textual descriptions of image aesthetics.<n>We introduce the Aesthetic Saliency Module (IASM), which efficiently and effectively extracts aesthetic saliency features from images.<n>We also design IAS-ViT as the image encoder for MLLMs, which fuses aesthetic saliency features with original image features via a cross-attention mechanism.
• Score: 26.924932114765596
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aesthetic Image Captioning (AIC) aims to generate textual descriptions of image aesthetics, becoming a key research direction in the field of computational aesthetics. In recent years, pretrained Multimodal Large Language Models (MLLMs) have advanced rapidly, leading to a significant increase in image aesthetics research that integrates both visual and textual modalities. However, most existing studies on image aesthetics primarily focus on predicting aesthetic ratings and have shown limited application in AIC. Existing AIC works leveraging MLLMs predominantly rely on fine-tuning methods without specifically adapting MLLMs to focus on target aesthetic content. To address this limitation, we propose the Aesthetic Saliency Enhanced Multimodal Large Language Model (ASE-MLLM), an end-to-end framework that explicitly incorporates aesthetic saliency into MLLMs. Within this framework, we introduce the Image Aesthetic Saliency Module (IASM), which efficiently and effectively extracts aesthetic saliency features from images. Additionally, we design IAS-ViT as the image encoder for MLLMs, this module fuses aesthetic saliency features with original image features via a cross-attention mechanism. To the best of our knowledge, ASE-MLLM is the first framework to integrate image aesthetic saliency into MLLMs specifically for AIC tasks. Extensive experiments demonstrated that our approach significantly outperformed traditional methods and generic MLLMs on current mainstream AIC benchmarks, achieving state-of-the-art (SOTA) performance.

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