Xiangfei Sheng

Recent years have witnessed increasing interest towards image aesthetics assessment (IAA), which predicts the aesthetic appeal of images by simulating human perception. The state-of-the-art IAA methods, despite their significant advancements, typically rely heavily on time-consuming and labor-intensive human annotation of aesthetic scores. Furthermore, they are subject to the generalization challenge, which is highly desired in real-world applications. Motivated by this, zero-shot image aesthetics assessment (ZIAA) is investigated to achieve robust model generalization without relying on manual aesthetic annotations, which remains largely underexplored. Specifically, a novel aesthetic prompt learning framework for ZIAA, dubbed AesPrompt, is presented in this paper. The key insight of AesPrompt is to emulate the human aesthetic perception process for learning aesthetic-oriented prompts in a multi-granularity manner. First, we first develop a new pseudo aesthetic distribution generation paradigm based on multi-LLM ensemble. Then, external knowledge of multi-granularity prompts encompassing image themes, emotions, and aesthetics is acquired. Through learning the multi-granularity aesthetic-oriented prompts, the proposed method achieves better generalization and interpretability. Extensive experiments on five IAA benchmarks demonstrate that AesPrompt consistently outperforms the state-of-the-art ZIAA methods across diverse-sourced images, covering natural images, artistic images, and artificial intelligence-generated images.

The highly abstract nature of image aesthetics perception (IAP) poses a significant challenge for current multimodal large language models (MLLMs). The lack of human-annotated multi-modality aesthetic data further exacerbates this dilemma, resulting in MLLMs falling short of aesthetics perception capabilities. To address the above challenge, we first introduce a comprehensively annotated Aesthetic Multi-Modality Instruction Tuning (AesMMIT) dataset, which serves as the footstone for building multi-modality aesthetics foundation models. Specifically, to align MLLMs with human aesthetics perception, we construct a corpus-rich aesthetic critique database with 21,904 diverse-sourced images and 88K human natural language feedbacks, which are collected via progressive questions, ranging from coarse-grained aesthetic grades to fine-grained aesthetic descriptions. To ensure that MLLMs can handle diverse queries, we further prompt GPT to refine the aesthetic critiques and assemble the large-scale aesthetic instruction tuning dataset, i.e. AesMMIT, which consists of 409K multi-typed instructions to activate stronger aesthetic capabilities. Based on the AesMMIT database, we fine-tune the open-sourced general foundation models, achieving multi-modality Aesthetic Expert models, dubbed AesExpert. Extensive experiments demonstrate that the proposed AesExpert models deliver significantly better aesthetic perception performances than the state-of-the-art MLLMs, including the most advanced GPT-4V and Gemini-Pro-Vision. Project Page: https://yipoh.github.io/aes-expert/.

Compared with the unimodal image aesthetics assessment (IAA), multimodal IAA has demonstrated superior performance. This indicates that the critiques could provide rich aesthetics-aware semantic information, which also enhance the explainability of IAA models. However, images are not always accompanied with critiques in real-world situation, rendering multimodal IAA inapplicable in most cases. Therefore, it would be interesting to investigate whether we can generate aesthetic critiques to facilitate image aesthetic representation learning and enhance model explainability. Motivated by these facts, this paper presents an attribute-oriented Critiques Generation framework for explainable IAA, dubbed CG-IAA, which consists of three major components, i.e., Vision-Language Aesthetic Pretraining (VLAP), Multi-Attribute Experts Learning (MAEL) and Multimodal Aesthetics Prediction (MAP). Specifically, the vanilla CLIP is first finetuned on a multimodal IAA database. Considering that the aesthetic critiques typically consist of multiple attributes, a new multimodal IAA database which contains over 1 million critiques with up to four aesthetic attributes is constructed with the language model-based knowledge transfer. Then, CLIP-based multi-attribute experts are trained based on this database. Finally, the pretrained experts are utilized to generate aesthetic critiques for assisting unimodal image aesthetics prediction. Extensive experiments have been done on four popular IAA databases, and the results demonstrate the advantage of CG-IAA over the state-of-the-arts. Furthermore, CG-IAA features better explainability and generalization with the assistance of generated critiques. The source code is available at https://github.com/sxfly99/CG-IAA.

Image aesthetics assessment (IAA) aims at predicting the aesthetic quality of images. Recently, large pre-trained vision-language models, like CLIP, have shown impressive performances on various visual tasks. When it comes to IAA, a straightforward way is to finetune the CLIP image encoder using aesthetic images. However, this can only achieve limited success without considering the uniqueness of multimodal data in the aesthetics domain. People usually assess image aesthetics according to fine-grained visual attributes, e.g., color, light and composition. However, how to learn aesthetics-aware attributes from CLIP-based semantic space has not been addressed before. With this motivation, this paper presents a CLIP-based multi-attribute contrastive learning framework for IAA, dubbed AesCLIP. Specifically, AesCLIP consists of two major components, i.e., aesthetic attribute-based comment classification and attribute-aware learning. The former classifies the aesthetic comments into different attribute categories. Then the latter learns an aesthetic attribute-aware representation by contrastive learning, aiming to mitigate the domain shift from the general visual domain to the aesthetics domain. Extensive experiments have been done by using the pre-trained AesCLIP on four popular IAA databases, and the results demonstrate the advantage of AesCLIP over the state-of-the-arts. The source code will be public at https://github.com/OPPOMKLab/AesCLIP.