Image Aesthetic Assessment (IAA) aims to rate the aesthetic quality of images and has many practical applications. However, existing methods typically rely on limited annotated data for training, leading to two key issues: 1) score-only predictions lack interpretability, making it hard for users to understand the reasoning behind ratings; 2) the assessment ability learned through supervised training struggles to generalize to scenarios beyond the training data. To address these challenges, we leverage Multi-Modal Large Language Models (MLLMs) for interpretable image aesthetic assessment. Drawing inspiration from the human aesthetic perception process, we propose two key components: aesthetic attribute assessment (AAA) and scene-aware in-context learning (ICL). AAA is to provide detailed attribute-based analysis by prompting with evaluation criteria. Meanwhile, scene-aware ICL is to improve the model’s understanding of the aesthetic scoring principles across different scenes with given corresponding references. The output of these two components is used to guide the model to provide ratings and interpretations for more reliable and understandable results. Experiments across multiple datasets show the effectiveness of our framework in enhancing MLLM’s aesthetic perception ability and underscore its potential for interpretable IAA.

Rumors proliferating on social media pose significant risks to politics, economics, and society. A considerable amount of research focuses on improving automated rumor detection models on dedicated datasets, but their performance often plummets when applied to novel, unforeseen events due to varying data distributions. Inspired by domain adaptation and few-shot learning, we propose the Multi-Source Knowledge Adaptation framework MSKA. It is a plug-and-play framework where any rumor detection model can be integrated to enhance its performance in detecting contemporary events. Specifically, MSKA relies on event-level knowledge adaptation and cross-domain category alignment to compel the rumor detection model to learn more discriminative knowledge representations. Experiments on the public dataset demonstrate that MSKA improves the performance of the base rumor detection model in detecting emerging events and outperforms other baselines. In terms of accuracy, MSKA achieves at least a 7% improvement over the base rumor detection model.

Since rumors widely spread on social networks can cause serious negative impacts, a batch of studies have investigated how to detect rumors. Most of them rely on existing datasets and try to improve the detection performance on those known datasets, but their performance drops significantly when detecting newly emerging events. This is not in line with the original intention of the rumor detection task. To tackle this issue, we formulate the rumor detection problem as a domain adaption task and propose the Class-based Adversarial Domain Adaptive framework, CADA, which is a general model framework where any latest rumor detection methods can be plugged-in. The improvement of new emerging rumor event detection is based on adversarial training. Specifically, CADA considers class-based discriminators to achieve fine-grained alignment of declarations to be detected of different classes. Experiments on three public datasets show that CADA can improve the detection performance of existing rumor detection models and achieve better results than state-of-the-art models. In terms of accuracy, the performance is improved by at least 3% compared with the original base model in the PHEME dataset, and as high as 10% in the Twitter datasets.