Current image forensics methods often utilize image features from various frequency domains. However, the effective use of these features frequently depends on complex network architectures and a large number of parameters. In this paper, we introduce a lightweight Multi-Clue image forgery detection and localization network (KLMN) along with a novel extractor-adapter framework based on multi-target knowledge distillation. Our extractor-adapter framework effectively integrates RGB features with low-level features, enhancing resilience against various forms of forgery while reducing the number of parameters. We also propose a feature-aggregated decoder that reconstructs the predicted mask by remixing features. The multi-target knowledge distillation process includes intermediate feature distillation, predicted map distillation from the teacher networks, and supervised training using ground truths. Experimental results on multiple datasets demonstrate that our approach can accurately and efficiently detect and localize manipulated regions while meeting the requirements for fewer parameters and reduced memory usage.

Video aesthetic assessment, a vital area in multimedia computing, integrates computer vision with human cognition. Its progress is limited by the lack of standardized datasets and robust models, as the temporal dynamics of video and multimodal fusion challenges hinder direct application of image-based methods. This study introduces VADB, the largest video aesthetic database with 10,490 diverse videos annotated by 37 professionals across multiple aesthetic dimensions, including overall and attribute-specific aesthetic scores, rich language comments and objective tags. We propose VADB-Net, a dual-modal pre-training framework with a two-stage training strategy, which outperforms existing video quality assessment models in scoring tasks and supports downstream video aesthetic assessment tasks. The dataset and source code are available at https://github.com/BestiVictory/VADB.

Datasets play a pivotal role in training visual models, facilitating the development of abstract understandings of visual features through diverse image samples and multidimensional attributes. However, in the realm of aesthetic evaluation of artistic images, datasets remain relatively scarce. Existing painting datasets are often characterized by limited scoring dimensions and insufficient annotations, thereby constraining the advancement and application of automatic aesthetic evaluation methods in the domain of painting.To bridge this gap, we introduce the Aesthetics Paintings and Drawings Dataset (APDD), the first comprehensive collection of paintings encompassing 24 distinct artistic categories and 10 aesthetic attributes. Building upon the initial release of APDDv1, our ongoing research has identified opportunities for enhancement in data scale and annotation precision. Consequently, APDDv2 boasts an expanded image corpus and improved annotation quality, featuring detailed language comments to better cater to the needs of both researchers and practitioners seeking high-quality painting datasets.Furthermore, we present an updated version of the Art Assessment Network for Specific Painting Styles, denoted as ArtCLIP. Experimental validation demonstrates the superior performance of this revised model in the realm of aesthetic evaluation, surpassing its predecessor in accuracy and efficacy.The dataset and model are available at https://github.com/BestiVictory/APDDv2.git.

In this paper we reproduce experimental results presented in our earlier work titled "Focusing on Persons: Colorizing Old Images Learning from Modern Historical Movies" that was presented in the course of the 29th ACM International Conference on Multimedia. The paper aims at verifying the soundness of our prior results and helping others understand our software framework. We present artifacts that help reproduce results that were included in our earlier work. Specifically, this paper contains the technical details of the package, including dataset preparation, source code structure and experimental environment. Using the artifacts we show that our results are reproducible. We invite everyone to use our software framework going beyond reproducibility efforts.

In the tasks of image aesthetic quality assessment, it is difficult to reach both the high score area and low score area due to the normal distribution of aesthetic datasets. To reduce the error in labelling and solve the problem of normal data distribution, we propose a new aesthetic mixed dataset with classification and regression called AMD-CR, and we train a meta reweighting network to reweight the loss of training data differently. In addition, we provide a training strategy according to different stages based on pseudo labels, and then we use it for aesthetic training according to different stages in classification and regression tasks. In the construction of the network structure, we construct an aesthetic adaptive block (AAB) structure that can adapt to any size of the input images. Besides, we also use the efficient channel attention (ECA) to strengthen the feature extracting ability of each task. The experimental result shows that our method improves 0.1112 compared with the conventional method in SROCC. The method can also help to find best aesthetic path planning for unmanned aerial vehicles (UAV) and vehicles.

Nowadays, almost everyone can shoot photos using smart phones. However, not everyone can take good photos. We propose to use computational aesthetics to automatically teach people without photography training to take excellent photos. We present Aesthetic Dashboard: a system of rich aesthetic evaluation and guidance for mobile photography. We take 2 most used types of photos: landscapes and portraits into consideration. When people take photos in the preview mode, for landscapes, we show the overall aesthetic score and scores of 3 basic attributes: light, composition and color usage. Meanwhile, the matching scores of the 3 basic attributes of current preview to typical templates are shown, which can help users to adjust 3 basic attributes accordingly. For portraits, besides the above basic attributes, the facial appearance, the guidance of face light, body pose and the garment color are also shown to the users. This is the first system that can teach mobile users to shoot good photos in the form of aesthetic dashboard, through which, users can adjust several aesthetic attributes to take good photos easily.