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The seamless integration of music with dance movements is essential for communicating the artistic intent of a dance piece. This alignment also significantly improves the immersive quality of gaming experiences and animation productions. Although there ...

In this study, we revisit the fundamental setting of face-swapping models and reveal that only using implicit supervision for training leads to the difficulty of advanced methods to preserve the source identity. We propose a novel reverse pseudo-input ...

In this paper, we reveal the two sides of data augmentation: enhancements in closed-set recognition correlate with a significant decrease in open-set recognition. Through empirical investigation, we find that multi-sample-based augmentations would ...

The new e-commerce industry in the field of e-commerce incorporating promotional factors has an important role to play. By accurately forecasting the demand for e-commerce products, it is possible to satisfy consumer needs, improve sales and marketing ...

With the development of diffusion models, text-guided image style transfer has demonstrated high-quality controllable synthesis results. However, the utilization of text for diverse music style transfer poses significant challenges, primarily due to the ...

Multi-modality pre-trained models (PTMs) have considerably boosted the performance on a broad range of computer vision topics. Still, they have not been explored purposefully in open set recognition (OSR) scenarios when applying PTMs to downstream ...

Personalizing generative models offers a way to guide image generation with user-provided references. Current personalization methods can invert an object or concept into the textual conditioning space and compose new natural sentences for text-to-image ...

This work presents Unified Contrastive Arbitrary Style Transfer (UCAST), a novel style representation learning and transfer framework, that can fit in most existing arbitrary image style transfer models, such as CNN-based, ViT-based, and flow-based ...

• We point out that the performances of self-labeling-based semi-supervised object detection (SSOD) approaches are always limited, and the reason behind such ...

Semi-supervised object detection has recently achieved substantial progress. As a mainstream solution, the self-labeling-based methods train the detector on both labeled data and unlabeled data with pseudo labels predicted by the ...

Point-based indoor 3D object detection has received increasing attention with the large demand for augmented reality, autonomous driving, and robot technology in the industry. However, the detection precision suffers from inputs with semantic ambiguity, ...

By exploring the localizable representations in deep CNN, weakly supervised object localization (WSOL) methods could determine the position of the object in each image just trained by the classification task. However, the partial activation problem caused ...

Limited by objectively poor lighting conditions and hardware devices, low-light images with low visual quality and low visibility are inevitable in the real world. Accurate local details and reasonable global information play their essential and distinct ...

Content-aware image resizing could automatically retarget an image to different aspect ratios while preserving visually salient contents. However, it is difficult for users to interact with the retargeting process and control the results. In this paper,...

The biased cues in the training data may build strong connections between specific targets and unexpected concepts, leading the learned representations could not be applied to real-world data that does not contain the same biased cues. To learn ...

Digital art synthesis is receiving increasing attention in the multimedia community because of engaging the public with art effectively. Current digital art synthesis methods usually use single-modality inputs as guidance, thereby limiting the ...

In this work, we tackle the challenging problem of arbitrary image style transfer using a novel style feature representation learning method. A suitable style representation, as a key component in image stylization tasks, is essential to achieve ...

Vision Transformer has shown impressive performance on the image classification tasks. Observing that most existing visual style transfer (VST) algorithms are based on the texture-biased convolution neural network (CNN), here raises the question ...

Multimodal and multi-domain stylization are two important problems in the field of image style transfer. Currently, there are few methods that can perform multimodal and multi-domain stylization simultaneously. In this study, we propose a unified ...

Style text with decorative elements has a strong visual sense, and enriches our daily work, study and life. However, it introduces new challenges to text detection and recognition. In this study, we propose a text destylized framework, that can ...

Art painting evaluation is sophisticated for a novice with no or limited knowledge on art criticism, and history. In this study, we propose the concept of <italic>representativity</italic> to evaluate paintings instead of using professional concepts, such ...