Given a single labeled examples, in-context segmentation aims to segment corresponding objects. This setting, known as one-shot segmentation in few-shot learning, explores the segmentation model's generalization ability and has been applied to various vision tasks, including scene understanding and image/video editing. While recent Segment Anything Models (SAMs) have achieved state-of-the-art results in interactive segmentation, these approaches are not directly applicable to in-context segmentation. In this work, we propose the Dual Consistency SAM (DC-SAM) method based on prompt-tuning to adapt SAM and SAM2 for in-context segmentation of both images and videos. Our key insights are to enhance the features of the SAM's prompt encoder in segmentation by providing high-quality visual prompts. When generating a mask prior from support images, we fuse the SAM features to better align the prompt encoder rather than relying solely on a pre-trained backbone. Then, we design a cycle-consistent cross-attention on fused features and initial visual prompts. This design leverages coarse masks from the SAM mask decoder to ensure consistency between features and visual prompts. Next, a dual-branch design is provided by using the discriminative positive and negative prompts in the prompt encoder. Furthermore, we design a simple mask-tube training strategy to adopt our proposed dual consistency method into the mask tube. Although the proposed DC-SAM is primarily designed for images, it can be seamlessly extended to the video domain with the support of SAM2. Given the absence of in-context segmentation in the video domain, we manually curate and construct the first benchmark from existing video segmentation datasets, namedIn-Context Video Object Segmentation (IC-VOS), to better assess the in-context capability of the model. Extensive experiments demonstrate that our method achieves 55.5 (+1.4) mIoU on COCO-20$^{i}$, 73.0 (+1.1) mIoU on PASCAL-5$^{i}$, and a$\mathcal {J\&F}$score of 71.52 on the proposed IC-VOS benchmark.

Transfer learning (TL) is a powerful approach that enhances the generalizability of cross-domain fault diagnosis. However, the challenge of acquiring high-quality mechanical fault signals limits its application. This article introduces the extreme class imbalance problem in the cross-domain diagnosis, restricting the label space of the target domain while relaxing the restrictions of unsupervised learning. The study proposes a novel generative TL method called fast sparse neural style, which employs sparse representation to capture the domain-invariant fault features as well as the Gram matrix to measure the domain-specific features. Fault features and domain features are proven to be separable in mechanical signals and are fused in the data generation process. Compared to other methods through various cross-domain diagnostic tasks on a piston aero-engine, the proposed method has obvious advantages in tasks with substantial inter-domain differences, demonstrating the potential and research value of generative TL.

Transforming Baimiao sketches into full-color Chinese brush paintings is significant in the context of rising market demands and exploring new possibilities in the realm of AI art creation. While several pixel-based works demonstrated the capability of generative adversarial network (GAN) for colorization, several challenges remain, e.g., the inappropriate thickness of lines in sketches and the shortage of local detail. To address the challenges, this paper introduces the double-scale discriminator GAN (DSD-GAN), with double discriminators that target realistic image generation at two scales. The generator of DSD-GAN integrates a Unet-based architecture with a convolutional attention module capable of focusing on critical areas to enhance the painting color and detail accuracy. Moreover, an additional enhancement block, inspired by the perceptual field model, is adopted to improve the precision of local details in the generated artwork. Extensive verification on a diverse dataset of Chinese brush paintings, including two artistic subject matter still life and landscape, demonstrates DSD-GAN’s superiority over state-of-the-art Image-to-Image translation methods across various metrics, including PSNR, SSIM, MSE, and PI. Qualitative visual assessments further confirm its effectiveness. The source code is accessible on GitHub upon paper acceptance.

As artificial intelligence (AI) and machine learning continue to advance, they have become key players in signal analysis and pattern recognition. However, challenges such as domain shift and a lack of labeled data hinder the application of machine learning to new, unfamiliar signals, particularly in the development of intelligent diagnostic methods for mechanical faults. In this study, we address these challenges by framing them as the extreme class imbalance problem. We introduce a novel generative transfer learning framework that leverages cycle-consistent adversarial networks with hard constraints (CycleGAN-HCs). This framework is designed to generate unpaired mechanical fault signals, which are then used to train classifiers that can operate across different domains. To enhance feature extraction capabilities for time series data, we developed a new generator model called U-Attention. Additionally, we refined the training loss function to better capture domain-specific and classification-specific features in mechanical vibration signals. The proposed method successfully facilitates feature transfer and data augmentation. Its effectiveness and reliability have been demonstrated through four cross-domain fault diagnosis tasks involving piston aero-engines. Comparative verification shows that the generative transfer learning diagnostic framework outperforms traditional methods, offering superior performance and better generalization in complex mechanical fault diagnosis scenarios.