Existing diffusion-based methods have achieved impressive results in human motion editing. However, these methods often exhibit significant ghosting and body distortion in unseen in-the-wild cases. In this paper, we introduce Edit-Your-Motion, a video motion editing method that tackles these challenges through one-shot fine-tuning on unseen cases. Specifically, firstly, we utilized DDIM inversion to initialize the noise, preserving the appearance of the source video and designed a lightweight motion attention adapter module to enhance motion fidelity. DDIM inversion aims to obtain the implicit representations by estimating the prediction noise from the source video, which serves as a starting point for the sampling process, ensuring the appearance consistency between the source and edited videos. The Motion Attention Module (MA) enhances the model's motion editing ability by resolving the conflict between the skeleton features and the appearance features. Secondly, to effectively decouple motion and appearance of source video, we design a spatio-temporal two-stage learning strategy (STL). In the first stage, we focus on learning temporal features of human motion and propose recurrent causal attention (RCA) to ensure consistency between video frames. In the second stage, we shift focus on learning the appearance features of the source video. With Edit-Your-Motion, users can edit the motion of humans in the source video, creating more engaging and diverse content. Extensive qualitative and quantitative experiments, along with user preference studies, show that Edit-Your-Motion outperforms other methods.

Semi-supervised change detection (SSCD) has experienced rapid development, with numerous semi-supervised methods being proposed to reduce the reliance on labeled data in change detection. Existing approaches typically rely on manually set high-confidence thresholds to select robust pseudo-labels. However, the single-pixel threshold filtering method for pseudo-labels (STFP) lacks context correlation, cannot eliminate high-confidence false positive samples, and leads to erroneously filtering out low-confidence true positive samples. To address this issue, we propose robust instance-based semi-supervised learning change detection (RISL) for remote sensing images. RISL evaluates the reliability of each instance object by linking the semantic information of the context, thereby generating robust pseudo-labels. In RISL, firstly, a simple boundary trimming module (BT) as a preprocessing method for change prediction map is introduced. BT can effectively remove low-confidence false positive samples while avoiding confusion in the category of instance objects, thereby improving the quality of instance objects. Then, we propose a reliable instance evaluation module (RIEM) to evaluate the reliability of each instance object. RIEM combines the semantic information of the entire instance and establishes correlations between sample contexts to determine the reliability of the instance, effectively eliminating high false positive samples. In addition, the consistency regularization (CR) is integrated into RISL, and a new strategy suitable for RIEM is constructed. This strategy enhances the model’s generalization ability by mining and hiding semantic information from different views of unlabeled data. Experimental results on the challenging WHU-CD, LEVIR-CD, and CDD-CD datasets show that the proposed method achieves 89.80%, 90.01%, and 87.56% F1 scores on labeled data with 5% distribution. RISL achieves state-of-the-art performance compared to other methods.

Data-free knowledge distillation further broadens the applications of the distillation model. Nevertheless, the problem of providing diverse data with rich expression patterns needs to be further explored. In this paper, a novel dynastic data-free knowledge distillation ($D^{3}K$) model is proposed to alleviate this problem. In this model, a dynastic supernet generator (D-SG) with a flexible network structure is proposed to generate diverse data. The D-SG can adaptively alter architectural configurations and activate different subnet generators in different sequential iteration spaces. The variable network structure increases the complexity and capacity of the generator, and strengthens its ability to generate diversified data. In addition, a novel additive constraint based on the differentiable dhash (D-Dhash) is designed to guide the structure parameter selection of the D-SG. This constraint forces the D-SG to constantly jump out of the fixed generation mode and generate diverse data in semantics and instance. The effectiveness of the proposed model is verified on the experimental benchmark datasets (MNIST, CIFAR-10, CIFAR-100, and SVHN).

The flexible Job-shop Scheduling Problem (FJSP) is an extension of the classical job-shop scheduling problem (JSP). In this paper, a memetic algorithm (MA) for the FJSP is presented. This MA is a hybrid genetic algorithm which explores the search space and two efficient local searchers to exploit information in the search region. An extensive computational study on 49 benchmark problems shows that the algorithm is effective and robust, with respect to other well-known effective algorithms.