Anomaly detection is a binary classification task, which is to determine whether each pixel is abnormal or not. The difficulties are that it is hard to obtain abnormal samples and predict the shape of abnormal regions. Due to these difficulties, traditional supervised segmentation methods fail. The usual weakly supervised segmentation methods will use artificially generate defects to construct training samples. However, the model will be overfitted to artificially generated defects during training, resulting in insufficient generalization ability of the model. In this paper, we presented a novel reconstruction-based weakly supervised method for sparse anomaly detection. We proposed to use generative adversarial networks (GAN) to learn the distribution of positive samples, and reconstructed negative samples which contained the sparse defect into positive ones. Due to the nature of GAN, the training dataset only needs to contain normal samples. Subsequently, the segmentation network performs progressive feature fusion on reconstructed and original samples to complete the anomaly detection. Specifically, we designed the loss function based on kullback-leibler divergence for sparse anomalous defects. The final weakly-supervised segmentation network only assumes a sparsity prior of the defect region; thus, it can circumvent the detailed semantic labels and alleviate the potential overfitting problem. We compared our method with the state of the art generation-based generative anomaly detection methods and observed the average area under the receiver operating characteristic curve increase of 3% on MVTec anomaly detection.

For the autonomous and intelligent operation of robots in unknown environments, simultaneous localization and mapping (SLAM) is essential. Since the proposal of visual odometry, the use of visual odometry in the mapping process has greatly advanced the development of pure visual SLAM techniques. However, the main challenges in current monocular odometry algorithms are the poor generalization of traditional methods and the low interpretability of deep learning-based methods. This paper presented a hybrid self-supervised visual monocular odometry framework that combined geometric principles and multi-frame temporal information. Moreover, a post-odometry optimization module was proposed. By using image synthesis techniques to insert synthetic views between the two frames undergoing pose estimation, more accurate inter-frame pose estimation was achieved. Compared to other public monocular algorithms, the proposed approach showed reduced average errors in various scene sequences, with a translation error of $2.211\mathrm{\%}$ and a rotation error of $0.418{}^{\circ }/100m$. With the help of the proposed optimizer, the precision of the odometry algorithm was further improved, with a relative decrease of approximately 10 $\mathrm{\%}$ intranslation error and 15 $\mathrm{\%}$ in rotation error.

Deep learning, particularly generative models, has inspired controllable image synthesis methods and applications. These approaches aim to generate specific visual content using latent prompts. To explore low-level controllable image synthesis for precise rendering and editing tasks, we present a survey of recent works in this field using deep learning. We begin by discussing data sets and evaluation indicators for low-level controllable image synthesis. Then, we review the state-of-the-art research on geometrically controllable image synthesis, focusing on viewpoint/pose and structure/shape controllability. Additionally, we cover photometrically controllable image synthesis methods for 3D re-lighting studies. While our focus is on algorithms, we also provide a brief overview of related applications, products and resources for practitioners.