Domain Adaptive Object Detection (DAOD) aims to adapt a detector trained on the labeled source domain to perform effectively on the unlabeled target domain. Most existing DAOD methods commonly employ self-training strategy and adversarial learning. However, the methods based on the self-training strategy often generate unreliable pseudo-labels (e.g., missing detections and false positives) due to the absence of target domain semantics, resulting in suboptimal models. Meanwhile, aligning a large number of unimportant pixels hampers adversarial learning’s ability to capture domain-invariant semantic information. Therefore, we propose the Multi-scale Adversarial Teacher Framework (MATF) based on the common self-training framework (teacher–student framework). Specifically, to mitigate the impact of unreliable pseudo-labels, we propose the Multi-scale Negatives Filtering (MNF) module in the teacher model, which prevents missing detections from damaging the model by filtering out unreliable negative samples at multiple feature scales. In addition, we propose the Multi-scale Key-Pixel Discriminator (MKPD), which predicts the distributions of key pixels at multiple feature scales emphasizing the adversarial alignment of key pixels rather than unimportant pixels. Finally, to generate higher-quality pseudo-labels, we introduce the discriminator into the student model to reduce the model’s bias towards the source domain. Unlike most methods, we construct our method on a computationally efficient but less work-intensive one-stage detector. Extensive experiments conducted on DAOD benchmark datasets such as Cityscapes, FoggyCityscapes, KITTI, and Sim10k demonstrate the strong adaptability and effectiveness of MATF.

Reconstructing visual stimuli from brain activities is crucial for deciphering the underlying mechanism of the human visual system. While recent studies have achieved notable results by leveraging deep generative models, challenges persist due to the lack of large-scale datasets and the inherent noise from non-invasive measurement methods. In this study, we draw inspiration from the mechanism of human memory and propose BrainRAM, a novel two-stage dual-guided framework for visual stimuli reconstruction. BrainRAM incorporates a Retrieval-Augmented Module (RAM) and diffusion prior to enhance the quality of reconstructed images from the brain. Specifically, in stage I, we transform fMRI voxels into the latent space of image and text embeddings via diffusion priors, obtaining preliminary estimates of the visual stimuli's semantics and structure. In stage II, based on previous estimates, we retrieve data from the LAION-2B-en dataset and employ the proposed RAM to refine them, yielding high-quality reconstruction results. Extensive experiments demonstrate that our BrainRAM outperforms current state-of-the-art methods both qualitatively and quantitatively, providing a new perspective for visual stimuli reconstruction.

The easy simulation of orthognathic surgery with high accuracy and good reliability, and the visual explanation of the prediction of surgery to patients with marillo-mandibular deformities remains a key focus in oral and maxillofacial surgery. A Computer-Aided Simulation System for Orthognathic Surgery (CASSOS) was developed by means of the technique of digital image processing, with which a detailed analysis could be performed automatically, including 71 measurements of distance, degree and ratio for frontal cephalograms and 68 measurements for lateral ones, with computer-aided diagnosis. A quantitative surgical simulation for the prediction of operations could be satisfactorily carried out for clinical use. All data and images were printed by Laserjet and color videoprinters. All procedures were finished in 20 minutes.>