With the popularity of monocular videos generated by video sharing and live broadcasting applications, reconstructing and editing dynamic scenes in stationary monocular cameras has become a special but anticipated technology. In contrast to scene reconstructions that exploit multi-view observations, the problem of modeling a dynamic scene from a single view is significantly more under-constrained and ill-posed. Inspired by recent progress in neural rendering, we present a novel framework to tackle 4D decomposition problem for dynamic scenes in monocular cameras. Our framework utilizes decomposed static and dynamic feature planes to represent 4D scenes and emphasizes the learning of dynamic regions through dense ray casting. Inadequate 3D clues from a single-view and occlusion are also particular challenges in scene reconstruction. To overcome these difficulties, we propose deep supervised optimization and ray casting strategies. With experiments on various videos, our method generates higher-fidelity results than existing methods for single-view dynamic scene representation.

Semi-supervised learning is becoming increasingly popular in medical image segmentation because of its ability to exploit large amounts of unlabelled data to extract additional information. However, most existing semi-supervised segmentation methods focus only on extracting information from unlabelled data, ignoring the potential of labelled data to further improve model performance. In this paper, we propose a new framework for Intra-Sample Cross Reconstruction Networks (ICR-Net) that utilises labelled data to help the network extract information from unlabelled data, thereby guiding the network’s regularisation learning. Our method contains two modules: Intra-Sample Cross Reconstruction (ICR) module and Synergistic Consistency Constraints (SCC) module. The ICR module processes the labelled data features in a more fine-grained manner, thus enabling the network to learn and capture the key patterns and features in the inputs more efficiently, and the SCC guides the network’s regularised learning by formulating additional model regularisations. Experiments on the LA dataset and the pancreas dataset show that our proposed framework is more effective than current state-of-the-art methods in medical image segmentation tasks.

In semi-supervised medical image segmentation, the scarcity of labeled data makes models prone to learning bias, causing persistent errors in certain regions and eventual over-fitting, significantly impacting segmentation performance. These problematic regions, termed difficult areas, are inadequately addressed by existing methods. To address this, We propose the Difficulty Perception-Processing Heterogeneous Network (DPP-Net). It guides the model in accurately perceiving and rectifying difficult areas, overcoming learning bias. Specifically, we introduce the Global Mutual Perception (GMP) to establish a comprehensive information perception channel between sample data, enabling a more holistic and accurate perception of difficult areas. The Difficulty-Aware Rectification (DAR) structure ensures continuous monitoring of difficult areas during training, allowing for timely adjustments to errors. Additionally, the Adaptive Competitive Pseudo-Label (ACP) Augmentation strategy enhances pseudo-labels through adaptive confidence competition. Experimental results on two different medical image databases (CT and MRI) demonstrate that our approach outperforms several state-of-the-art methods.