We demonstrate that memory representations undergo semantization during short-term memory, complementing the slow generalization during consolidation, with a notable shift from visual to semantic encoding. Foretinib clinical trial In addition to perceptual and conceptual structures, we explore how affective evaluations contribute to the formation of episodic memories. The combined results of these studies showcase how the examination of neural representations might provide a more profound understanding of the essence of human memory.
Recent investigations explored the impact of geographic separation between mothers and adult daughters on their reproductive life-course decisions. The extent to which a daughter's geographical proximity to her mother affects her fertility, encompassing the number and age of her pregnancies and children, has received limited scholarly attention. This study endeavors to close the existing gap by exploring the relocation motivations of adult daughters and mothers that bring them into closer proximity. Data from the Belgian register are used to analyze a cohort of 16742 firstborn girls, who were 15 years old at the beginning of 1991, and their mothers, who resided apart at least once within the observation period from 1991 to 2015. We analyzed recurrent events using event-history models, examining how an adult daughter's pregnancies and her children's ages and number affected the probability of her living close to her mother. We then differentiated between whether the daughter's or the mother's relocation led to this close living situation. A correlation was observed in the data, whereby daughters were more likely to move closer to their mothers during the initial pregnancy, and mothers showed a greater propensity to move closer to their daughters when their daughters' children were older than 25. The research presented here contributes to the current body of work on the effects of family relationships on the (im)mobility of individuals.
The task of crowd counting is fundamental to crowd analysis, holding significant importance in the realm of public safety. In consequence, its significance has risen steeply in recent times. The prevailing method merges crowd counting with convolutional neural networks to generate the corresponding density map, derived from the application of custom Gaussian kernels to the marked points. While the counting accuracy is boosted by the novel network architectures, a common shortcoming remains: the perspective effect. This leads to a substantial disparity in the size of targets in various locations within a single scene, a discrepancy poorly captured by existing density maps. Acknowledging the impact of target scale on prediction accuracy for crowd density, we propose a scale-sensitive framework for crowd density map estimation. This framework's approach is to tackle scale variation in the stages of density map creation, network architecture development, and model optimization. This entity is built from the Adaptive Density Map (ADM), the Deformable Density Map Decoder (DDMD), and the Auxiliary Branch. For each particular target, the Gaussian kernel's size is adjusted dynamically to generate an ADM containing scale-related information. By employing deformable convolution, DDMD aligns with the Gaussian kernel's variability, consequently improving the model's sensitivity to scale. The Auxiliary Branch manages the training process of learning deformable convolution offsets. Eventually, we execute experiments on diverse large-scale datasets. The results definitively illustrate the impact of the ADM and DDMD. The visualization, in addition, underscores that deformable convolution learns to account for the target's scale alterations.
A fundamental difficulty in computer vision is accurately reconstructing and comprehending 3D scenes using a single camera. The application of recent learning-based approaches, particularly multi-task learning, results in impressive performance enhancements for associated tasks. Nevertheless, certain works exhibit limitations in their capacity to capture loss-spatial-aware information. A novel Joint-Confidence-Guided Network (JCNet) is proposed in this paper to predict depth, semantic labels, surface normals, and a corresponding joint confidence map, each with its dedicated loss function. upper respiratory infection The Joint Confidence Fusion and Refinement (JCFR) module, centrally designed for multi-task feature fusion in a unified, independent space, also extracts and utilizes the geometric-semantic structural information from the joint confidence map. Across spatial and channel dimensions, we employ confidence-guided uncertainty, derived from the joint confidence map, to supervise multi-task predictions. To balance the attention paid to various loss functions or spatial areas during training, the Stochastic Trust Mechanism (STM) dynamically modifies the elements of the joint confidence map probabilistically. For the final step, we create a calibrating operation to improve the performance of the joint confidence branch in tandem with the rest of JCNet, thereby avoiding overfitting. Medical Knowledge The NYU-Depth V2 and Cityscapes datasets show that the proposed methods excel in geometric-semantic prediction and uncertainty estimation, demonstrating state-of-the-art performance.
By integrating information from multiple modalities, multi-modal clustering (MMC) seeks to optimize clustering outcomes. Deep neural networks are used in this article to explore challenging aspects of MMC methodologies. A significant limitation of current methodologies lies in their fragmented objectives, which preclude the simultaneous learning of inter- and intra-modality consistency. This consequently restricts the scope of representation learning. Alternatively, the vast majority of established processes are designed for a restricted dataset, failing to address information outside of their training set. Addressing the two challenges above, we introduce a novel approach, the Graph Embedding Contrastive Multi-modal Clustering network (GECMC), considering representation learning and multi-modal clustering as interconnected processes, not as separate objectives. To summarize, we construct a contrastive loss that capitalizes on pseudo-labels to explore consistent representations across modalities. Hence, the GECMC technique highlights a practical method for amplifying the similarities of intra-cluster elements, whilst minimizing the similarities of elements belonging to different clusters, focusing on both inter- and intra-modal characteristics. Within the co-training framework, clustering and representation learning are mutually reinforcing and evolve in tandem. Following this, we design a clustering layer using cluster centroids as parameters, highlighting GECMC's ability to acquire clustering labels from provided samples and process out-of-sample data. GECMC's results surpass those of 14 rival methods on four challenging datasets. https//github.com/xdweixia/GECMC contains the GECMC's codes and datasets for reference.
The image restoration process of real-world face super-resolution (SR) suffers from significant ill-posedness. Cycle-GAN's cycle-consistent approach, while successful in face super-resolution, frequently generates artifacts in realistic situations. This is because a shared degradation pathway, exacerbating differences between synthetic and real low-resolution images, can hinder final performance. To fully exploit GAN's generative power for real-world facial super-resolution, we implement in this paper two separate degradation branches, one for the forward and one for the backward cycle-consistent reconstruction, both sharing a common restoration branch. The Semi-Cycled Generative Adversarial Network (SCGAN) diminishes the adverse effects of the disparity between real-world low-resolution (LR) facial images and synthetic LR images, ultimately achieving strong and accurate face super-resolution (SR) performance. This is achieved via a shared restoration branch, reinforced by cycle-consistent learning in both forward and backward directions. On two synthetic and two real-world data sets, our SCGAN model achieved superior performance in recovering face structures/details and quantitative metrics in comparison to the existing cutting-edge methods for real-world face SR. At https//github.com/HaoHou-98/SCGAN, the code will be made available to the public.
This paper delves into the intricacies of face video inpainting. Primarily, existing video inpainting methods concentrate on scenes with recurring visual patterns found in nature. No prior facial knowledge is utilized in the process of recovering correspondences for the damaged face. Consequently, their outcomes are less than ideal, especially when dealing with faces exhibiting significant variations in pose and expression, where facial features display substantial differences between successive frames. In this article, we develop a two-stage deep learning algorithm for the task of inpainting facial video. Before transforming a face between image space and UV (texture) space, we leverage 3DMM as our 3D facial model. In Stage I, the UV space serves as the environment for executing face inpainting. This process effectively removes the impact of facial poses and expressions, thus creating a more straightforward learning process focused on correctly aligned facial features. A frame-wise attention module is incorporated to capitalize on correspondences in neighboring frames, thus assisting the inpainting task. In Stage II, we reintegrate the inpainted facial regions into the image plane, and conduct face video refinement to inpaint any background areas not inpainted in Stage I, enhancing the inpainted facial regions. Extensive experimental results demonstrate our approach's substantial superiority to 2D-based methods, particularly when processing faces subjected to considerable pose and expression changes. The project's online repository is available at https://ywq.github.io/FVIP.