Our method's end-to-end training capability stems from these algorithms, which allow the backpropagation of grouping errors to directly guide the learning of multi-granularity human representations. This method is markedly different from existing bottom-up human parsers or pose estimators, which invariably involve complex post-processing steps or greedy heuristic algorithms. Extensive investigations of three instance-specific human parsing datasets (MHP-v2, DensePose-COCO, and PASCAL-Person-Part) highlight our method's advantage over prevailing human parsing techniques, offering considerably more efficient inference. Our MG-HumanParsing project's code is publicly available on GitHub, at the following URL: https://github.com/tfzhou/MG-HumanParsing.

Improved single-cell RNA-sequencing (scRNA-seq) technology allows for an examination of the diversity in tissues, organisms, and sophisticated diseases at a cellular resolution. The process of clustering is crucial within the realm of single-cell data analysis. However, the numerous variables in scRNA-seq data, the ever-rising count of cells measured, and the unavoidable presence of technical noise create formidable challenges for clustering calculations. Motivated by the positive results of contrastive learning in various domains, we introduce ScCCL, a novel self-supervised contrastive learning method aimed at clustering scRNA-seq data. ScCCL randomly masks gene expression in each cell twice, and adds a subtle Gaussian noise level. Following this, features are extracted from the enhanced data using the momentum encoder structure. Within the instance-level and cluster-level contrastive learning modules, contrastive learning is, respectively, applied. Following training, a representation model is generated that can effectively extract high-order embeddings for individual cells. Experiments on multiple public datasets were undertaken using ARI and NMI as the two evaluation metrics. Analysis of the results demonstrates ScCCL's enhanced clustering performance relative to the benchmark algorithms. Undeniably, the broad applicability of ScCCL, independent of a specific data type, makes it valuable in clustering analyses of single-cell multi-omics data.

In hyperspectral images (HSIs), the limited target size and spatial resolution frequently result in the appearance of subpixel targets. This, unfortunately, creates a crucial bottleneck in hyperspectral target detection, specifically in the area of subpixel target localization. This article introduces the LSSA detector, uniquely designed for hyperspectral subpixel target detection, by learning single spectral abundances. The proposed LSSA method differs from existing hyperspectral detectors that typically use spectral matching with spatial context or background analysis. It uniquely learns the spectral abundance of the target, making it possible to identify subpixel targets. LSSA processes the prior target spectrum by updating and learning its abundance, keeping the prior target spectrum itself constant within a non-negative matrix factorization model. This particular method is quite effective at identifying and learning the abundance of subpixel targets, thus contributing to successful detection of such targets within hyperspectral imagery (HSI). A substantial number of experiments, utilizing one synthetic dataset and five actual datasets, confirm the LSSA's superior performance in hyperspectral subpixel target detection over alternative techniques.

The application of residual blocks in deep learning networks is substantial. Nevertheless, residual blocks might suffer information loss as a consequence of rectifier linear unit (ReLU) relinquishment of data. The recent proposal of invertible residual networks aims to resolve this issue; however, these networks are typically bound by strict restrictions, thus limiting their potential applicability. Immune reconstitution This brief scrutinizes the conditions under which the invertibility of a residual block is determined. Presented is a sufficient and necessary condition that guarantees the invertibility of residual blocks possessing a single ReLU layer. We present a demonstration that residual blocks, widely used in convolutional architectures, are invertible under certain limitations, depending on the convolution's zero-padding implementation. Inverse algorithms are presented, and experiments are designed to demonstrate the efficacy of the proposed inverse algorithms, validating the accuracy of the theoretical findings.

Unsupervised hashing methods have become increasingly popular due to the explosion of large-scale data, as they enable the learning of compact binary codes, leading to a significant reduction in storage and computational needs. Current unsupervised hashing approaches, seeking to exploit the valuable content embedded within samples, are deficient in considering the local geometric structure of the unlabeled data. Furthermore, hashing methods employing auto-encoders prioritize minimizing reconstruction error between input data and binary codes, overlooking the potential for harmony and interdependence between data originating from multiple sources. For the stated issues, we propose a hashing algorithm constructed using auto-encoders, specifically for multi-view binary clustering. This algorithm learns affinity graphs dynamically, incorporating low-rank constraints, and it implements collaborative learning between the auto-encoders and affinity graphs. The result is a unified binary code, termed graph-collaborated auto-encoder (GCAE) hashing for multi-view binary clustering. A novel multiview affinity graph learning model is proposed, incorporating a low-rank constraint, enabling the extraction of the underlying geometric information from multiview data. learn more Finally, we devise an encoder-decoder structure to unify the processing of the multiple affinity graphs, which leads to the efficient learning of a unified binary representation. For a significant reduction in quantization errors, we apply decorrelation and code balance to binary codes. Employing an alternating iterative optimization method, we arrive at the multiview clustering results. Demonstrating the algorithm's superiority over existing state-of-the-art methods, extensive experimental results are presented using five public datasets.

Despite their impressive performance on supervised and unsupervised learning, deep neural models face challenges in deployment on devices with limited resources due to their substantial size. Knowledge distillation, a fundamental strategy for compressing and accelerating models, efficiently addresses this issue by transferring knowledge accumulated by teacher models to their smaller student counterparts. While many distillation methods concentrate on replicating the responses of teacher networks, they often overlook the inherent information redundancy present in student networks. Difference-based channel contrastive distillation (DCCD), a novel distillation framework, is presented in this article to integrate channel contrastive knowledge and dynamic difference knowledge into student networks, thereby lessening redundancy. Student networks' feature expression space is effectively broadened by a newly constructed contrastive objective at the feature level, preserving richer information in the feature extraction step. At the concluding output level, teacher networks yield more detailed knowledge by calculating the difference in responses from various augmented viewpoints on the same example. In order to facilitate greater sensitivity to nuanced dynamic transformations, we optimize student networks. By refining two critical DCCD elements, the student network acquires a deeper understanding of contrasts and differences, thereby minimizing overfitting and redundancy. Finally, the student's performance on CIFAR-100 tests yielded results that astonished everyone, ultimately exceeding the teacher's accuracy. We've lowered the top-1 error rate for ImageNet classification, achieved using ResNet-18, to 28.16%. Concurrently, our cross-model transfer results with ResNet-18 show a 24.15% decrease in top-1 error. Datasets commonly used in empirical experiments and ablation studies show our proposed method achieving state-of-the-art accuracy, exceeding other distillation methods.

Hyperspectral anomaly detection (HAD) is predominantly approached in existing techniques by considering it as a problem of background modeling and spatial anomaly detection. Employing the frequency domain, this article models the background, viewing anomaly detection through a frequency analysis lens. The amplitude spectrum's spikes are shown to be indicative of the background, and applying a Gaussian low-pass filter to this spectrum acts as an anomaly detector. Reconstruction of the filtered amplitude along with the raw phase spectrum culminates in the initial anomaly detection map. By diminishing the effect of non-anomalous high-frequency detailed information, we show that the phase spectrum is crucial for interpreting the spatial prominence of anomalies. The initial anomaly map is substantially enhanced by incorporating a saliency-aware map obtained through phase-only reconstruction (POR), thus achieving better background suppression. Not only is the standard Fourier Transform (FT) utilized, but also the quaternion Fourier Transform (QFT) to enable concurrent multiscale and multifeature processing, thereby obtaining the frequency domain representation of the hyperspectral images (HSIs). This ensures robust detection performance. Analysis of experimental results on four real High-Speed Imaging Systems (HSIs) highlights the exceptional detection performance and superior time efficiency of our proposed method, demonstrating significant advantages over contemporary anomaly detection approaches.

Community identification seeks to locate tightly knit groups within a network, a fundamental graph technique employed in numerous applications, including the discovery of protein functional units, image segmentation, and social circle recognition, to name just a few. Recently, significant interest has been generated in community detection methods employing nonnegative matrix factorization (NMF). teaching of forensic medicine Although common approaches often ignore the multi-hop connectivity patterns in a network, these are surprisingly practical for community detection.