Conversely, the entire images reveal the absent semantic information for the obstructed representations of the same identity. Hence, the holistic image serves as a potential remedy for the impediment described above, by compensating for the occluded segment. Human cathelicidin in vitro This paper details a novel Reasoning and Tuning Graph Attention Network (RTGAT) that learns comprehensive person representations from images containing occlusions. The network leverages joint reasoning on body part visibility and occlusion compensation for the semantic loss. Integrated Immunology Specifically, we independently analyze the semantic linkage between the attributes of each part and the global attribute in order to reason about the visibility scores of bodily constituents. We subsequently introduce visibility scores calculated via graph attention, guiding the Graph Convolutional Network (GCN) to diffusely suppress noise from occluded part features and disseminate missing semantic information from the complete image to the obscured portion. Finally, complete person representations of occluded images are available for effectively matching features. The experimental outcomes on occluded benchmarks definitively demonstrate the superiority of our technique.
Zero-shot video classification, in its generalized form, seeks to train a classifier capable of categorizing videos encompassing both previously encountered and novel categories. Since training data lacks visual representations for unseen videos, prevalent techniques utilize generative adversarial networks to generate visual features for novel classes based on their categorical embeddings. However, category labels usually convey only the video content without considering other relevant contextual information. Videos, as rich carriers of information, encompass actions, performers, and environments, and their semantic descriptions articulate events across various levels of action. In order to maximize the use of video data, a fine-grained feature generation model is proposed, utilizing the video category names and their corresponding detailed descriptions for generalized zero-shot video classification. To acquire complete information, we initially derive content data from general semantic categories and movement information from specific semantic descriptions as the basis for synthesizing features. Subsequently, we decompose motion into a hierarchical framework of constraints, focusing on the intricate relationship between events and actions at the feature level, based on fine-grained correlations. We also introduce a loss that specifically addresses the uneven distribution of positive and negative samples, thereby constraining the consistency of features across each level. Our proposed framework is validated by extensive quantitative and qualitative assessments performed on the UCF101 and HMDB51 datasets, showcasing positive results in the context of generalized zero-shot video classification.
Multimedia applications heavily rely on the faithful measurement of perceptual quality. Employing reference images in their entirety, full-reference image quality assessment (FR-IQA) methods usually result in better predictive performance. In a different approach, no-reference image quality assessment (NR-IQA), also known as blind image quality assessment (BIQA), which doesn't consider the benchmark image, is a demanding but critical aspect of image quality evaluation. Methods for assessing NR-IQA in the past have disproportionately concentrated on spatial attributes, failing to adequately utilize the valuable data from different frequency bands. Employing spatial optimal-scale filtering analysis, this paper introduces a multiscale deep blind image quality assessment (BIQA) method, designated as M.D. The human visual system's multi-channel nature and contrast sensitivity function served as the impetus for decomposing an image into multiple spatial frequency ranges via multi-scale filtering. We then employed convolutional neural networks to derive an image's subjective quality score from the extracted features. BIQA, M.D.'s experimental performance aligns closely with that of existing NR-IQA methodologies and showcases successful generalization across diverse datasets.
A novel sparsity-minimization scheme forms the foundation of the semi-sparsity smoothing method we propose in this paper. Understanding the pervasive application of semi-sparsity prior knowledge, particularly in situations lacking complete sparsity, like polynomial-smoothing surfaces, is fundamental to the model's derivation. The identification of such priors is demonstrated through a generalized L0-norm minimization formulation within higher-order gradient domains, leading to a novel filter that effectively fits sparse singularities (corners and salient edges) and smooth polynomial surfaces concurrently. The proposed model lacks a direct solver due to the non-convexity and combinatorial structure associated with L0-norm minimization. We propose a resolution to this issue, roughly, using an effective half-quadratic splitting procedure. Through a range of signal/image processing and computer vision applications, we illustrate this technology's versatility and substantial benefits.
Biological investigations frequently leverage cellular microscopy imaging for data acquisition. Inferences regarding cellular health and growth status can be made by observing gray-level morphological characteristics. The presence of a variety of cell types within a single cellular colony creates a substantial impediment to accurate colony-level categorization. Subsequently developing cell types, within a hierarchical framework, can frequently share similar visual characteristics, even while biologically diverse. This paper's empirical results indicate that traditional deep Convolutional Neural Networks (CNNs) and classic object recognition techniques prove inadequate in distinguishing these slight visual differences, thus causing misclassifications. The hierarchical classification system, integrated with Triplet-net CNN learning, is applied to refine the model's ability to differentiate the distinct, fine-grained characteristics of the two frequently confused morphological image-patch classes, Dense and Spread colonies. The Triplet-net method outperforms a four-class deep neural network in classification accuracy by 3%, a difference deemed statistically significant, and also outperforms existing cutting-edge image patch classification methods and standard template matching. Accurate classification of multi-class cell colonies with contiguous boundaries is now achievable through these findings, which significantly enhances the reliability and efficiency of automated, high-throughput experimental quantification using non-invasive microscopy.
To grasp directed interactions in intricate systems, inferring causal or effective connectivity from measured time series is paramount. The inherent complexities of the brain's underlying dynamics make this task particularly demanding. Frequency-domain convergent cross-mapping (FDCCM), a novel causality measure introduced in this paper, uses nonlinear state-space reconstruction to utilize frequency-domain dynamics.
By utilizing synthesized chaotic time series, we explore the general suitability of FDCCM across a range of causal strengths and noise levels. Two Parkinson's datasets, one with 31 and the other with 54 subjects in a resting state, also underwent our methodological analysis. In pursuit of this objective, we formulate causal networks, extract their relevant features, and perform machine learning analyses to differentiate Parkinson's disease (PD) patients from age- and gender-matched healthy controls (HC). Network nodes' betweenness centrality is calculated using FDCCM networks, and these values are employed as features in the classification models.
FDCCM, as evidenced by analysis on simulated data, exhibits resilience to additive Gaussian noise, thereby proving suitable for real-world applications. Decoding scalp electroencephalography (EEG) signals using our proposed methodology, we distinguished Parkinson's Disease (PD) and healthy control (HC) groups, with approximately 97% accuracy confirmed through leave-one-subject-out cross-validation. Comparing decoders across six cortical regions, we found that features extracted from the left temporal lobe achieved a remarkably high classification accuracy of 845%, exceeding those from other regions. Beyond that, an 84% accuracy was attained by a classifier trained on FDCCM networks from one dataset, when it was evaluated on a distinct, independent dataset. The accuracy achieved is far exceeding that of correlational networks (452%) and CCM networks (5484%).
By utilizing our spectral-based causality measure, these findings demonstrate enhanced classification performance and the discovery of valuable Parkinson's disease network biomarkers.
Using our spectral-based causality measure, these findings suggest improved classification accuracy and the identification of useful network biomarkers, specifically for Parkinson's disease.
For a machine to demonstrate collaborative intelligence, it must anticipate and comprehend the human actions undertaken when working with the machine within a shared control framework. A method for online learning of human behavior in continuous-time linear human-in-the-loop shared control systems, contingent solely on system state data, is described in this study. Temple medicine The dynamic interplay of control between a human operator and an automation actively offsetting human actions is represented by a two-player linear quadratic nonzero-sum game. This game model's cost function, which is intended to capture human behavior, is based on a weighting matrix whose values are yet to be determined. Human behavior and the weighting matrix are to be discerned from the system state data alone, in our approach. Therefore, an innovative adaptive inverse differential game (IDG) method, integrating concurrent learning (CL) and linear matrix inequality (LMI) optimization, is developed. Creating a CL-based adaptive law and an interactive controller for automation to estimate the human feedback gain matrix online is the first step, followed by resolving an LMI optimization issue for determining the weighting matrix of the human cost function.