A spatial-temporal deformable feature aggregation (STDFA) module, the second element, is presented to adaptively capture and aggregate spatial and temporal contexts from dynamic video frames for enhanced super-resolution reconstruction. Empirical findings across various datasets highlight the superior performance of our approach compared to leading STVSR techniques. The source code can be accessed at https://github.com/littlewhitesea/STDAN.
The learning of generalizable feature representations forms a cornerstone of effective few-shot image classification. Recent work, leveraging task-specific feature embeddings from meta-learning for few-shot learning, proved restricted in tackling complex tasks, as the models were easily swayed by irrelevant contextual factors like the background, domain, and style of the images. This paper proposes a novel, disentangled feature representation framework (DFR), designated DFR, to enhance few-shot learning. The discriminative features modeled by the classification branch of DFR can be adaptively decoupled from the class-irrelevant component within the variation branch. In summary, most of the prevalent deep few-shot learning models are adaptable as the classification branch, therefore DFR can bolster their performance on a variety of few-shot learning assignments. In addition, we introduce a novel FS-DomainNet dataset, stemming from DomainNet, to benchmark few-shot domain generalization (DG) capabilities. To assess the proposed DFR across general, fine-grained, and cross-domain few-shot classification, as well as few-shot DG, we undertook thorough experiments employing the four corresponding benchmarks: mini-ImageNet, tiered-ImageNet, Caltech-UCSD Birds 200-2011 (CUB), and the newly developed FS-DomainNet. Exceptional performance across all datasets was exhibited by the DFR-based few-shot classifiers, a direct outcome of the effective feature disentanglement.
Deep convolutional neural networks (CNNs) have achieved remarkable success in pansharpening, as evidenced by recent research. Most deep convolutional neural network-based pansharpening models, employing a black-box architecture, necessitate supervision, leading to their significant dependence on ground-truth data and a subsequent decrease in their interpretability for specific problems encountered during network training. IU2PNet, a novel, interpretable, unsupervised, end-to-end pansharpening network, is presented in this study; this network explicitly incorporates the well-known pansharpening observation model into a structure of unsupervised, iterative, adversarial processing. The first step involves the creation of a pan-sharpening model, whose iterative computations are carried out using the half-quadratic splitting algorithm. The iterative steps are then articulated within the context of a deep, interpretable iterative generative dual adversarial network—iGDANet. The generator in iGDANet is fundamentally shaped by the intricate integration of deep feature pyramid denoising modules and deep interpretable convolutional reconstruction modules. Every cycle of the generator is characterized by an adversarial struggle with both spectral and spatial discriminators, aiming to update both spectral and spatial characteristics without the use of ground-truth images. Extensive experimentation demonstrates that, in comparison to cutting-edge methodologies, our proposed IU2PNet achieves highly competitive performance, as evidenced by quantitative metrics and qualitative visual appraisals.
This article presents a dual event-triggered adaptive fuzzy control scheme, resilient to mixed attacks, for a class of switched nonlinear systems characterized by vanishing control gains. The proposed scheme's approach to dual triggering in sensor-to-controller and controller-to-actuator channels relies on two innovative switching dynamic event-triggering mechanisms (ETMs). It is determined that an adjustable positive lower bound on inter-event times for every ETM is necessary to circumvent Zeno behavior. In the meantime, mixed attacks, including deception attacks on sampled state and controller data, and dual random denial-of-service attacks on sampled switching signal data, are addressed by the design of event-triggered adaptive fuzzy resilient controllers for subsystems. In contrast to prior research confined to single-trigger switched systems, this paper delves into the intricate asynchronous switching dynamics induced by dual triggers, mixed attacks, and the switching of subsystems. Beyond that, the difficulty caused by vanishing control gains at specific instances is resolved by proposing a state-dependent switching strategy triggered by events and incorporating vanishing control gains into a switching dynamic ETM. For verification purposes, a mass-spring-damper system and a switched RLC circuit system were subsequently applied to the derived outcome.
Using a data-driven approach, this article explores the control of linear systems exhibiting external disturbances via trajectory imitation, focusing on inverse reinforcement learning (IRL) with static output feedback (SOF). A learner's pursuit of mimicking an expert's trajectory defines the Expert-Learner model. Utilizing exclusively the measured input and output data of experts and learners, the learner calculates the expert's policy by recreating its unknown value function weights; thus, mimicking the expert's optimally performing trajectory. https://www.selleckchem.com/products/nvl-655.html The paper presents three novel inverse reinforcement learning methods for static OPFB. The foundational algorithm, based on a model, lays the groundwork. Leveraging input-state data, the second algorithm is a data-driven process. A data-driven method, the third algorithm is completely reliant on input-output data. A thorough analysis has been conducted on the stability, convergence, optimality, and robustness. To confirm the efficacy of the suggested algorithms, simulation experiments are performed.
Thanks to the development of extensive data collection methods, data sets are frequently characterized by multiple modalities or sourced from numerous origins. In the prevailing multiview learning paradigm, the assumption is usually made that each dataset specimen appears in every view. Nonetheless, this presumption is excessively restrictive in certain practical applications, including multi-sensor surveillance systems, where each sensor's view is incomplete due to missing data. We delve into the classification of incomplete multiview data within a semi-supervised context, proposing a technique termed absent multiview semi-supervised classification (AMSC). Independent construction of partial graph matrices, employing anchor strategies, quantifies relationships among each present sample pair on each view. AMSC's method for unambiguous classification of all unlabeled data involves the simultaneous learning of view-specific and common label matrices. By means of partial graph matrices, AMSC gauges the similarity between pairs of view-specific label vectors for each view. It additionally assesses the similarity between view-specific label vectors and class indicator vectors, leveraging the common label matrix. To assess the impacts of various perspectives, the pth root integration approach is employed to combine the losses from different viewpoints. We explore the correlation between the p-th root integration strategy and the exponential decay integration method, resulting in an algorithm with demonstrated convergence for the non-convex problem. Real-world datasets and the document classification task are utilized to assess AMSC's efficacy by benchmarking it against existing methodologies. The experimental results yield a compelling demonstration of our proposed approach's strengths.
3D volumetric data is now a staple in modern medical imaging, leading to a challenge for radiologists in comprehensively examining every part of the dataset. Volumetric data, particularly in digital breast tomosynthesis, is often accompanied by a synthesized two-dimensional representation (2D-S) generated from the corresponding three-dimensional data. Our analysis focuses on how this image pairing affects the process of locating and discerning both large and small spatial signals. Observers examined 3D volumes, 2D-S images, and a fusion of both in their search for these signals. We propose that a lower spatial acuity in the observers' visual periphery leads to an impediment in detecting small signals present in the 3D images. However, the 2D-S system effectively guides eye movement to suspicious points, consequently bolstering the observer's ability to identify signals within the complex three-dimensional configuration. Results from behavioral experiments highlight a performance improvement in localizing and detecting smaller (but not larger) signals when 2D-S data is incorporated alongside volumetric data, in contrast to the performance using 3D data alone. There is a concurrent reduction in the incidence of search errors. To gain a computational understanding of this process, we employ a Foveated Search Model (FSM) which simulates human eye movements and then analyzes image points with varying degrees of spatial detail, dependent on their distance from fixation points. The FSM's assessment of human performance for various signals integrates the reduction in search errors that arises from the interplay between the 3D search and the supplementary 2D-S. Emphysematous hepatitis Experimental and modeling results confirm the benefits of using 2D-S within 3D search, diminishing the negative consequences of low-resolution peripheral processing by directing attention to crucial zones, thereby reducing the incidence of errors.
This research paper investigates the synthesis of novel viewpoints of a human performer, based on a restricted set of camera observations. Studies on learning implicit neural representations of 3D environments have highlighted the potential for achieving excellent view synthesis results with plentiful input views. Nevertheless, the representation learning process will be improperly defined if the perspectives are exceedingly sparse. infectious ventriculitis Our key approach to resolving this ill-defined problem centers on the integration of observations across successive video frames.