Following the initial steps, a part/attribute transfer network is developed to establish representative features for attributes yet to be encountered, with additional prior knowledge providing crucial support. Finally, a network specifically designed for prototype completion is created to utilize these prior knowledge components. Selleck SH-4-54 In addition, a Gaussian-based prototype fusion strategy, designed to circumvent prototype completion errors, integrates mean-based and completed prototypes, leveraging unlabeled data. Ultimately, we also created a finalized economic prototype for FSL, eliminating the requirement for gathering fundamental knowledge, allowing for a fair comparison against existing FSL methods lacking external knowledge. Extensive empirical analysis validates that our technique produces more accurate prototypes and demonstrates superior performance in both inductive and transductive few-shot learning. Our Prototype Completion for FSL code, which is open-sourced, is hosted at this GitHub repository: https://github.com/zhangbq-research/Prototype Completion for FSL.

We present Generalized Parametric Contrastive Learning (GPaCo/PaCo) in this paper, a method effective for handling both imbalanced and balanced datasets. From a theoretical standpoint, supervised contrastive loss demonstrates a tendency to favor high-frequency classes, thus heightening the difficulty in imbalanced learning applications. We introduce a set of parametric, class-wise, learnable centers for rebalancing from an optimization standpoint. In addition, we analyze GPaCo/PaCo loss under a balanced condition. GPaCo/PaCo's ability to adapt the intensity of pushing similar samples closer together, as more samples consolidate around their corresponding centroids, is demonstrated by our analysis to support hard example learning. Long-tailed benchmarks, when subjected to experimentation, reveal the state-of-the-art methodology for long-tailed recognition. The ImageNet benchmark indicates that models utilizing the GPaCo loss function, encompassing CNNs and vision transformers, outperform MAE models in both generalization and robustness. Moreover, the GPaCo model demonstrates its effectiveness in semantic segmentation, showing improvements across the four most prevalent benchmark datasets. Our Parametric Contrastive Learning source code is hosted on GitHub at https://github.com/dvlab-research/Parametric-Contrastive-Learning.

Computational color constancy plays a significant role in Image Signal Processors (ISP) for accurate white balancing across a wide variety of imaging devices. Deep convolutional neural networks (CNNs), recently, have been adopted for color constancy applications. Performance enhancements are notable when contrasting their results with those of shallow learning methods or statistical benchmarks. Nonetheless, the substantial requirement for numerous training examples, the significant computational burden, and the immense model size render CNN-based methodologies unsuitable for deployment on resource-constrained ISPs in real-time applications. To overcome these bottlenecks and reach the performance level of CNN-based methods, a method for selecting the ideal simple statistics-based approach (SM) is developed for each image. In order to achieve this, we propose a novel ranking-based color constancy method (RCC), which views the selection of the optimal SM method as a label ranking problem. RCC crafts a unique ranking loss function, incorporates a low-rank constraint to curtail model complexity, and implements a grouped sparse constraint for feature selection. The RCC model is used lastly to predict the sequence of candidate SM strategies for an examination image, and estimate its illumination using the predicted optimal SM procedure (or by merging results evaluated from the prime k SM methods). Experimental results unequivocally demonstrate that the proposed RCC method surpasses nearly all shallow learning techniques, reaching performance on par with, and in some cases exceeding, deep CNN-based approaches, while employing only 1/2000th the model size and training time. RCC's excellent generalization across various cameras is complemented by its strong robustness with constrained training data. Lastly, to liberate the model from reliance on ground truth illumination, we extend RCC to create a novel, ranking-based approach, RCC NO, that trains a ranking model by leveraging simple, partial binary preference data provided by non-expert annotators instead of utilizing expert input. RCC NO achieves superior results compared to SM methods and the majority of shallow learning-based methods, all while maintaining remarkably low costs for sample collection and illumination measurement.

Two fundamental research areas within event-based vision are video-to-events simulation and events-to-video reconstruction. The deep neural networks presently employed for E2V reconstruction are commonly complex and difficult to interpret. In addition, event simulators currently available are intended to produce authentic events; however, study focusing on enhancing event generation methodologies has, up to this point, been restricted. We propose a lightweight and straightforward model-based deep network in this paper for E2V reconstruction, analyze the diversity of adjacent pixel values within V2E generation, and ultimately build a V2E2V pipeline to evaluate the influence of varying event generation approaches on video reconstruction. E2V reconstruction leverages sparse representation models to model the connection between event occurrences and corresponding intensity values. The algorithm unfolding strategy is subsequently used to create a convolutional ISTA network (CISTA). neonatal pulmonary medicine For heightened temporal coherence, long short-term temporal consistency (LSTC) constraints are additionally introduced. The V2E generation introduces the concept of interleaved pixels exhibiting varying contrast thresholds and low-pass bandwidths, hypothesizing that this enhancement facilitates the extraction of more valuable information from intensity data. severe deep fascial space infections Finally, the V2E2V architectural paradigm is applied to confirm the effectiveness of this strategy. The findings from our CISTA-LSTC network surpass existing state-of-the-art techniques, achieving a more consistent temporal representation. Variations within generated events uncover subtle details, ultimately producing a significantly improved reconstruction.

Multitasking optimization using evolutionary methods is a developing area of investigation within the field of research. An essential consideration when approaching multitask optimization problems (MTOPs) is the efficient transference of pertinent knowledge across diverse tasks. Nevertheless, the exchange of knowledge within current algorithms faces two constraints. Only when dimensions in different tasks align can knowledge be transferred, bypassing any similarities or connections between other dimensions. A significant gap exists in the transfer of knowledge across related dimensions within a single task. This paper introduces an interesting and efficient approach to resolve these two limitations, organizing individuals into multiple blocks for knowledge transfer at the block level, thus creating the block-level knowledge transfer (BLKT) framework. BLKT segments individuals across all tasks, forming a block-based population; each block encompasses a series of successive dimensions. Similar blocks, originating from identical or diverse tasks, are conglomerated within the same cluster for evolutionary purposes. BLKT, in this manner, mediates the exchange of knowledge across similar dimensional spaces, irrespective of their inherent alignment or divergence, and irrespective of whether they relate to identical or diverse tasks, resulting in enhanced rational understanding. Comprehensive trials on the CEC17 and CEC22 MTOP benchmarks, a novel and more demanding composite MTOP test suite, and real-world MTOP instances demonstrate that the BLKT-based differential evolution (BLKT-DE) algorithm outperforms all existing state-of-the-art algorithms. In addition, another significant finding is that the BLKT-DE methodology shows promise in addressing single-task global optimization problems, performing competitively with certain cutting-edge algorithms.

A wireless networked cyber-physical system (CPS), comprised of distributed sensors, controllers, and actuators, is the focus of this article, which investigates the model-free remote control challenge. The controlled system's status is observed by sensors to formulate control commands, which are then conveyed to the remote controller for execution by actuators, thereby maintaining the system's stability. The deep deterministic policy gradient (DDPG) algorithm is strategically utilized within the controller to realize control in a model-free system, thereby enabling model-independent control mechanisms. In contrast to the conventional DDPG algorithm, which solely utilizes the current system state, this paper integrates historical action data as input, thereby enhancing information extraction and facilitating precise control in scenarios involving communication delays. Furthermore, the DDPG algorithm's experience replay mechanism integrates reward information using the prioritized experience replay (PER) strategy. Based on the simulation outcomes, the suggested sampling policy boosts convergence speed by leveraging the joint effect of temporal difference (TD) error and reward to determine transition probabilities.

Data journalism's growing prevalence in online news is directly related to the corresponding rise in the visualization of article thumbnail images. However, a small amount of research has been done on the design rationale of visualization thumbnails, particularly regarding the processes of resizing, cropping, simplifying, and enhancing charts shown within the article. Consequently, within this paper, we seek to analyze these design choices and delineate the characteristics that make a visualization thumbnail appealing and comprehensible. To accomplish this goal, our preliminary action encompassed a review of online-compiled visualization thumbnails. Following this, we conducted discussions about visualization thumbnail practices with data journalists and news graphics designers.