To enhance learning, a part/attribute transfer network is designed to infer the representative characteristics of unseen attributes, employing supplementary prior information as a guiding principle. Ultimately, a prototype completion network is created, incorporating these pre-existing understandings for the purpose of prototype completion. Classical chinese medicine To counteract prototype completion errors, a Gaussian-based prototype fusion strategy has been developed, which merges mean-based and completed prototypes using insights gleaned from unlabeled datasets. We have, at last, produced a finished economic prototype of FSL, which doesn't require collecting preliminary knowledge, facilitating a fair comparison with existing FSL methods, free from 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 project, with its open-source code, is available on GitHub at https://github.com/zhangbq-research/Prototype Completion for FSL.

This paper introduces Generalized Parametric Contrastive Learning (GPaCo/PaCo), demonstrating its efficacy across both imbalanced and balanced datasets. The theoretical examination reveals that supervised contrastive loss exhibits a bias towards high-frequency classes, thereby increasing the challenge of achieving effective imbalanced learning. Parametric, class-wise, learnable centers are introduced to rebalance from an optimization perspective. In addition, we analyze GPaCo/PaCo loss under a balanced condition. The analysis demonstrates GPaCo/PaCo's ability to dynamically heighten the pushing force of like samples as they draw closer to their centroid with sample accumulation, aiding in hard example learning. Long-tailed recognition's state-of-the-art is manifest in experiments using long-tailed benchmarks. GPaCo loss-trained models, spanning CNNs to vision transformers, display improved generalization and robustness on the complete ImageNet dataset, when evaluated against MAE models. GPaCo's capacity to handle semantic segmentation tasks is underscored by the observed improvements across four highly regarded benchmark datasets. The Parametric Contrastive Learning code resides on the GitHub platform, specifically at the location https://github.com/dvlab-research/Parametric-Contrastive-Learning.

White balancing in many imaging devices is achieved through the use of Image Signal Processors (ISP) which utilize computational color constancy. The recent use of deep convolutional neural networks (CNNs) is aimed at improving color constancy. Compared to comparable shallow learning approaches and statistical data, their performance shows a considerable improvement. However, the significant training data demands, the high computational cost, and the large model sizes prove problematic for deploying CNN-based methods on resource-scarce ISPs for real-time use cases. To surmount these constraints and attain performance on par with CNN-based techniques, a streamlined method is established for choosing the ideal simple statistics-based method (SM) per image. To accomplish this goal, we suggest a novel ranking-based color constancy technique (RCC), which treats the optimal SM method selection as a label ranking problem. RCC's distinctive ranking loss function is structured with a low-rank constraint for managing the model's complexity and a grouped sparse constraint for optimizing feature selection. The RCC model is used in the final step to foresee the arrangement of candidate SM methods for a test picture, and subsequently compute its illumination using the predicted superior SM method (or by integrating the estimates from the top k SM methods). Substantial experimental findings indicate that the proposed RCC method exhibits superior performance compared to virtually all shallow learning approaches, achieving a level of performance comparable to (and sometimes exceeding) deep CNN-based methods with a model size and training duration reduced by a factor of 2000. RCC displays impressive stability in the face of limited training samples, and excellent generalization across various cameras. Subsequently, seeking to remove the influence of ground truth illumination, we expand RCC into a novel ranking approach: RCC NO. This new approach trains its ranking model utilizing basic partial binary preference feedback gathered from non-expert annotators, rather than from specialized experts. With lower costs for sample collection and illumination measurement, RCC NO outperforms SM methods and most shallow learning-based methods in terms of performance.

Events-to-video (E2V) reconstruction and video-to-events (V2E) simulation are two central research subjects within event-based vision. The interpretability of deep neural networks commonly employed in E2V reconstruction is frequently hampered by their complexity. Moreover, the prevailing event simulators are designed to generate realistic events, but the exploration concerning enhancing event generation practices has been constrained. Employing a light and simple model-based deep network, this paper investigates E2V reconstruction, examines the diversity in adjacent pixel values for V2E generation, and concludes with a V2E2V architecture to demonstrate the impact of alternative event generation strategies on video reconstruction. E2V reconstruction leverages sparse representation models to model the connection between event occurrences and corresponding intensity values. Subsequently, a CISTA (convolutional ISTA network) is developed using the algorithm unfolding strategy. genetic approaches Introducing long short-term temporal consistency (LSTC) constraints provides a further means of enhancing temporal coherence. The V2E generation method incorporates the interleaving of pixels with varied contrast thresholds and low-pass bandwidths, anticipating an improved extraction of useful information from intensity measurements. Aldometanib Ultimately, the efficacy of this strategy is validated through the application of the V2E2V architectural framework. The CISTA-LSTC network's results indicate superior performance over existing state-of-the-art approaches, showcasing better temporal coherence. By detecting diverse elements in event generation, a greater level of detail becomes apparent, leading to a considerable enhancement in reconstruction quality.

An innovative approach to problem-solving, evolutionary multitask optimization aims at tackling multiple targets simultaneously. Multitask optimization problems (MTOPs) present a substantial obstacle in terms of effectively sharing knowledge among the tasks. While knowledge transfer is a desirable feature, there are two key limitations in the implementation of this feature in existing algorithms. The exchange of knowledge is restricted to aligned dimensions of distinct tasks, not based on similarities or correlations in other dimensions. Moreover, the transmission of understanding across similar dimensions within the same task is disregarded. To circumvent these two limitations, this article proposes an innovative and efficient scheme, dividing individuals into multiple blocks for block-level knowledge transmission. This framework is called block-level knowledge transfer (BLKT). BLKT's block-based population framework divides all individuals across all tasks into multiple blocks, with each block corresponding to a series of consecutive dimensions. In order to facilitate evolution, similar blocks originating from the same or multiple tasks are assimilated into the same cluster. BLKT facilitates knowledge transfer between dimensions that are alike, whether originally aligned or not, or whether they tackle the same task or different tasks, representing a more rational approach. Extensive testing across the CEC17 and CEC22 MTOP benchmarks, an advanced composite MTOP test suite, and practical MTOP applications reveals that BLKT-based differential evolution (BLKT-DE) surpasses the performance of state-of-the-art algorithms. Finally, another notable observation is that the BLKT-DE method demonstrates potential for effectively tackling single-task global optimization problems, achieving results that are competitive with the performance of several leading-edge algorithms.

This article investigates the model-free remote control problem in a wireless networked cyber-physical system (CPS) characterized by its spatially distributed sensors, controllers, and actuators. The states of the controlled system are observed by sensors, producing control instructions directed at the remote controller; simultaneously, actuators act on these instructions, ensuring the stability of the system. Under a model-free control architecture, the controller adopts the deep deterministic policy gradient (DDPG) algorithm for enabling control without relying on a system model. This paper departs from the traditional DDPG algorithm, which uses only the immediate system state, by including historical action data in its input. This expanded input enables more nuanced information extraction and results in superior control performance, especially in the presence of communication latency. The DDPG algorithm's experience replay strategy, in turn, employs a prioritized experience replay (PER) method augmented with reward values. A faster convergence rate, as per the simulation results, is a consequence of the proposed sampling policy, which establishes transition sampling probabilities contingent on a joint analysis of temporal difference (TD) error and reward.

The integration of data journalism into online news is associated with a concurrent increase in the application of visualizations to 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 achieve this, we initially reviewed online-gathered visualization thumbnails, then delved into thumbnail practices with data journalists and news graphic designers.