Subsequently, a part/attribute transfer network is created to acquire and interpret representative features for unseen attributes, utilizing supplementary prior knowledge. Lastly, a network for completing prototypes is developed, leveraging these pre-established principles to achieve its purpose. Medical adhesive The Gaussian-based prototype fusion strategy, developed to mitigate the prototype completion error, merges mean-based and completed prototypes, making use of unlabeled examples. 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. The results of extensive trials confirm that our method produces more accurate prototypes and achieves superior performance in inductive as well as transductive few-shot learning contexts. The open-source code for the Prototype Completion for FSL project is located on GitHub, specifically at https://github.com/zhangbq-research/Prototype Completion for FSL.

Our proposed approach, Generalized Parametric Contrastive Learning (GPaCo/PaCo), performs well on both imbalanced and balanced datasets, as detailed in this paper. Based on a theoretical framework, we find that supervised contrastive loss exhibits a preference for high-frequency classes, consequently increasing the complexity of imbalanced learning. To rebalance from an optimization viewpoint, we introduce a set of parametric class-wise learnable centers. Additionally, we delve into our GPaCo/PaCo loss under a balanced environment. GPaCo/PaCo, as revealed by our analysis, shows an adaptive ability to intensify the force of pushing similar samples closer, as more samples cluster around their respective centroids, ultimately contributing to hard example learning. The emerging, leading-edge capabilities in long-tailed recognition are exemplified by experiments on long-tailed benchmarks. Models on ImageNet, trained using GPaCo loss, from CNN architectures to vision transformers, exhibit stronger generalization performance and resilience than MAE models. Subsequently, GPaCo demonstrates its effectiveness in semantic segmentation, displaying significant enhancements on four leading benchmark datasets. Our Parametric Contrastive Learning code is readily available for download from this GitHub repository: https://github.com/dvlab-research/Parametric-Contrastive-Learning.

Image Signal Processors (ISP), in many imaging devices, are designed to use computational color constancy to ensure proper white balancing. For color constancy, deep convolutional neural networks (CNNs) have become increasingly prevalent recently. When measured against shallow learning approaches and statistical data, their performance exhibits a substantial increase. Although beneficial, the extensive training sample needs, the computationally intensive nature of the task, and the substantial model size render CNN-based methods ill-suited for deployment on low-resource ISPs in real-time operational settings. To compensate for these impediments and accomplish results on a par with CNN-based methodologies, a well-defined method is introduced to select the best simple statistics-based method (SM) for each individual image. With this in mind, we introduce a novel ranking-based color constancy method, RCC, where the choice of the best SM method is formulated as a label ranking problem. To design a specific ranking loss function, RCC employs a low-rank constraint, thereby managing model intricacy, and a grouped sparse constraint for selecting key features. Finally, the RCC model is applied to anticipate the succession of the suggested SM approaches for a specimen image, and then calculating its illumination by adopting the projected ideal SM technique (or by combining the outcomes generated by the most effective k SM methods). Extensive experimentation validates the superior performance of the proposed RCC method, demonstrating its ability to outperform nearly all shallow learning techniques and match or exceed the performance of deep CNN-based approaches while using only 1/2000th the model size and training time. The robustness of RCC extends to limited training samples, and its performance generalizes across different camera perspectives. Moreover, to eliminate reliance on ground truth illumination, we extend RCC to develop a novel ranking-based approach, RCC NO, that eschews ground truth illumination. This approach learns the ranking model using basic partial binary preference markings from untrained annotators instead of relying on experts. RCC NO demonstrates superior performance compared to SM methods and the majority of shallow learning-based approaches, all while minimizing the costs associated with sample collection and illumination measurement.

E2V reconstruction and V2E simulation represent two core research pillars within the realm of event-based vision. Interpreting current deep neural networks designed for E2V reconstruction presents a significant challenge due to their intricate nature. 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 present a streamlined, model-driven deep learning network for E2V reconstruction in this paper, alongside an examination of the diversity of adjacent pixel values in the V2E generation process. This is followed by the development of a V2E2V architecture to evaluate the effects of varying event generation strategies on video reconstruction accuracy. To model the relationship between events and intensity within the E2V reconstruction framework, we utilize sparse representation models. A convolutional ISTA network, known as CISTA, is then developed with the use of the algorithm unfolding technique. Fasiglifam price The temporal coherence is enhanced by adding long short-term temporal consistency (LSTC) constraints. Within the V2E generation, we propose interleaving pixels with distinct contrast thresholds and low-pass bandwidths, anticipating that this approach will yield more insightful intensity information. Medical billing The V2E2V architecture is leveraged to verify the success of this strategy's implementation. Our CISTA-LSTC network's results demonstrate superior performance compared to current leading methods, achieving enhanced temporal consistency. The introduction of diversity into the event generation process reveals a significant amount of fine-grained detail, leading to an improved reconstruction quality.

The pursuit of solving multiple tasks simultaneously is driving the evolution of multitask optimization methods. Multitask optimization problems (MTOPs) are frequently complicated by the difficulty in effectively sharing knowledge between and amongst various tasks. Yet, the transmission of knowledge in existing algorithms is constrained by two factors. Knowledge is exchanged exclusively between tasks where corresponding dimensions coincide, sidestepping the involvement of comparable or related dimensions. Concerning knowledge exchange, related dimensions within the same job are disregarded. Overcoming these two limitations, this article suggests a creative and effective method, organizing individuals into multiple blocks for the transference of knowledge at the block level. This is the block-level knowledge transfer (BLKT) framework. BLKT groups individuals associated with all tasks into multiple blocks, each covering a sequence of several dimensions. To enable evolution, similar blocks, originating either from a single task or from multiple tasks, are clustered together. The transfer of knowledge across similar dimensions, enabled by BLKT, is rational, irrespective of whether these dimensions are initially aligned or unaligned, and irrespective of whether they deal with equivalent or distinct tasks. The CEC17 and CEC22 MTOP benchmarks, along with a complex composite MTOP test suite and real-world MTOP applications, all demonstrate that BLKT-based differential evolution (BLKT-DE) possesses superior performance against existing leading algorithms. Moreover, an intriguing observation is that the BLKT-DE approach also exhibits potential in resolving single-task global optimization challenges, yielding results comparable to those of some of the most advanced algorithms currently available.

In a wireless networked cyber-physical system (CPS) with distributed sensors, controllers, and actuators, this article explores the model-free remote control problem. Data gathered from the controlled system's state by sensors is used to generate control instructions for the remote controller; actuators then execute these commands to maintain the system's stability. To achieve control within a model-free system, the deep deterministic policy gradient (DDPG) algorithm is employed within the controller to facilitate model-independent control. Distinguishing itself from the standard DDPG algorithm, which only employs the system's current state, this article integrates historical action information into its input. This enriched input allows for enhanced information retrieval and precise control, particularly beneficial in cases of communication lag. Within the DDPG algorithm's experience replay framework, the prioritized experience replay (PER) procedure is utilized, which takes the reward into consideration. The simulation data reveals that the proposed sampling policy accelerates convergence by establishing sampling probabilities for transitions, factoring in both the temporal difference (TD) error and reward.

As online news outlets increasingly feature data journalism, a parallel surge in the utilization of visualizations is observed within 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. Thus, we propose to investigate these design selections and pinpoint the qualities that define an attractive and understandable visualization thumbnail. For this purpose, we commenced by examining online-collected visualization thumbnails and subsequently engaged in dialogues with data journalists and news graphics designers about thumbnail strategies.