In a second stage, a transfer network focusing on parts and attributes is engineered, to anticipate and extract representative features for unseen attributes, drawing on supplementary prior information. Lastly, a network for completing prototypes is developed, leveraging these pre-established principles to achieve its purpose. Patient Centred medical home Moreover, a Gaussian-based prototype fusion strategy was created to address the issue of prototype completion error. It combines mean-based and completed prototypes, capitalizing on unlabeled data points. We have developed a complete and economical prototype for FSL, which circumvents the need for collecting rudimentary knowledge, enabling a fair comparison to existing FSL methods independent of external knowledge. Our methodology, backed by extensive experimentation, has produced more accurate prototypes, leading to superior performance in inductive and transductive few-shot learning problems. Publicly accessible on GitHub, our open-source Prototype Completion for FSL code is hosted at https://github.com/zhangbq-research/Prototype Completion for FSL.
Generalized Parametric Contrastive Learning (GPaCo/PaCo), a novel method, is presented in this paper, showcasing its proficiency with both imbalanced and balanced data. Supervised contrastive loss, as indicated by theoretical analysis, exhibits a bias towards high-frequency classes, ultimately escalating the difficulty of imbalanced learning scenarios. In order to rebalance, from an optimization perspective, we introduce parametric, class-wise, learnable centers. Additionally, we delve into our GPaCo/PaCo loss under a balanced environment. The analysis of GPaCo/PaCo shows that it can dynamically strengthen the pressure of pushing identical samples closer together as more samples concentrate around their respective centroids, thus promoting hard example learning. The emerging, leading-edge capabilities in long-tailed recognition are exemplified by experiments on long-tailed benchmarks. When assessed on the complete ImageNet dataset, models trained using GPaCo loss, from CNNs to vision transformers, demonstrate superior generalization and robustness, contrasting with MAE models. Moreover, the GPaCo model demonstrates its effectiveness in semantic segmentation, showing improvements across the four most prevalent benchmark datasets. Access our Parametric Contrastive Learning code repository at 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. Color constancy has seen the application of deep convolutional neural networks (CNNs) in recent times. Their performance surpasses that of shallow learning-based methods and corresponding statistical measures. Furthermore, the requirement for an expansive training sample set, the extensive computational needs, and the substantial size of the models render CNN-based methods infeasible for real-time deployment on low-resource internet service providers. 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. 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 approach to model design involves a specific ranking loss function, utilizing a low-rank constraint to manage complexity and a grouped sparse constraint to select features. 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). The outcome of comprehensive experiments indicates that the proposed RCC methodology consistently outperforms nearly all shallow learning techniques, attaining performance comparable to, and sometimes surpassing, deep CNN-based methods, whilst requiring only 1/2000th of the model size and training time. RCC showcases robust performance on limited training data, and generalizes effectively across multiple camera systems. For the purpose of detaching from the reliance on ground truth illumination, we develop a new ranking-based methodology from RCC, termed RCC NO. This ranking method uses uncomplicated partial binary preferences gathered from untrained annotators, contrasting with the use of expert judgments in prior methods. RCC NO exhibits a superior performance compared to the SM methods and most shallow learning-based techniques, while concurrently minimizing the costs associated with both sample collection and illumination measurement.
Fundamental research in event-based vision involves both video-to-events simulation and events-to-video reconstruction. Deep neural networks typically used for E2V reconstruction are often intricate and challenging to decipher. Additionally, current event simulators are built to create realistic events, but the investigation into upgrading the process of event generation remains scarce. This paper introduces a lightweight and simple model-based deep learning network for E2V reconstruction, analyzes the variety in adjacent pixel values during V2E generation, and subsequently builds a V2E2V architecture to demonstrate how various event generation methods improve video reconstruction. Sparse representation models are employed to model the association between events and intensity for the E2V reconstruction. Employing the algorithm unfolding strategy, a CISTA (convolutional ISTA network) is then fashioned. find more Further enhancing temporal coherence, long short-term temporal consistency (LSTC) constraints are introduced. The V2E generation proposes interleaving pixels with variable contrast thresholds and low-pass bandwidths, anticipating a more comprehensive extraction of insightful information from the intensity. driving impairing medicines 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. The identification of diverse event patterns during generation yields more nuanced details, ultimately enhancing reconstruction accuracy significantly.
Emerging research into evolutionary multitask optimization focuses on tackling multiple problems simultaneously. Multitask optimization problems (MTOPs) present a substantial obstacle in terms of effectively sharing knowledge among the tasks. Nonetheless, knowledge transfer in existing algorithms is hampered by two limitations. Knowledge moves across the aligned dimensions of various tasks, eschewing any connection with dimensions having similar or related characteristics. The dissemination of knowledge among the related facets contained within a single task is overlooked. In order to overcome these two limitations, this article introduces an innovative and efficient technique, which groups individuals into multiple blocks and transfers knowledge among them at the block level. This is the block-level knowledge transfer (BLKT) framework. To achieve a block-based population, BLKT distributes individuals from all tasks into multiple blocks, each composed of several consecutive dimensions. Blocks possessing similarities, whether stemming from one task or several, are unified into clusters for the purpose of evolution. 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 BLKT-based differential evolution (BLKT-DE) approach exhibits superior performance compared to current leading algorithms, as substantiated by extensive experimentation on the CEC17 and CEC22 MTOP benchmarks, a cutting-edge and challenging composite MTOP test suite, and real-world MTOP scenarios. Importantly, the BLKT-DE method also presents encouraging results for addressing single-task global optimization, achieving performance on par with several state-of-the-art algorithms.
The model-free remote control predicament within a spatially dispersed wireless networked cyber-physical system (CPS), encompassing sensors, controllers, and actuators, is addressed in this article. The controlled system's state is sensed by sensors, which issue control instructions to the remote controller; actuators, in response, carry out these commands to preserve the system's stability. Employing the deep deterministic policy gradient (DDPG) algorithm in the controller allows for model-free control in the system, enabling control independent of a model. This work proposes an alternative to the DDPG algorithm, which traditionally uses only the current system state. Instead, historical action data is included as part of the input. This enhancement allows for a more comprehensive data analysis and enables precise control, especially when communication latency is a factor. The prioritized experience replay (PER) method is incorporated into the DDPG algorithm's experience replay mechanism for the purpose of incorporating reward data. The results of the simulation show that the proposed sampling policy increases the convergence rate by calculating sampling probabilities for transitions using the temporal difference (TD) error and reward as factors.
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. In this paper, we undertake the task of understanding these design choices and determining the elements that make a visualization thumbnail engaging and easily interpretable. To realize this, our initial procedure was to scrutinize online-collected visualization thumbnails; we subsequently discussed visualization thumbnail practices with data journalists and news graphics designers.