Solving the complex challenges of sophisticated terrain and multi-scale targets in remote sensing (RS) images requires a synergistic combination of Transformer and convolutional neural network (CNN). However, crafting...
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Solving the complex challenges of sophisticated terrain and multi-scale targets in remote sensing (RS) images requires a synergistic combination of Transformer and convolutional neural network (CNN). However, crafting effective CNN architectures remains a major challenge. To address these difficulties, this study introduces the knowledge guided evolutionary Transformer for RS scene classification (Evo RSFormer). It amalgamates adaptive evolutionary CNN (Evo CNN) with Transformers in a hybrid strategy synergistically, which combines fine-grained local feature extraction of CNNs with long-range contextual dependency modeling of Transformers. Furthermore, for the development of Evo CNN blocks, this paper presents a knowledge-guided adaptive efficient multi-objective evolutionary neural architecture search (MOE2-NAS) strategy. This approach markedly diminishes the labor-intensive characteristics associated with traditional CNN design, striking a balance for both accuracy and compactness. Additionally, by leveraging domain knowledge from natural scene analysis into the RS field, MOE2-NAS facilitates the efficiency of classical NAS. It utilizes a priori knowledge to generate promising initial solutions and constructs a surrogate model for efficient search. The effectiveness of the proposed Evo RSFormer has been rigorously tested on various benchmark RS datasets, including UC Merced, NWPU45, and AID. Empirical results strongly support the superiority of Evo RSFormer over existing methods. Furthermore, experiments on MOE2-NAS have been studied to confirm the important role of knowledge guidance in improving the efficiency of NAS.
Recently, neuralarchitecturesearch (NAS) has gained a lot of attention as a tool for constructing deep neural networks automatically. NAS methods have successfully found convolutional neural networks (CNNs) that exc...
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Recently, neuralarchitecturesearch (NAS) has gained a lot of attention as a tool for constructing deep neural networks automatically. NAS methods have successfully found convolutional neural networks (CNNs) that exceed human expert-designed networks on image classification in computer vision. However, there are growing demands for semantic segmentation in several areas including remote sensing image analysis. In this paper, we introduce an evolutionary NAS method for semantic segmentation of high-resolution aerial images. The proposed method leverages the complementary strengths of gene expression programming and cellular encoding to develop an encoding scheme, called symbolic linear generative encoding (SLGE), for evolving cells (directed acyclic graphs) as building-blocks to construct modularized encoder-decoder CNNs via an evolutionary process. SLGE can evolve cells with multi-branch and shortcut connections similar to the Inception-ResNet-like modules which can improve training and inference performance in deep neural networks. In experiments, we demonstrate the effectiveness of the proposed method on the challenging ISPRS Vaihingen, Potsdam and UAVid semantic segmentation benchmarks. Compared with recent state-of-the-art systems, our network, dubbed SLGENet, improves the overall accuracy performance on Vaihingen and Potsdam;and achieves a competitive overall accuracy on UAVid using fewer parameters. Our method achieves promising results in a little time of 2.5 GPU days.
As a popular research in the field of artificial intelligence in the last 2 years, evolutionary neural architecture search (ENAS) compensates the disadvantage that the construction of convolutional neural network (CNN...
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As a popular research in the field of artificial intelligence in the last 2 years, evolutionary neural architecture search (ENAS) compensates the disadvantage that the construction of convolutional neural network (CNN) relies heavily on the prior knowledge of designers. Since its inception, a great deal of researches have been devoted to improving its associated theories, giving rise to many related algorithms with pretty good results. Considering that there are still some limitations in the existing algorithms, such as the fixed depth or width of the network, the pursuit of accuracy at the expense of computational resources, and the tendency to fall into local optimization. In this article, a multi-objective genetic programming algorithm with a leader-follower evolution mechanism (LF-MOGP) is proposed, where a flexible encoding strategy with variable length and width based on Cartesian genetic programming is designed to represent the topology of CNNs. Furthermore, the leader-follower evolution mechanism is proposed to guide the evolution of the algorithm, with the external archive set composed of non-dominated solutions acting as the leader and an elite population updated followed by the external archive acting as the follower. Which increases the speed of population convergence, guarantees the diversity of individuals, and greatly reduces the computational resources. The proposed LF-MOGP algorithm is evaluated on eight widely used image classification tasks and a real industrial task. Experimental results show that the proposed LF-MOGP is comparative with or even superior to 35 existing algorithms (including some state-of-the-art algorithms) in terms of classification error and number of parameters.
neuralarchitecturesearch (NAS) algorithms have discovered highly novel state-of-the-art Convolutional neural Networks (CNNs) for image classification, and are beginning to improve our understanding of CNN architectu...
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ISBN:
(纸本)9781450371285
neuralarchitecturesearch (NAS) algorithms have discovered highly novel state-of-the-art Convolutional neural Networks (CNNs) for image classification, and are beginning to improve our understanding of CNN architectures. However, within NAS research, there are limited studies focussing on the role of skip-connections, and how the configurations of connections between layers can be optimised to improve CNN performance. Our work focusses on developing a new evolutionary NAS algorithm based on adjacency matrices to optimise skip-connection structures, creating more specialised and powerful skip-connection structures within a DenseNet-BC network than previously seen in the literature. Our work further demonstrates how simple adjacency matrices can be interpreted in a way which allows for a more dynamic variant of DenseNet-BC. The final algorithm, using this novel interpretation of adjacency matrices for architecture design and evolved on the CIFAR100 dataset, finds networks with improved performance relative to a baseline DenseNet-BC network on both the CIFAR10 and CIFAR100 datasets, being the first, to our knowledge, NAS algorithm for skip-connection optimisation to do so. Finally, skip-connection structures discovered by our algorithm are analysed, and some important skip-connection patterns are highlighted.
Tracking-by-detection approaches have demonstrated their strength in addressing Multiple Object Tracking (MOT) problems. DeepSORT, one of the classical tracking-by-etection MOT methods, relies on a deep appearance des...
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ISBN:
(数字)9781728186719
ISBN:
(纸本)9781728186719
Tracking-by-detection approaches have demonstrated their strength in addressing Multiple Object Tracking (MOT) problems. DeepSORT, one of the classical tracking-by-etection MOT methods, relies on a deep appearance descriptor to extract global appearance features of identities. Although the appearance descriptor acts as a key component of such tracking-y-detection methods, which is responsible for modeling appearance information, the relationship between it and tracking performance remains unclear, especially whether further improvements to it will be reflected in the tracking performance. To explore that, extensive experiments are conducted on the appearance descriptor by applying various traditional optimization methods. Furthermore, we propose an evolutionary neural architecture search (ENAS) strategy for the appearance descriptor named Genetic-SORT to assist exploration. The experimental results demonstrate that tracking performance fails to follow the improvements applied on the appearance descriptor and even shows a negative correlation, which is contrary to our intuition.
evolutionaryneural network architecturesearch (ENAS) has attracted the attention of many experts due to its global optimization capabilities to automatically search for convolutional neural network architectures bas...
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evolutionaryneural network architecturesearch (ENAS) has attracted the attention of many experts due to its global optimization capabilities to automatically search for convolutional neural network architectures based on the target task. The current search space for ENAS is not to design a fully structured network, but to search for smaller cell architectures to reduce search costs. However, blind search strategies do not effectively utilize the potential experience of the population. In order to utilize the potential experience learned by the current population to guide the evolutionarysearch of the population, we propose a similarity guided neural network architecturesearch algorithm based on cell architecture, which utilizes the similarity between pairwise architectures in the population as empirical knowledge learned by the population. Our proposed algorithm provides a novel method for calculating architecture similarity, which calculates architecture similarity separately from the cell and macro-structure. Then we decouple the connections and operations in the cell and calculate connection and operation similarity separately. In addition, we propose adaptive similarity selection and binary tournament selection strategies to enhance the algorithm's global and local search capabilities and effectively explore the search space. Finally, we design an improved single-point crossover operator to enhance the local search ability of the evolutionary operator. The experimental results show that SAGNAS is a competitive algorithm that achieves 97.44% and 81.60% in CIFAR10 and CIFAR100 with only 1.9 GPU-days spent.
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