Wild fish recognition is a fundamental problem of ocean ecology research and contributes to the understanding of biodiversity. Given the huge number of wild fish species and unrecognized category, the essence of the p...
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Wild fish recognition is a fundamental problem of ocean ecology research and contributes to the understanding of biodiversity. Given the huge number of wild fish species and unrecognized category, the essence of the problem is an open set fine-grained recognition. Moreover, the unrestricted marine environment makes the problem even more challenging. Deep learning has been demonstrated as a powerful paradigm in image classification tasks. In this article, the wild fish recognition deep neural network (termed WildFishNet) is proposed. Specifically, an open set fine-grained recognition neural network with a fused activation pattern is constructed to implement wild fish recognition. First, three different reciprocal inverted residual structural modules are combined by neural structure search to obtain the best feature extraction performance for fine-grained recognition;next, a new fusion activation pattern of softmax and openmax functions is designed to improve the recognition ability of open set. Then, the experiments are implemented on the WildFish dataset that consists of 54 459 unconstrained images, which includes 685 known classes and 1 open set unrecognized category. Finally, the experimental results are analyzed comprehensively to demonstrate the effectiveness of the proposed method. The in-depth study also shows that artificial intelligence can empower marine ecosystem research.
Lightweight Network Architecture is essential for autonomous and intelligent monitoring of Unmanned Aerial Vehicles (UAVs), such as in object detection, image segmentation, and crowd counting applications. The state-o...
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Lightweight Network Architecture is essential for autonomous and intelligent monitoring of Unmanned Aerial Vehicles (UAVs), such as in object detection, image segmentation, and crowd counting applications. The state-of-the-art lightweight network learning based on neural Architecture search (NAS) usually costs enormous computation resources. Alternatively, low-performance embedded platforms and high-resolution drone images pose a challenge for lightweight network learning. To alleviate this problem, this paper proposes a new lightweight object detection model, called GhostShuffleNet (GSNet), for UAV images, which is built based on Zero-Shot neural Architecture search. This paper also introduces the new components which compose GSNet, namely GhostShuffle units (loosely based on ShuffleNetV2) and the backbone GSmodel-L. Firstly, a lightweight search space is constructed with the GhostShuffle (GS) units to reduce the parameters and floating-point operations (FLOPs). Secondly, the parameters, FLOPs, layers, and memory access cost (MAC) as constraints add to search strategy on a Zero-Shot neural structure search algorithm, which then searches for an optimal network GSmodelL. Finally, the optimal GSmodel-L is used as the backbone network and a Ghost-PAN feature fusion module and detection heads are added to complete the design of the lightweight object detection network (GSNet). Extensive experiments are conducted on the VisDrone2019 (14.92%mAP) dataset and the our UAV-OUC-DET (8.38%mAP) dataset demonstrating the efficiency and effectiveness of GSNet. The completed code is available at: https://***/yfq-yy/GSNet. (c) 2022 Elsevier B.V. All rights reserved.
Google Ads is an advertising agency that provides ads to advertisers. Advertisers match the user's search terms and push ads by selecting keywords related to their ad content. Keywords can determine the type of us...
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Google Ads is an advertising agency that provides ads to advertisers. Advertisers match the user's search terms and push ads by selecting keywords related to their ad content. Keywords can determine the type of users an advertiser pushes, the effectiveness of the ad promotion, and the sales of the ad product. Automatically selecting keywords that are satisfactory to advertisers from a large number of keywords provided by Google Ads is the main task of this paper. But there is not too much time for the model to judge whether keywords are selected, choosing correct keywords in the shortest time is another task of this paper. Therefore, a structure of the model that can get some useful keywords for advertisers is designed and an improved multi-objective particle swarm optimization algorithm is proposed to achieve this multiobjective task. These are also the main contributions of this paper. To accomplish this multi-objective task, many technical issues need to be overcome, such as the mixed language problem, the imbalance problem, the problem of extracting features from corpora and so on. This paper proposes a corpus selection method to solve the mixed problem of Chinese and English in keywords, word embedding method to solve the representation of keywords, re-sampling to solve data imbalance problem, improved convolutional neural network (CNN) to solve classification problem, and a multi-objective particle swarm optimization algorithm (MOPSO) to achieve neural structure search of CNN so that the effect of the classification is improved and the training time is reduced. The keyword selection problem is solved with the combination of evolutionary computing, deep learning, machine learning, and text processing techniques. Experimental results show that the proposed algorithm greatly improved the accuracy of keyword selection and shortened the time of selecting keywords. Therefore, this algorithm has a good application value.
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