Titlebook: Computer Vision – ACCV 2020; 15th Asian Conferenc Hiroshi Ishikawa,Cheng-Lin Liu,Jianbo Shi Conference proceedings 2021 Springer Nature Swi

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書目名稱Computer Vision – ACCV 2020影響因子(影響力)




書目名稱Computer Vision – ACCV 2020影響因子(影響力)學(xué)科排名




書目名稱Computer Vision – ACCV 2020網(wǎng)絡(luò)公開度




書目名稱Computer Vision – ACCV 2020網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Computer Vision – ACCV 2020被引頻次




書目名稱Computer Vision – ACCV 2020被引頻次學(xué)科排名




書目名稱Computer Vision – ACCV 2020年度引用




書目名稱Computer Vision – ACCV 2020年度引用學(xué)科排名




書目名稱Computer Vision – ACCV 2020讀者反饋




書目名稱Computer Vision – ACCV 2020讀者反饋學(xué)科排名

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Introspective Learning by Distilling Knowledge from Online Self-explanatione created explanations to improve the learning process has been less explored. The explanations extracted from a model can be used to guide the learning process of the model itself. Another type of information used to guide the training of a model is the knowledge provided by a powerful teacher mode

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Hyperparameter-Free Out-of-Distribution Detection Using Cosine Similarityring the nature of OOD samples, detection methods should not have hyperparameters that need to be tuned depending on incoming OOD samples. However, most recently proposed methods do not meet this requirement, leading to a compromised performance in real-world applications. In this paper, we propose

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Meta-Learning with Context-Agnostic Initialisationsl properties within training data (which we refer to as context), not relevant to the target task, which act as a distractor to meta-learning, particularly when the target task contains examples from a novel context not seen during training..We address this oversight by incorporating a context-adver

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Second Order Enhanced Multi-glimpse Attention in Visual Question Answeringion from both visual and textual modalities. Previous endeavours of VQA are made on the good attention mechanism, and multi-modal fusion strategies. For example, most models, till date, are proposed to fuse the multi-modal features based on implicit neural network through cross-modal interactions. T

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Localize to Classify and Classify to Localize: Mutual Guidance in Object Detectionand ground truth boxes to evaluate the matching quality between anchors and objects. In this paper, we question this use of IoU and propose a new anchor matching criterion guided, during the training phase, by the optimization of both the localization and the classification tasks: the predictions re

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