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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书目名称Computer Vision – ACCV 2020影响因子(影响力)




书目名称Computer Vision – ACCV 2020影响因子(影响力)学科排名




书目名称Computer Vision – ACCV 2020网络公开度




书目名称Computer Vision – ACCV 2020网络公开度学科排名




书目名称Computer Vision – ACCV 2020被引频次




书目名称Computer Vision – ACCV 2020被引频次学科排名




书目名称Computer Vision – ACCV 2020年度引用




书目名称Computer Vision – ACCV 2020年度引用学科排名




书目名称Computer Vision – ACCV 2020读者反馈




书目名称Computer Vision – ACCV 2020读者反馈学科排名

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外来

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

考博

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

inculpate

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

不能仁慈

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

不能仁慈

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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