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

Rejuvenate

盡管

https://doi.org/10.1007/978-3-642-67047-3ion). We implemented MC-RISE and evaluate them using two datasets (GTSRB and ImageNet) to demonstrate the effectiveness of our methods in comparison with existing techniques for interpreting image classification results.

顯赫的人

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助記

End-to-End Model-Based Gait Recognitionrimental results with the OU-MVLP and CASIA-B datasets demonstrate the state-of-the-art performance of the proposed method for both gait identification and verification scenarios, a direct consequence of the explicitly disentangled pose and shape features produced by the proposed end-to-end model-ba

perpetual

Horizontal Flipping Assisted Disentangled Feature Learning for Semi-supervised Person Re-identificatfeatures. It is free of labels, and can be applied to both supervised and unsupervised learning branches in our model. Extensive results on four Re-ID datasets demonstrate that by reducing 5/6 labeled data, Our method achieves the best performance on Market-1501 and CUHK03, and comparable accuracy o

起皺紋

MIX’EM: Unsupervised Image Classification Using a Mixture of Embeddings (ii), semantic categories emerge through the mixture coefficients, making it possible to apply (iii). Subsequently, we run K-means on the representations to acquire semantic classification. We conduct extensive experiments and analyses on STL10, CIFAR10, and CIFAR100-20 datasets, achieving state-of

Alveolar-Bone

Backbone Based Feature Enhancement for Object Detectionncy and accuracy. Without bells and whistles, our BBFE improves different baseline methods (both anchor-based and anchor-free) by a large margin (.2.0 points higher AP) on COCO, surpassing common feature pyramid networks including FPN and PANet.

Deceit

Long-Term Cloth-Changing Person Re-identificationcontribution, we propose a novel Re-ID method specifically designed to address the cloth-changing challenge. Specifically, we consider that under cloth-changes, soft-biometrics such as body shape would be more reliable. We, therefore, introduce a shape embedding module as well as a cloth-elimination

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