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

Rejuvenate

尽管

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.

显赫的人

malign

助记

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

起皱纹

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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