Localizing the Common Action Among a Few Videos

Autor: Yang, P., Hu, V.T., Mettes, P., Snoek, C.G.M., Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M.

Publikováno v: Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020 : proceedings, VII, 505-521
Computer Vision – ECCV 2020 ISBN: 9783030585709
ECCV (7)

This paper strives to localize the temporal extent of an action in a long untrimmed video. Where existing work leverages many examples with their start, their ending, and/or the class of the action during training time, we propose few-shot common act

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::63a54e2831caaa328e9ab946d05b9a5c
https://hdl.handle.net/11245.1/54444605-0ec3-4859-9392-bf042065487c

Latent Embedding Feedback and Discriminative Features for Zero-Shot Classification

Autor: Narayan, S., Gupta, A., Khan, F.S., Snoek, C.G.M., Shao, L., Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M.

Publikováno v: Computer Vision – ECCV 2020 ISBN: 9783030585419
ECCV (22)
Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020 : proceedings, XXII, 479-495

Zero-shot learning strives to classify unseen categories for which no data is available during training. In the generalized variant, the test samples can further belong to seen or unseen categories. The state-of-the-art relies on Generative Adversari

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::428e123cb608d50244fd2e7a45add468
https://dare.uva.nl/personal/pure/en/publications/latent-embedding-feedback-and-discriminative-features-for-zeroshot-classification(7be47683-4fdd-4e3b-aeb0-e8aa7595b518).html

Learning to Learn with Variational Information Bottleneck for Domain Generalization

Autor: Du, Y., Xu, J., Xiong, H., Qiu, Q., Zhen, X., Snoek, C.G.M., Shao, L., Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M.

Publikováno v: Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020 : proceedings, X, 200-216
Computer Vision – ECCV 2020 ISBN: 9783030586065
ECCV (10)

Domain generalization models learn to generalize to previously unseen domains, but suffer from prediction uncertainty and domain shift. In this paper, we address both problems. We introduce a probabilistic meta-learning model for domain generalizatio

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::292efcb981f1d93ff8505b5f7ea67a6e
https://dare.uva.nl/personal/pure/en/publications/learning-to-learn-with-variational-information-bottleneck-for-domain-generalization(e27f4b6e-bc2c-4ce7-a442-e5649d345ae1).html

NODIS: Neural Ordinary Differential Scene Understanding

Autor: Cong, Yuren, Ackermann, Hanno, Liao, Wentong, Yang, Michael Ying, Rosenhahn, Bodo, Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M.

Publikováno v: Computer Vision – ECCV 2020 ISBN: 9783030585648
ECCV (20)
Lecture Notes in Computer Science: Computer Vision-ECCV 2020, 12365, 636-653

Semantic image understanding is a challenging topic in computer vision. It requires to detect all objects in an image, but also to identify all the relations between them. Detected objects, their labels and the discovered relations can be used to con

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f2ad5cf7f3d8dd1aadf6fab38a80e595
https://doi.org/10.1007/978-3-030-58565-5_38

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