Výsledky vyhledávání

Neural Feature Matching in Implicit 3D Representations

Autor: Chen, Y., Fernando, B., Bilen, H., Mensink, T., Gavves, E.

Publikováno v: Proceedings of Machine Learning Research, 139, 1582-1593

Recently, neural implicit functions have achieved impressive results for encoding 3D shapes. Conditioning on low-dimensional latent codes generalises a single implicit function to learn shared representation space for a variety of shapes, with the ad

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::284d6b3260473c259f81da5cb14df098
https://dare.uva.nl/personal/pure/en/publications/neural-feature-matching-in-implicit-3d-representations(67c7cd5a-ed8f-4199-ae10-087a9574ed52).html

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Three for one and one for three: Flow, Segmentation, and Surface Normals

Autor: Le, H.-A., Baslamisli, A.S., Mensink, T., Gevers, T.

Publikováno v: British Machine Vision Conference 2018: BMVC 2018, Newcastle, UK, September 3-6, 2018
British Machine Vision Conference 2018

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::758216f5043091e87844f8d7bba32c85
https://dare.uva.nl/personal/pure/en/publications/three-for-one-and-one-for-three-flow-segmentation-and-surface-normals(a0b3866e-fa3c-47db-943b-fbc3ce592805).html

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Video Stream Retrieval of Unseen Queries using Semantic Memory

Autor: Cappallo, S., Mensink, T., Snoek, C.G.M., Wilson, R.C., Hancock, E.R., Smith, W.A.P.

Publikováno v: Proceedings of the British Machine Vision Conference: BMVC 2016
Proceedings of the British Machine Vision Conference
BMVC

Retrieval of live, user-broadcast video streams is an under-addressed and increasingly relevant challenge. The on-line nature of the problem requires temporal evaluation and the unforeseeable scope of potential queries motivates an approach which can

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af97949627b46723109366bd1be6932b
http://arxiv.org/abs/1612.06753

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Akademický článek

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Event Fisher Vectors: Robust Encoding Visual Diversity of Visual Streams

Autor: Nagel, M., Mensink, T., Snoek, C.G.M., Xie, X., Jones, M.W., Tam, G.K.L.

Publikováno v: Proceedings of the British Machine Vision Conference 2015: BMVC 2015: 7-10 September, Swansea, UK
BMVC

In this paper we focus on event recognition in visual image streams. More specifically, we aim to construct a compact representation which encodes the diversity of the visual stream from just a few observations. For this purpose, we introduce the Eve

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2390375f8b89ae9815088a629aace50a
https://dare.uva.nl/personal/pure/en/publications/event-fisher-vectors-robust-encoding-visual-diversity-of-visual-streams(7f889ee6-3e1b-45c7-9c74-01cb32508feb).html

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Attributes Make Sense on Segmented Objects

Autor: Li, Z., Gavves, E., Mensink, T., Snoek, C.G.M., Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T.

Publikováno v: Computer Vision – ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014: proceedings, VI, 350-365
Computer Vision – ECCV 2014 ISBN: 9783319105987
ECCV (6)

In this paper we aim for object classification and segmentation by attributes. Where existing work considers attributes either for the global image or for the parts of the object, we propose, as our first novelty, to learn and extract attributes on s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4bc4c5efb2744a83e1c4dc78c1cc7475
https://dare.uva.nl/personal/pure/en/publications/attributes-make-sense-on-segmented-objects(28c60cc2-f222-4645-98ad-fb2e7ef3133c).html

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MediaMill at TRECVID 2014: Searching Concepts, Objects, Instances and Events in Video

Autor: Snoek, C.G.M., van de Sande, K.E.A., Fontijne, D., Cappallo, S., van Gemert, J., Habibian, A., Mensink, T., Mettes, P., Tao, R., Koelma, D.C., Smeulders, A.W.M.

Publikováno v: 2014 TREC Video Retrieval Evaluation: notebook papers and slides

In this paper we summarize our TRECVID 2014 video retrieval experiments. The MediaMill team participated in five tasks: concept detection, object localization, instance search, event recognition and recounting. We experimented with concept detection

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::9fa7962f08013d81ccd9e02613adf2b3
https://dare.uva.nl/personal/pure/en/publications/mediamill-at-trecvid-2014-searching-concepts-objects-instances-and-events-in-video(f177f123-3dff-41ec-8e37-e0ce2f1ca648).html

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Large Scale Metric Learning for Distance-Based Image Classification on Open Ended Data Sets

Autor: Mensink, T., Verbeek, J., Perronnin, F., Csurka, G., Farinella, G.M., Battiato, S., Cipolla, R

Publikováno v: Advanced Topics in Computer Vision ISBN: 9781447155195
Advanced Topics in Computer Vision
Farinella, Giovanni Maria and Battiato, Sebastiano and Cipolla, Roberto. Advanced Topics in Computer Vision, Springer, pp.243-276, 2013, 978-1-4471-5519-5. ⟨10.1007/978-1-4471-5520-1_9⟩
Advanced topics in computer vision, 243-276
STARTPAGE=243;ENDPAGE=276;TITLE=Advanced topics in computer vision

International audience; Many real-life large-scale datasets are open-ended and dynamic: new images are continuously added to existing classes, new classes appear over time, and the semantics of existing classes might evolve too. Therefore, we study l

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::df5055ef3299a8a8887f8ec4ac958fc8
https://doi.org/10.1007/978-1-4471-5520-1_9

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