Amir Habibian
Amir Habibian
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Video2vec Embeddings Recognize Events when Examples are Scarce
This paper aims for event recognition when video examples are scarce or even completely absent. The key in such a challenging setting …
Amirhossein Habibian
,
Thomas Mensink
,
Cees GM Snoek
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Discovering Semantic Vocabularies for Cross-Media Retrieval
This paper proposes a data-driven approach for cross-media retrieval by automatically learning its underlying semantic vocabulary. …
Amirhossein Habibian
,
Thomas Mensink
,
Cees GM Snoek
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Videostory: A New Multimedia Embedding for Few-Example Recognition and Translation of Events
This paper proposes a data-driven approach for cross-media retrieval by automatically learning its underlying semantic vocabulary. …
Amirhossein Habibian
,
Thomas Mensink
,
Cees GM Snoek
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Recommendations for recognizing video events by concept vocabularies
Representing videos using vocabularies composed of concept detectors appears promising for generic event recognition. While many have …
Amirhossein Habibian
,
Cees GM Snoek
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Composite Concept Discovery for Zero-shot Video Event Detection
R We consider automated detection of events in video without the use of any visual training examples. A common approach is to represent …
Amirhossein Habibian
,
Thomas Mensink
,
Cees GM Snoek
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Cite
Recommendations for Video Event Recognition using Concept Vocabularies
Representing videos using vocabularies composed of concept detectors appears promising for event recognition. While many have recently …
Amirhossein Habibian
,
Koen EA van de Sande
,
Cees GM Snoek
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