The objective of video retrieval is as follows: given a text query and a pool of candidate videos, select the video which corresponds to the text query. Typically, the videos are returned as a ranked list of candidates and scored via document retrieval metrics.
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This paper proposes an end-to-end deep hashing framework with category mask for fast video retrieval.
In this paper, we introduce a network architecture that takes long-term content into account and enables fast per-video processing at the same time.
Ranked #28 on Action Recognition on Something-Something V1 (using extra training data)
This report summarizes the results of the first edition of the challenge together with the findings of the participants.
The rapid growth of video on the internet has made searching for video content using natural language queries a significant challenge.
Ranked #1 on Video Retrieval on DiDeMo
Experiments on text-to-video retrieval and video question answering on six datasets demonstrate that ClipBERT outperforms (or is on par with) existing methods that exploit full-length videos, suggesting that end-to-end learning with just a few sparsely sampled clips is often more accurate than using densely extracted offline features from full-length videos, proving the proverbial less-is-more principle.
The objective of this paper is self-supervised learning from video, in particular for representations for action recognition.