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Status: Unchecked ×Keyword: sparse sampling ×Clear all

2 claims from 1 paper

1. Computer Science › Multimodal Machine Learning Applications

   Less is More: CLIPBERT for Video-and-Language Learning via Sparse Sampling

   Lei, Li, Zhou et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2021

   The authors propose ClipBERT, a framework that learns video-and-language tasks end to end from a few sparsely sampled short clips, and test it on text-to-video retrieval and video question answering.

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   1. UncheckedClipBERT, trained end to end on a few sparsely sampled short clips, matched or beat methods using full-length videos on six video-and-language datasets.“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 usin…”
   2. UncheckedClipBERT was tested on datasets with very different video types and lengths, from 3-second GIFs to 180-second YouTube videos, which the authors say shows it generalises.“Videos in the datasets are from considerably different domains and lengths, ranging from 3-second generic domain GIF videos to 180-second YouTube human activity videos, showing the generalization ability of our approach.”