The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos
Open accessAbstract
Every moment counts in action recognition. A comprehensive understanding of human activity in video requires labeling every frame according to the actions occurring, placing multiple labels densely over a video sequence. To study this problem we extend the existing THUMOS dataset and introduce MultiTHUMOS, a new dataset of dense labels over unconstrained internet videos. Modeling multiple, dense labels benefits from temporal relations within and across classes. We define a novel variant of long short-term memory deep networks for modeling these temporal relations via multiple input and output connections. We show that this model improves action labeling accuracy and further enables deeper understanding tasks ranging from structured retrieval to action prediction.
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Cites
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Self-citationHmdb: a large video database for human motion recognition
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Action Recognition with Improved Trajectories
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ActivityNet: A large-scale video benchmark for human activity understanding
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Cited by
Long-Term Recurrent Convolutional Networks for Visual Recognition and Description
Donahue, J., Hendricks, L. A., Rohrbach, M., Venugopalan, S., Guadarrama, S., Saenko, K., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2599174 · Cited by 1,547
Sensor-based and vision-based human activity recognition: A comprehensive survey
Minh Dang, L., Min, K., Wang, H., Jalil Piran, M., Hee Lee, C., & Moon, H. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107561 · Cited by 730
Generating Visual Explanations
Hendricks, L. A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., & Darrell, T. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_1 · Cited by 534
Rescaling egocentric vision: Collection, pipeline and challenges for epic-kitchens-100
Damen, D., Doughty, H., Farinella, G. M., Furnari, A., Kazakos, E., Ma, J., Moltisanti, D., Munro, J., Perrett, T., Price, W., et al. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-021-01531-2 · Cited by 400
Scaling Egocentric Vision: The EPIC-KITCHENS Dataset
Damen, D., Doughty, H., Farinella, G. M., Fidler, S., Furnari, A., Kazakos, E., Moltisanti, D., Munro, J., Perrett, T., Price, W., et al. (2018) · arXiv · doi:10.48550/arxiv.1804.02748 · Cited by 274
Metadata
- Authors
- Yeung, S., Russakovsky, O., Jin, N., Andriluka, M., Mori, G., & Fei-Fei, L.
- Venue
- International Journal of Computer Vision
- Published
- 2017-05-22
- Volume
- 126
- Issue
- 2-4
- Pages
- 375-389
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2292288263
- Cited by
- 354
- References
- 10
- FWCI
- 18.53
- Type
- journal article
- Language
- en
- Open access
- green
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- Yeung, S., Russakovsky, O., Jin, N., Andriluka, M., Mori, G., & Fei-Fei, L. (2018). Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos. International Journal of Computer Vision. https://doi.org/10.1007/s11263-017-1013-y
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