The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Dense-Captioning Events in Videos
Open accessAbstract
Most natural videos contain numerous events. For example, in a video of a “man playing a piano”, the video might also contain “another man dancing” or “a crowd clapping”. We introduce the task of dense-captioning events, which involves both detecting and describing events in a video. We propose a new model that is able to identify all events in a single pass of the video while simultaneously describing the detected events with natural language. Our model introduces a variant of an existing proposal module that is designed to capture both short as well as long events that span minutes. To capture the dependencies between the events in a video, our model introduces a new captioning module that uses contextual information from past and future events to jointly describe all events. We also introduce ActivityNet Captions, a large-scale benchmark for dense-captioning events. ActivityNet Captions contains 20k videos amounting to 849 video hours with 100k total descriptions, each with its unique start and end time. Finally, we report performances of our model for dense-captioning events, video retrieval and localization.
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Cites
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Large-Scale Video Classification with Convolutional Neural Networks
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Self-citationRecurrent neural network based language model
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Self-citationDeep visual-semantic alignments for generating image descriptions
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Self-citationHmdb: a large video database for human motion recognition
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Self-citationRecognizing human action in time-sequential images using hidden Markov model
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Cited by
Transformers in Vision: A Survey
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The “Something Something” Video Database for Learning and Evaluating Visual Common Sense
Goyal, R., Kahou, S. E., Michalski, V., Materzynska, J., Westphal, S., Kim, H., Haenel, V., Fruend, I., Yianilos, P., Mueller-Freitag, M., et al. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.622 · Cited by 1,471
Multimodal learning with transformers: A survey
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HellaSwag: Can a Machine Really Finish Your Sentence?
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Multi-modal Transformer for Video Retrieval
Gabeur, V., Sun, C., Alahari, K., & Schmid, C. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58548-8_13 · Cited by 615
Metadata
- Authors
- Krishna, R., Hata, K., Ren, F., Fei-Fei, L., & Niebles, J. C.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 706-715
- OpenAlex
- W2610163825
- Cited by
- 1,251
- References
- 10
- FWCI
- 20.22
- Type
- conference paper
- Language
- en
- Open access
- green
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