Results 21 to 30 of about 400 (142)

Multimodal Pretraining for Dense Video Captioning

Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, 2020
AACL-IJCNLP ...
Gabriel Huang, Bo Pang 0001, Zhenhai Zhu, Clara Rivera, Radu Soricut   +4 more
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An Efficient Framework for Dense Video Captioning

Proceedings of the AAAI Conference on Artificial Intelligence, 2020
Dense video captioning is an extremely challenging task since an accurate and faithful description of events in a video requires a holistic knowledge of the video contents as well as contextual reasoning of individual events. Most existing approaches handle this problem by first proposing event boundaries from a video and then captioning on a subset of
Maitreya Suin, A. N. Rajagopalan 0001
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Dense Procedure Captioning in Narrated Instructional Videos [PDF]

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019
Understanding narrated instructional videos is important for both research and real-world web applications. Motivated by video dense captioning, we propose a model to generate procedure captions from narrated instructional videos which are a sequence of step-wise clips with description.
Botian Shi, Lei Ji 0001, Yaobo Liang, Nan Duan 0001, Peng Chen 0029, Zhendong Niu, Ming Zhou 0001   +6 more
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Leveraging auxiliary image descriptions for dense video captioning

Pattern Recognition Letters, 2021
Abstract Collecting textual descriptions is an especially costly task for dense video captioning, since each event in the video needs to be annotated separately and a long descriptive paragraph needs to be provided. In this paper, we investigate a way to mitigate this heavy burden and propose to leverage captions of visually similar images as ...
Emre Boran, Aykut Erdem, Nazli Ikizler-Cinbis, Erkut Erdem, Pranava Madhyastha, Lucia Specia   +5 more
openaire   +3 more sources

End-to-End Dense Video Captioning with Masked Transformer [PDF]

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018
Dense video captioning aims to generate text descriptions for all events in an untrimmed video. This involves both detecting and describing events. Therefore, all previous methods on dense video captioning tackle this problem by building two models, i.e. an event proposal and a captioning model, for these two sub-problems. The models are either trained
Luowei Zhou, Yingbo Zhou 0002, Jason J. Corso, Richard Socher, Caiming Xiong   +4 more
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Weakly Supervised Dense Event Captioning in Videos

CoRR, 2018
Dense event captioning aims to detect and describe all events of interest contained in a video. Despite the advanced development in this area, existing methods tackle this task by making use of dense temporal annotations, which is dramatically source-consuming.
Xuguang Duan, Wen-bing Huang 0001, Chuang Gan 0001, Jingdong Wang 0001, Wenwu Zhu 0001, Junzhou Huang   +5 more
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Jointly Localizing and Describing Events for Dense Video Captioning [PDF]

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018
Automatically describing a video with natural language is regarded as a fundamental challenge in computer vision. The problem nevertheless is not trivial especially when a video contains multiple events to be worthy of mention, which often happens in real videos. A valid question is how to temporally localize and then describe events, which is known as
Yehao Li, Ting Yao 0003, Yingwei Pan, Hongyang Chao, Tao Mei 0001   +4 more
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End-to-end Dense Video Captioning as Sequence Generation

CoRR, 2022
Dense video captioning aims to identify the events of interest in an input video, and generate descriptive captions for each event. Previous approaches usually follow a two-stage generative process, which first proposes a segment for each event, then renders a caption for each identified segment.
Wanrong Zhu, Bo Pang 0001, Ashish V. Thapliyal, William Yang Wang, Radu Soricut   +4 more
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Understanding Video Narratives Through Dense Captioning with Linguistic Modules, Contextual Semantics, and Caption Selection

AI
Dense video captioning involves identifying, localizing, and describing multiple events within a video. Capturing temporal and contextual dependencies between events is essential for generating coherent and accurate captions.
Dvijesh Bhatt, Priyank Thakkar
doaj   +1 more source

Streaming Dense Video Captioning

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
An ideal model for dense video captioning -- predicting captions localized temporally in a video -- should be able to handle long input videos, predict rich, detailed textual descriptions, and be able to produce outputs before processing the entire video. Current state-of-the-art models, however, process a fixed number of downsampled frames, and make a
Xingyi Zhou, Anurag Arnab, Shyamal Buch, Shen Yan 0008, Austin Myers, Xuehan Xiong, Arsha Nagrani, Cordelia Schmid   +7 more
openaire   +4 more sources