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Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward
[article]
2018
arXiv
pre-print
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To train our DSN, we propose an end-to-end, reinforcement learning-based framework, where we design a novel reward function that jointly accounts for diversity and representativeness of generated summaries ...
During training, the reward function judges how diverse and representative the generated summaries are, while DSN strives for earning higher rewards by learning to produce more diverse and more representative ...
Acknowledgments We thank Ke Zhang and Wei-Lun Chao for discussions of details of their paper (Zhang et al. 2016b) . ...
arXiv:1801.00054v3
fatcat:hl2dexakjfa4zbptukr3buzfve
Deep Reinforcement Learning for Unsupervised Video Summarization With Diversity-Representativeness Reward
2018
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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To train our DSN, we propose an end-to-end, reinforcement learning-based framework, where we design a novel reward function that jointly accounts for diversity and representativeness of generated summaries ...
During training, the reward function judges how diverse and representative the generated summaries are, while DSN strives for earning higher rewards by learning to produce more diverse and more representative ...
Acknowledgments We thank Ke Zhang and Wei-Lun Chao for discussions of details of their paper (Zhang et al. 2016b) ...
doi:10.1609/aaai.v32i1.12255
fatcat:nziaj4wwunbsbfdu4cpyn3ytyi
Ultrasound Video Summarization using Deep Reinforcement Learning
[article]
2020
arXiv
pre-print
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Our approach is framed as reinforcement learning problem and produces agents focusing on the preservation of important diagnostic information. ...
Video is an essential imaging modality for diagnostics, e.g. in ultrasound imaging, for endoscopy, or movement assessment. ...
In this paper, we present an ultrasound imaging summarization method using deep reinforcement learning. We show effectiveness for the example of fetal ultrasound screening. ...
arXiv:2005.09531v1
fatcat:7smho4n6lber3ccxzwvvfac6yi
Weakly Supervised Video Summarization by Hierarchical Reinforcement Learning
[article]
2020
arXiv
pre-print
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Conventional video summarization approaches based on reinforcement learning have the problem that the reward can only be received after the whole summary is generated. ...
With the guide of the subgoal, the worker predicts the importance scores for video frames in the subtask by policy gradient according to both global reward and innovative defined sub-rewards to overcome ...
ACKNOWLEDGMENTS This work was partially financially supported by the Grants-in-Aid for Scientific Research Numbers JP19H05590 and JP19K20289. ...
arXiv:2001.05864v2
fatcat:mdkkrc75zjex3jnvk7blnqemqe
AudViSum: Self-Supervised Deep Reinforcement Learning for Diverse Audio-Visual Summary Generation
2021
British Machine Vision Conference
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To this end, we introduce a novel self-supervised audio-visual summarization network AudViSum, that leverages both audio and visual information and employs Deep Reinforcement Learning to reward the model ...
To ensure diverse summary generation we report the top-3 summaries for each video. ...
[58] proposed an unsupervised diversity-representativeness reward to guide the RL agent for video summarization. Taking cue from them we introduce a weighted reward function. ...
dblp:conf/bmvc/ChowdhuryPDB21
fatcat:qbzd2my6hreipl3xjzt55fkdoq
Interp-SUM: Unsupervised Video Summarization with Piecewise Linear Interpolation
2021
Sensors
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To train the video summarization network, we exploit a reinforcement learning-based framework with an explicit reward function. ...
This paper addresses the problem of unsupervised video summarization. Video summarization helps people browse large-scale videos easily with a summary from the selected frames of the video. ...
Recently, the reinforcement learning (RL) based unsupervised video summarization method outperformed in results with an explicit reward function to select keyframes [4] . ...
doi:10.3390/s21134562
fatcat:jjn655pcybbj5ove65lphehzae
Spatial attention model‐modulated bi‐directional long short‐term memory for unsupervised video summarisation
2021
Electronics Letters
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summarisation, deep reinforcement-deep summarization network (DR-DSN), which comprises a novel feedback reward with the aspects of diversity and representativeness. ...
Given the excellent performance of reinforcement learning in video representation, Zhou [6] subsequently presented an end-to-end long short-term memory (LSTM)-based unsupervised learning method for video ...
summarisation, deep reinforcement-deep summarization network (DR-DSN), which comprises a novel feedback reward with the aspects of diversity and representativeness. ...
doi:10.1049/ell2.12111
fatcat:ovrvf6dskbh65dra5cxgnfbqwm
Video Summarization Using Deep Neural Networks: A Survey
[article]
2021
arXiv
pre-print
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After presenting the motivation behind the development of technologies for video summarization, we formulate the video summarization task and discuss the main characteristics of a typical deep-learning-based ...
This work focuses on the recent advances in the area and provides a comprehensive survey of the existing deep-learning-based methods for generic video summarization. ...
using a diversity-representativeness reward. ...
arXiv:2101.06072v2
fatcat:7mozntfhdrf3lkw6pwcr5v2rpu
Deep Reinforcement Learning for Query-Conditioned Video Summarization
2019
Applied Sciences
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After that, a deep reinforcement learning-based summarization network (SummNet) is developed to provide personalized summaries by integrating relatedness, representativeness and diversity rewards. ...
Query-conditioned video summarization requires to (1) find a diverse set of video shots/frames that are representative for the whole video, and that (2) the selected shots/frames are related to a given ...
For example, Zhou and Qiao [25] develop a deep reinforcement learning-based summarization network with a diversity-representativeness reward to generate summaries, and achieve a good performance on generic ...
doi:10.3390/app9040750
fatcat:5nsbvbpxtjgbfejvm2xgjsxgba
Unsupervised multi-latent space reinforcement learning framework for video summarization in ultrasound imaging
[article]
2021
arXiv
pre-print
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We propose a new unsupervised reinforcement learning (RL) framework with novel rewards that facilitates unsupervised learning avoiding tedious and impractical manual labelling for summarizing ultrasound ...
Our new paradigm for video summarization is capable of delivering classification labels and segmentation of key landmarks for each of the summarized keyframes. ...
The authors are grateful to Compute Canada and the NVIDIA and CDAC for giving access to the PARAM SIDDHI AI system for providing the computing resources for the project. ...
arXiv:2109.01309v1
fatcat:urzvzwunczegppchpvy73vp4dq
AC-SUM-GAN: Connecting Actor-Critic and Generative Adversarial Networks for Unsupervised Video Summarization
2020
IEEE transactions on circuits and systems for video technology (Print)
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This paper presents a new method for unsupervised video summarization. ...
Moreover, the introduced criterion for choosing the best model after the training ends, enables the automatic selection of proper values for parameters of the training process that are not learned from ...
Unsupervised Video Summarization To avoid using ground-truth-annotated training data for learning video summarization, most existing unsupervised approaches focus on the principle that a representative ...
doi:10.1109/tcsvt.2020.3037883
fatcat:trhxq7kusra7he7eop33lf6tau
Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net
[article]
2021
arXiv
pre-print
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A 3D spatio-temporal U-Net is used to efficiently encode spatio-temporal information of the input videos for downstream reinforcement learning (RL). ...
In this paper, we introduce the 3DST-UNet-RL framework for video summarization. ...
the representativeness reward Rrep and diversity reward R div . ...
arXiv:2106.10528v1
fatcat:6q3wnioqtrdktkfl5w5n3pjthi
Summarizing Videos using Concentrated Attention and Considering the Uniqueness and Diversity of the Video Frames
2022
Proceedings of the 2022 International Conference on Multimedia Retrieval
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In this work, we describe a new method for unsupervised video summarization. ...
To overcome limitations of existing unsupervised video summarization approaches, that relate to the unstable training of Generator-Discriminator architectures, the use of RNNs for modeling long-range frames ...
Session 4B: Captioning and Summarization ICMR '22, June 27-30, 2022, Newark, NJ, USA. ...
doi:10.1145/3512527.3531404
fatcat:wj7utepnmvccbeipf4aukg6vpe
How Local is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization
[article]
2018
arXiv
pre-print
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To tackle these problems, we instead devise a reinforcement learning algorithm for training the proposed model. ...
The large volume of video content and high viewing frequency demand automatic video summarization algorithms, of which a key property is the capability of modeling diversity. ...
B.G. would like to thank Trevor Darrell, Charless Fowlkes, Alexander Ihler, Dequan Wang, and Huazhe Xu for the insightful discussions on SeqDPP which inspired this work. ...
arXiv:1807.04219v4
fatcat:sewoyvmfxjb5ngxtxrrmr3x7ze
How Local Is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization
[chapter]
2018
Lecture Notes in Computer Science
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To tackle these problems, we instead devise a reinforcement learning algorithm for training the proposed model. ...
The large volume of video content and high viewing frequency demand automatic video summarization algorithms, of which a key property is the capability of modeling diversity. ...
B.G. would like to thank Trevor Darrell, Charless Fowlkes, Alexander Ihler, Dequan Wang, and Huazhe Xu for the insightful discussions on SeqDPP which inspired this work. ...
doi:10.1007/978-3-030-01237-3_10
fatcat:bwqpoy5kxbb5bommka73vn246u
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