DAPs: Deep Action Proposals for Action Understanding

Escorcia, Víctor; Heilbron, Fabian Caba; Niebles, Juan Carlos; Ghanem, Bernard

doi:10.1007/978-3-319-46487-9_47

Cited by 366 publications

(320 citation statements)

References 32 publications

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“…To achieve high prediction accuracy, most of the existing state-of-the-art algorithms for temporal action proposals use supervised deep learning approaches [3,14,15,23]. Such approaches require large amount of labeled videos.…”

Section: Introductionmentioning

confidence: 99%

Learning Temporal Action Proposals With Fewer Labels

Cao

Niebles

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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Temporal action proposals are a common module in action detection pipelines today. Most current methods for training action proposal modules rely on fully supervised approaches that require large amounts of annotated temporal action intervals in long video sequences. The large cost and effort in annotation that this entails motivate us to study the problem of training proposal modules with less supervision. In this work, we propose a semi-supervised learning algorithm specifically designed for training temporal action proposal networks. When only a small number of labels are available, our semi-supervised method generates significantly better proposals than the fully-supervised counterpart and other strong semi-supervised baselines. We validate our method on two challenging action detection video datasets, ActivityNet v1.3 and THUMOS14. We show that our semi-supervised approach consistently matches or outperforms the fully supervised state-of-the-art approaches.

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Section: Introductionmentioning

confidence: 99%

Learning Temporal Action Proposals With Fewer Labels

Cao

Niebles

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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“…Since natural videos are always long and untrimmed, temporal action proposals and detection have aroused intensive interest from researchers [6,26,1,25,3,8]. DAP [4] leverages LSTM to encode the video sequence for temporal features. SST [1] presents a method combined C3D and GRU to generate temporal action proposals, trying to capture long-time dependency.…”

Section: Related Workmentioning

confidence: 99%

Deep Point-Wise Prediction for Action Temporal Proposal

Kong²,

Sun

et al. 2019

Lecture Notes in Computer Science

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Detecting actions in videos is an important yet challenging task. Previous works usually utilize (a) sliding window paradigms, or (b) per-frame action scoring and grouping to enumerate the possible temporal locations. Their performances are also limited to the designs of sliding windows or grouping strategies. In this paper, we present a simple and effective method for temporal action proposal generation, named Deep Point-wise Prediction (DPP). DPP simultaneously predicts the action existing possibility and the corresponding temporal locations, without the utilization of any handcrafted sliding window or grouping. The whole system is end-to-end trained with joint loss of temporal action proposal classification and location prediction. We conduct extensive experiments to verify its effectiveness, generality and robustness on standard THUMOS14 dataset. DPP runs more than 1000 frames per second, which largely satisfies the real-time requirement. The code is available at https://github.com/liluxuan1997/DPP.

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“…end for 5: % after above loop 6: end for Uncut silhouette video τ (1) τ (2) τ (3) … τ (w) Generating Motion History Images (MHIs)…”

Section: B Clustering Of Mhis Into Action Proposalsmentioning

confidence: 99%

“…1 [ ,..., ] a P P , which are temporally non-overlapping. Hence we can directly use these proposals as multiple temporal segments of a long uncut video.…”

Section: Clustering Mhis Into Action Proposalsmentioning

confidence: 99%