UntrimmedNets for Weakly Supervised Action Recognition and Detection
Limin Wang,Yuanjun Xiong,Dahua Lin,Luc Van Gool +3 more
- pp 6402-6411
TLDR
This paper presents a new weakly supervised architecture, called UntrimmedNet, which is able to directly learn action recognition models from untrimmed videos without the requirement of temporal annotations of action instances.Abstract:
Current action recognition methods heavily rely on trimmed videos for model training. However, it is expensive and time-consuming to acquire a large-scale trimmed video dataset. This paper presents a new weakly supervised architecture, called UntrimmedNet, which is able to directly learn action recognition models from untrimmed videos without the requirement of temporal annotations of action instances. Our UntrimmedNet couples two important components, the classification module and the selection module, to learn the action models and reason about the temporal duration of action instances, respectively. These two components are implemented with feed-forward networks, and UntrimmedNet is therefore an end-to-end trainable architecture. We exploit the learned models for action recognition (WSR) and detection (WSD) on the untrimmed video datasets of THUMOS14 and ActivityNet. Although our UntrimmedNet only employs weak supervision, our method achieves performance superior or comparable to that of those strongly supervised approaches on these two datasets.read more
Citations
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Proceedings ArticleDOI
Step-by-step Erasion, One-by-one Collection: A Weakly Supervised Temporal Action Detector
TL;DR: A detector is trained by driving a series of classifiers to find new actionness clips progressively, via step-by-step erasion from a complete video, and even compares with quite a few strongly supervised methods.
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CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection
TL;DR: The proposed weakly supervised anomaly detection method obtains 83.03% and 89.67% frame-level AUC performance on the UCF Crime and ShanghaiTech datasets respectively, demonstrating its superiority over the existing state-of-the-art algorithms.
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PA3D: Pose-Action 3D Machine for Video Recognition
TL;DR: This work proposes a concise Pose-Action 3D Machine (PA3D), which can effectively encode multiple pose modalities within a unified 3D framework, and consequently learn spatio-temporal pose representations for action recognition.
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BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation
TL;DR: BSN++ is presented, a new framework which exploits complementary boundary regressor and relation modeling for temporal proposal generation and introduces the scale-balanced re-sampling strategy.
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3C-Net: Category Count and Center Loss for Weakly-Supervised Action Localization
TL;DR: Wang et al. as mentioned in this paper proposed a weakly supervised temporal action localization framework, called 3C-Net, which only requires video-level supervision in the form of action category labels and corresponding count.
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