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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Journal ArticleDOI
SRG: Snippet Relatedness-Based Temporal Action Proposal Generator
TL;DR: This paper proposes a new snippet score-based method, named Snippet Relatedness-based Generator (SRG), with a novel concept of “snippet relatedness”, which outperforms state-of-the-art methods for temporal action proposal generation and leads to significant improvements in temporal action detection.
Journal ArticleDOI
SODA: Weakly Supervised Temporal Action Localization Based on Astute Background Response and Self-Distillation Learning
TL;DR: The astute background response strategy is proposed, which can endow the conductive effect between video- level classification and frame-level classification, thus guiding the action category to suppress responses at background frames astutely and helping address the over- localization and joint-localization challenges.
Posted Content
Weakly Supervised Temporal Action Localization Using Deep Metric Learning
Ashraful Islam,Richard J. Radke +1 more
TL;DR: This work proposes a weakly supervised temporal action localization method that only requires video-level action instances as supervision during training, and proposes a classification module to generate action labels for each segment in the video, and a deep metric learning module to learn the similarity between different action instances.
Posted Content
Weakly-Supervised Action Localization by Generative Attention Modeling
TL;DR: In this paper, a conditional VAE model is proposed to model the class-agnostic frame-wise probability conditioned on the frame attention using conditional variational autoencoder (VAE).
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Scale Matters: Temporal Scale Aggregation Network for Precise Action Localization in Untrimmed Videos
TL;DR: Wang et al. as mentioned in this paper proposed a novel integrated temporal scale aggregation network (TSA-Net), which ensembles convolution filters with different dilation rates to enlarge the receptive field with low computational cost.
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