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
ActionBytes: Learning From Trimmed Videos to Localize Actions
TL;DR: The advantage of ActionBytes for zero-shot localization as well as traditional weakly supervised localization, that train on long videos, to achieve state-of-the-art results are shown.
Book ChapterDOI
Part-Activated Deep Reinforcement Learning for Action Prediction
TL;DR: This paper designs the PA-DRL to exploit the structure of the human body by extracting skeleton proposals under a deep reinforcement learning framework and considers the saliency part for expressing actions.
Journal ArticleDOI
Learning Causal Temporal Relation and Feature Discrimination for Anomaly Detection
TL;DR: Wang et al. as mentioned in this paper proposed a method that consists of four modules to leverage the effect of the temporal cue and feature discrimination for anomaly detection, where the causal temporal relation module captures local-range temporal dependencies among features to enhance features, and the classifier projects enhanced features to the category space using the causal convolution and further expands the temporal modeling range.
Posted Content
Weakly-supervised Temporal Action Localization by Uncertainty Modeling
TL;DR: A new perspective on background frames is presented where they are modeled as out-of-distribution samples regarding their inconsistency and a background entropy loss is introduced to better discriminate background frames by encouraging their in-dist distribution (action) probabilities to be uniformly distributed over all action classes.
Proceedings ArticleDOI
Action Recognition From Single Timestamp Supervision in Untrimmed Videos
TL;DR: This work proposes a method that is supervised by single timestamps located around each action instance, in untrimmed videos, that replaces expensive action bounds with sampling distributions initialised from these timestampeds, and demonstrates that these distributions converge to the location and extent of discriminative action segments.
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Proceedings Article
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
TL;DR: Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.
Journal ArticleDOI
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
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