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Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Deep convolutional networks have achieved great success for visual recognition in still images. However, for action recognition in videos, the advantage over traditional methods is not so evident.
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
TLDR
A novel model of dynamic skeletons called Spatial-Temporal Graph Convolutional Networks (ST-GCN), which moves beyond the limitations of previous methods by automatically learning both the spatial and temporal patterns from data.
Unsupervised Feature Learning via Non-parametric Instance Discrimination
TLDR
This work forms this intuition as a non-parametric classification problem at the instance-level, and uses noise-contrastive estimation to tackle the computational challenges imposed by the large number of instance classes.
Towards Good Practices for Very Deep Two-Stream ConvNets
TLDR
This report presents very deep two-stream ConvNets for action recognition, by adapting recent very deep architectures into video domain, and extends the Caffe toolbox into Multi-GPU implementation with high computational efficiency and low memory consumption.
Temporal Action Detection with Structured Segment Networks
TLDR
The structured segment network (SSN) is presented, a novel framework which models the temporal structure of each action instance via a structured temporal pyramid and introduces a decomposed discriminative model comprising two classifiers, respectively for classifying actions and determining completeness.
UntrimmedNets for Weakly Supervised Action Recognition and Detection
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.
Temporal Segment Networks for Action Recognition in Videos
TLDR
The proposed TSN framework, called temporal segment network (TSN), aims to model long-range temporal structure with a new segment-based sampling and aggregation scheme and won the video classification track at the ActivityNet challenge 2016 among 24 teams.
Temporal Action Detection with Structured Segment Networks
This paper addresses an important and challenging task, namely detecting the temporal intervals of actions in untrimmed videos. Specifically, we present a framework called structured segment network
Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination
TLDR
This work forms this intuition as a non-parametric classification problem at the instance-level, and uses noise-contrastive estimation to tackle the computational challenges imposed by the large number of instance classes.
Recognize complex events from static images by fusing deep channels
TLDR
Inspired by the recent success of deep learning, a multi-layer framework is formulated to tackle the problem of event recognition, which takes into account both visual appearance and the interactions among humans and objects and combines them via semantic fusion.
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