Going deeper into action recognition
Citations
791 citations
529 citations
Cites background from "Going deeper into action recognitio..."
...For example, the legs motion for kicking a football is a simple action, while jumping for a head-shoot is a collective motion of legs, arms, head, and whole body [3]....
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489 citations
Cites background from "Going deeper into action recognitio..."
...Most of the prior work in action recognition is dedicated to hand-crafted features [18] such as dense trajectory features [15, 21, 41, 42]....
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471 citations
Cites background from "Going deeper into action recognitio..."
...Traditionally, the community has focused on activity recognition in the domain of RGB videos [34, 13]....
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391 citations
References
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"Going deeper into action recognitio..." refers background in this paper
...Another architecture based on LSTM is proposed by Donahue et al. (2015) to exploit end-toend training over the composite network as shown in Fig....
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...Generative models for action recognition are expected to discover long-term cues and deep models with LSTM cells are natural choices....
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...The LSTM autoencoder consists of two RNNs, namely the encoder LSTM and the decoder LSTM....
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...The LSTM autoencoder can be used to predict the future of a sequence as well....
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...Deep-generative architectures Vincent et al. (2008); Goodfellow et al. (2014); Hochreiter and Schmidhuber (1997) aim this goal, i.e., learning from temporal data in an unsupervised matter....
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42,067 citations
40,257 citations
38,211 citations
"Going deeper into action recognitio..." refers background or methods in this paper
...To sidestep various difficulties in training deep generative models, Goodfellow et al. (2014) introduced the adversarial networks where a generative model competes with a discriminative model known as an adversary. The discriminative model learns to determine whether a sample is coming from the generative model or the data itself. During training, the generative model learns to generate samples that share more similarities to the data to pass the adversary model’s test while adversary model improves its judgments on whether a given sample is authentic or not. To this end, Mathieu et al. (2015) adopted the adversarial methodology to train a multi-scale convolutional network for video prediction....
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...Deep-generative architectures Vincent et al. (2008); Goodfellow et al. (2014); Hochreiter and Schmidhuber (1997) aim this goal, i.e., learning from temporal data in an unsupervised matter....
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...To sidestep various difficulties in training deep generative models, Goodfellow et al. (2014) introduced the adversarial networks where a generative model competes with a discriminative model known as an adversary....
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...(2008); Goodfellow et al. (2014); Hochreiter and Schmidhuber (1997) aim this goal, i....
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