According to various embodiments, a method for gesture recognition using a neural network is provided. The method comprises a training mode and an inference mode. In the training mode, the method includes: passing a dataset into the neural network; and training the neural network to recognize a gesture of interest, wherein the neural network includes a convolution-nonlinearity step and a recurrent step. The inference mode, the method includes: passing a series of images into the neural network, wherein the series of images is not part of the dataset; and recognizing the gesture of interest in the series of images.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for gesture recognition using a neural network, the method comprising: in a training mode: passing a dataset into the neural network; training the neural network to recognize a gesture of interest, wherein the neural network includes a convolution-nonlinearity step and a recurrent step; in an inference mode: passing a series of images into the neural network, wherein the series of images is not part of the dataset; recognizing the gesture of interest in the series of images. 2. The method of claim 1, wherein the dataset comprises a random subset of a video with known gestures of interest. 3. The method of claim 1, wherein the convolution-nonlinearity step comprises a convolution layer and a rectified linear layer. 4. The method of claim 1, wherein the convolution-nonlinearity step takes a third-order tensor as input and outputs a feature tensor. 5. The method of claim 1, wherein the convolution-nonlinearity step comprises a plurality of convolution-nonlinearity layer pairs, each convolution-nonlinearity layer pair comprising a convolution layer followed by a rectified linear layer. 6. The method of claim 1, wherein the recurrent step comprises a concatenation layer followed by a convolution layer, the concatenation layer taking as input two third-order tensors and outputting a concatenated third-order tensor, the convolution layer taking the concatenated third-order tensor as input and outputting a recurrent convolution layer output. 7. The method of claim 6, wherein the recurrent convolution layer output is inputted into a linear layer in order to produce a linear layer output, the linear layer output being a first-order tensor with a specific dimension corresponding to the number of gestures of interest. 8. The method of claim 7, wherein linear layer output is inputted into a sigmoid layer, the sigmoid layer transforming each output from the linear layer into a probability that a given gesture occurs within a current frame. 9. The method of claim 1, wherein during the recurrent step, a current frame depends on its own feature tensor and the feature tensor from all the frames preceding the current frame. 10. The method of claim 1, wherein, during the training mode, parameters in the neural network are updated using a stochastic gradient descent. 11. A system for gesture recognition using a neural network, comprising: one or more processors; memory; and one or more programs stored in the memory, the one or more programs comprising instructions to operate in a training mode and an inference mode; wherein in the training mode, the one or more programs comprise instructions for: passing a dataset into the neural network; training the neural network to recognize a gesture of interest, wherein the neural network includes a convolution-nonlinearity step and a recurrent step; wherein in the inference mode, the one or more programs comprise instructions to: passing a series of images into the neural network, wherein the series of image is not part of the dataset; and recognizing the gesture of interest in the series of images. 12. The system of claim 11, wherein the dataset comprises a random subset of a video with known gestures of interest. 13. The system of claim 11, wherein the convolution-nonlinearity step comprises a convolution layer and a rectified linear layer. 14. The system of claim 11, wherein the convolution-nonlinearity step takes a third-order tensor as input and outputs a feature tensor. 15. The system of claim 11, wherein the convolution-nonlinearity step comprises a plurality of convolution-nonlinearity layer pairs, each convolution-nonlinearity layer pair comprising a convolution layer followed by a rectified linear layer. 16. The system of claim 11, wherein the recurrent step comprises a concatenation layer followed by a convolution layer, the concatenation layer taking as input two third-order tensors and outputting a concatenated third-order tensor, the convolution layer taking the concatenated third-order tensor as input and outputting a recurrent convolution layer output. 17. The system of claim 16, wherein the recurrent convolution layer output is inputted into a linear layer in order to produce a linear layer output, the linear layer output being a first-order tensor with a specific dimension corresponding to the number of gestures of interest. 18. The system of claim 17, wherein linear layer output is inputted into a sigmoid layer, the sigmoid layer transforming each output from the linear layer into a probability that a given gesture occurs within a current frame. 19. The system of claim 11, wherein during the recurrent step, a current frame depends on its own feature tensor and the feature tensor from all the frames preceding the current frame. 20. A non-transitory computer readable storage medium storing one or more programs configured for execution by a computer, the one or more programs comprising instructions to operate in a training mode and an inference mode; wherein in the training mode, the one or more programs comprise instructions for: passing a dataset into the neural network; training the neural network to recognize a gesture of interest, wherein the neural network includes a convolution-nonlinearity step and a recurrent step; wherein in the inference mode, the one or more programs comprise instructions to: passing a series of images into the neural network, wherein the series of image is not part of the dataset; and recognizing the gesture of interest in the series of images.