Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for parallel processing of video frames using neural networks. One of the methods includes receiving a video sequence comprising a respective video frame at each of a plurality of time steps; and processing the video sequence using a video processing neural network to generate a video processing output for the video sequence, wherein the video processing neural network includes a sequence of network components, wherein the network components comprise a plurality of layer blocks each comprising one or more neural network layers, wherein each component is active for a respective subset of the plurality of time steps, and wherein each layer block is configured to, at each time step at which the layer block is active, receive an input generated at a previous time step and to process the input to generate a block output.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying actions in a video. One of the methods obtaining a feature representation of a video clip; obtaining data specifying a plurality of candidate agent bounding boxes in the key video frame; and for each candidate agent bounding box: processing the feature representation through an action transformer neural network.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying actions in a video. One of the methods obtaining a feature representation of a video clip; obtaining data specifying a plurality of candidate agent bounding boxes in the key video frame; and for each candidate agent bounding box: processing the feature representation through an action transformer neural network.

Abstract: Techniques are disclosed that enable processing a video capturing a periodic activity using a repetition network to generate periodic output (e.g., a period length of the periodic activity captured in the video and/or a frame wise periodicity indication of the video capturing the periodic activity). Various implementations include a class agnostic repetition network which can be used to generate periodic output for a wide variety of periodic activities. Additional or alternative implementations include generating synthetic repetition videos which can be utilized to train the repetition network.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using an image processing neural network system that includes a spatial transformer module. One of the methods includes receiving an input feature map derived from the one or more input images, and applying a spatial transformation to the input feature map to generate a transformed feature map, comprising: processing the input feature map to generate spatial transformation parameters for the spatial transformation, and sampling from the input feature map in accordance with the spatial transformation parameters to generate the transformed feature map.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for parallel processing of video frames using neural networks. One of the methods includes receiving a video sequence comprising a respective video frame at each of a plurality of time steps; and processing the video sequence using a video processing neural network to generate a video processing output for the video sequence, wherein the video processing neural network includes a sequence of network components, wherein the network components comprise a plurality of layer blocks each comprising one or more neural network layers, wherein each component is active for a respective subset of the plurality of time steps, and wherein each layer block is configured to, at each time step at which the layer block is active, receive an input generated at a previous time step and to process the input to generate a block output.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for parallel processing of video frames using neural networks. One of the methods includes receiving a video sequence comprising a respective video frame at each of a plurality of time steps; and processing the video sequence using a video processing neural network to generate a video processing output for the video sequence, wherein the video processing neural network includes a sequence of network components, wherein the network components comprise a plurality of layer blocks each comprising one or more neural network layers, wherein each component is active for a respective subset of the plurality of time steps, and wherein each layer block is configured to, at each time step at which the layer block is active, receive an input generated at a previous time step and to process the input to generate a block output.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a plurality of possible segmentations of an image. In one aspect, a method comprises: receiving a request to generate a plurality of possible segmentations of an image; sampling a plurality of latent variables from a latent space, wherein each latent variable is sampled from the latent space in accordance with a respective probability distribution over the latent space that is determined based on the image; generating a plurality of possible segmentations of the image, comprising, for each latent variable, processing the image and the latent variable using a segmentation neural network having a plurality of segmentation neural network parameters to generate the possible segmentation of the image; and providing the plurality of possible segmentations of the image in response to the request.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing video data. An example system receives video data and generates optical flow data. An image sequence from the video data is provided to a first 3D spatio-temporal convolutional neural network to process the image data in at least three space-time dimensions and to provide a first convolutional neural network output. A corresponding sequence of optical flow image frames is provided to a second 3D spatio-temporal convolutional neural network to process the optical flow data in at least three space-time dimensions and to provide a second convolutional neural network output. The first and second convolutional neural network outputs are combined to provide a system output.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying actions in a video. One of the methods obtaining a feature representation of a video clip; obtaining data specifying a plurality of candidate agent bounding boxes in the key video frame; and for each candidate agent bounding box: processing the feature representation through an action transformer neural network.

Abstract: A computer-implemented neural network system for decomposing input video data. A video data input receives a sequence of video image frames. The sequence is encoded, using a 3D spatio-temporal encoder neural network, into a set of latent variables representing a compressed version of the sequence. A 3D spatio-temporal decoder neural network processes the set of latent variables to generate two or more sets of decomposed video data; these may be stored, communicated, and/or made available to a user interface. Input video including undesired features such as reflections, shadows, and occlusions may thus be decomposed into two or more video sequences, one in which the undesired features are suppressed, and another containing the undesired features.

Abstract: An encoder neural network is described which can encode a data item, such as a frame of a video, to form a respective encoded data item. Data items of a first data sequence are associated with respective data items of a second sequence, by determining which of the encoded data items of the second sequence is closest to the encoded data item produced from each data item of the first sequence. Thus, the two data sequences are aligned. The encoder neural network is trained automatically using a training set of data sequences, by an iterative process of successively increasing cycle consistency between pairs of the data sequences.

Abstract: There is described a neural network system for determining a similarity measure between a query data item and a set of support data items. The neural network system is implemented by one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations comprising receiving the query data item and obtaining a support set of one or more support data items comprising a support key embedding and a support value embedding for each respective support data item in the support set. The operations further comprise generating a query key embedding for the query data item using a key embedding neural network subsystem configured to process a data item to generate a key embedding.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using an image processing neural network system that includes a spatial transformer module. One of the methods includes receiving an input feature map derived from the one or more input images, and applying a spatial transformation to the input feature map to generate a transformed feature map, comprising: processing the input feature map to generate spatial transformation parameters for the spatial transformation, and sampling from the input feature map in accordance with the spatial transformation parameters to generate the transformed feature map.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for parallel processing of video frames using neural networks. One of the methods includes receiving a video sequence comprising a respective video frame at each of a plurality of time steps; and processing the video sequence using a video processing neural network to generate a video processing output for the video sequence, wherein the video processing neural network includes a sequence of network components, wherein the network components comprise a plurality of layer blocks each comprising one or more neural network layers, wherein each component is active for a respective subset of the plurality of time steps, and wherein each layer block is configured to, at each time step at which the layer block is active, receive an input generated at a previous time step and to process the input to generate a block output.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing video data. An example system receives video data and generates optical flow data. An image sequence from the video data is provided to a first 3D spatio-temporal convolutional neural network to process the image data in at least three space-time dimensions and to provide a first convolutional neural network output. A corresponding sequence of optical flow image frames is provided to a second 3D spatio-temporal convolutional neural network to process the optical flow data in at least three space-time dimensions and to provide a second convolutional neural network output. The first and second convolutional neural network outputs are combined to provide a system output.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a plurality of possible segmentations of an image. In one aspect, a method comprises: receiving a request to generate a plurality of possible segmentations of an image; sampling a plurality of latent variables from a latent space, wherein each latent variable is sampled from the latent space in accordance with a respective probability distribution over the latent space that is determined based on the image; generating a plurality of possible segmentations of the image, comprising, for each latent variable, processing the image and the latent variable using a segmentation neural network having a plurality of segmentation neural network parameters to generate the possible segmentation of the image; and providing the plurality of possible segmentations of the image in response to the request.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing video data. An example system receives video data and generates optical flow data. An image sequence from the video data is provided to a first 3D spatio-temporal convolutional neural network to process the image data in at least three space-time dimensions and to provide a first convolutional neural network output. A corresponding sequence of optical flow image frames is provided to a second 3D spatio-temporal convolutional neural network to process the optical flow data in at least three space-time dimensions and to provide a second convolutional neural network output. The first and second convolutional neural network outputs are combined to provide a system output.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using an image processing neural network system that includes a spatial transformer module. One of the methods includes receiving an input feature map derived from the one or more input images, and applying a spatial transformation to the input feature map to generate a transformed feature map, comprising: processing the input feature map to generate spatial transformation parameters for the spatial transformation, and sampling from the input feature map in accordance with the spatial transformation parameters to generate the transformed feature map.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing video data. An example system receives video data and generates optical flow data. An image sequence from the video data is provided to a first 3D spatio-temporal convolutional neural network to process the image data in at least three space-time dimensions and to provide a first convolutional neural network output. A corresponding sequence of optical flow image frames is provided to a second 3D spatio-temporal convolutional neural network to process the optical flow data in at least three space-time dimensions and to provide a second convolutional neural network output. The first and second convolutional neural network outputs are combined to provide a system output.