Qualcomm Incorporated

Davide Abati

Amirhossein Habibian

Babak Ehteshami Bejnordi

Tijmen Pieter Frederik Blankevoort

Amir Ghodrati

Haitam Ben Yahia

Jakub Mikolaj Tomczak

Certain aspects of the present disclosure provide techniques and apparatus for processing video content using an artificial neural network. An example method generally includes receiving a video data stream including at least a first frame and a second frame. First features are extracted from the first frame using a teacher neural network. A difference between the first frame and the second frame is determined. Second features are extracted from at least the difference between the first frame and the second frame using a student neural network. A feature map for the second frame is generated based a summation of the first features and the second features. An inference is generated for at least the second frame of the video data stream based on the generated feature map for the second feature.

Video processing using delta distillation

Various aspects provide methods for learning, such as continual learning, that support task-incremental learning using a multi-head classification architecture. Various aspects may enable conditional computing to support multi-head classification. Various aspects provide methods for learning, such as continual learning, that support class-incremental learning using a single-head classification architecture. Various aspects may enable conditional computing to support single-head classification by predicting the task associated with a given test input and selecting an associated classification head based at least in part on the task prediction.

Conditional computation for continual learning

A processor-implemented method for processing a video includes receiving the video as an input at an artificial neural network (ANN). The video includes a sequence of frames. A set of features of a current frame of the video and a prior frame of the video are extracted. The set of features including a set of support features for a set of pixels of the prior frame to be aligned with a set of reference features of the current frame. A similarity between a support feature for each pixel in the set of pixels of the set of support features of the prior frame and a corresponding reference feature of the current frame is computed. An attention map is generated based on the similarity. An output including a reconstruction of the current frame is generated based on the attention map.

Efficient self-attention for video processing

A method for video processing via an artificial neural network includes receiving a video stream as an input at the artificial neural network. A residual is computed based on a difference between a first feature of a current frame of the video stream and a second feature of a previous frame of the video stream. One or more portions of the current frame of the video stream are processed based on the residual. Additionally, processing is skipped for one or more portions of the current frame of the video based on the residual.

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Amsterdam, Netherlands

Davide Abati