Qualcomm Incorporated

Yizhe Zhang

Fatih Murat Porikli

Yang Yang

Amir Said

Taco Sebastiaan Cohen

Reza Pourreza

Yinhao Zhu

Hoang Cong Minh Le

Amirhossein Habibian

Shubhankar Mangesh Borse

Techniques are provided for determining consistency measures for image segmentation. For instance, a system can determine a first segmentation feature associated with a first segmentation mask of a first image frame. The system can determine a second segmentation feature associated with a second segmentation mask of a second image frame. The second segmentation feature corresponds to the first segmentation feature. The system can determine a first image feature of the first image frame that corresponds to the first segmentation feature and a second image feature of the second image frame that corresponds to the second segmentation feature. The system can determine a first similarity measurement between the first image feature and the second image feature. The system can further determine a temporal consistency measurement associated with the first image frame and the second image frame based at least in part on the first similarity measurement.

Consistency measure for image segmentation processes

A method for processing a video includes receiving a video as an input at a first layer of an artificial neural network (ANN). A first frame of the video is processed to generate a first label. Thereafter, the artificial neural network is updated based on the first label. The updating is performed while concurrently processing a second frame of the video. In doing so, the temporal inconsistency between labels is reduced.

Efficient test-time adaptation for improved temporal consistency in video processing

A processor-implemented method for image compression using an artificial neural network (ANN) includes receiving, at an encoder of the ANN, an image and a spatial segmentation map corresponding to the image. The spatial segmentation map indicates one or more regions of interest. The encoder compresses the image according to a controllable spatial bit allocation. The controllable spatial bit allocation is based on a learned quantization bin size.

Neural image compression with controllable spatial bit allocation

Systems, methods, and non-transitory media are provided for providing spatiotemporal recycling networks (e.g., for video segmentation). For example, a method can include obtaining video data including a current frame and one or more reference frames. The method can include determining, based on a comparison of the current frame and the one or more reference frames, a difference between the current frame and the one or more reference frames. Based on the difference being below a threshold, the method can include performing semantic segmentation of the current frame using a first neural network. The semantic segmentation can be performed based on higher-spatial resolution features extracted from the current frame by the first neural network and lower-resolution features extracted from the one or more reference frames by a second neural network. The first neural network has a smaller structure and/or a lower processing cost than the second neural network.

Spatiotemporal recycling network

A method of image compression includes receiving an image. Multiple quantized latent representations are generated to represent features of the image. Each of the quantized latent representations has a different resolution and is generated at staggered timings. Each of the later generated quantized latent representations is conditioned on each of the prior generated quantized latent representations. The multiple quantized latent representations are decoded to reconstruct the image.

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Yizhe Zhang