Northeastern University

Amazon Technologies, Inc.

Songyao Jiang

Yun Fu

Jim Oommen Thomas

Junwu Luo

Bin Sun

Shuang Gao

Jingyi Zhang

A stylus provides input via a touchscreen comprising a touch sensor and a display. Latency between placement of a stylus tip and corresponding presentation of visual indicia on the display is reduced or eliminated by determining a predicted path of the stylus tip during a stroke. Visual indicia is presented on the display, based on the predicted path. Inputs from the touch sensor may include hover events associated with detection of the tip while not in contact with the touchscreen and touch events associated with presence of the tip on the touchscreen. A machine learning network may be trained to determine the predicted path. A portion of the network may be trained to accept dynamic-length sequences of events and generate fixed length sequences, reducing subsequent network complexity. A hand may be detected and used to determine the predicted path. The end of a stroke may be predicted, reducing overshoot.

Stylus input latency compensation system

Embodiments identify joints of a multi-limb body in an image. One such embodiment unifies depth of a plurality of multi-scale feature maps generated from an image of a multi-limb body to create a plurality of feature maps each having a same depth. In turn, for each of the plurality of feature maps having the same depth, an initial indication of one or more joints in the image is generated. The one or more joints are located at an interconnection of a limb to the multi-limb body or at an interconnection of a limb to another limb. To continue, a final indication of the one or more joints in the image is generated using each generated initial indication of the one or more joints.

Light-weight pose estimation network with multi-scale heatmap fusion

Embodiments provide methods and systems for image generation through use of adversarial networks. An embodiment trains an image generator comprising (i) a generator implemented with a first neural network configured to generate a fake image based on a target segmentation, (ii) a discriminator implemented with a second neural network configured to distinguish a real image from a fake image and output a discrimination result as a function thereof and (iii) a segmentor implemented with a third neural network configured to generate a segmentation from the fake image. The training includes (i) operating the generator to output the fake image to the discriminator and the segmentor and (ii) iteratively operating the generator, discriminator, and segmentor during a training period, whereby the discriminator and generator train in an adversarial relationship with each other and the generator and segmentor train in a collaborative relationship with each other.

Segmentation guided image generation with adversarial networks

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Everett, MA, United States of America

Songyao Jiang