Naver Corporation

Ioannis Kalantidis

Philippe Weinzaepfel

Diane Larlus

Gregory Rogez

Romain Bregier

Thibault Groueix

Mert Bulent Sariyildiz

Thomas Lucas

Noé Pion

Fabien Baradel

A training system includes: a neural network model configured to determine three-dimensional coordinates of joints, respectively, representing poses of animals in images, where the neural network model is trained using a first training dataset including: images including animals; and coordinates of joints of the animals in the images, respectively; and a training module configured to, after the training of the neural network model using the first training dataset, train the neural network model using a second training dataset including motion capture data, where the motion capture data does not include images of animals and includes measured coordinates at points, respectively, on animals.

Pose estimation systems and methods trained using motion capture data

Am image retrieval system includes: a neural network (NN) module configured to generate local features based on an input image; an iterative attention module configured to, via T iterations, generate an ordered set of super features in the input image based on the local features, where T is an integer greater than 1; and a selection module configured to select a second image from a plurality of images in an image database based on the second image having a second ordered set of super features that most closely match the ordered set of super features in the input image, where the super features in the set of super features do not include redundant local features of the input image.

Systems and methods for image retrieval using super features

A training system includes: an encoder module configured to receive a query image and to generate a first vector representative of one or more features in the query image using an encoder; a mixing module configured to generate a second vector by mixing a third vector, representative of one or more features in a second image that is classified as a negative relative to the query image, with a fourth vector; and an adjustment module configured to train the encoder by selectively adjusting one or more parameters of the encoder based on the first vector and the second vector.

Unsupervised contrastive learning of visual representations using negative mixing

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Grenoble, France

Ioannis Kalantidis