Eth Zurich

Toyota Jidosha Kabushiki Kaisha

Toyota Motor Europe

Eth Zurich the Swiss Federal Institute of Technology Zurich

Dengxin Dai

Luc Van Gool

Wim Abbeloos

Christos Sakaridis

Nicolas Vignard

Stamatios Georgoulis

Hiroaki Shimizu

Arun Balajee Vasudevan

Vaishakh Patil

Anton Obukhov

Maksim Rakhuba

Menelaos Kanakis

Martin Hahner

Yu Fisher

Ozan Unal

Niclas VÖDISCH

Ke Li

A neural network, a system using this neural network and a method for training a neural network to output a description of the environment in the vicinity of at least one sound acquisition device on the basis of an audio signal acquired by the sound acquisition device, the method including: obtaining audio and image training signals of a scene showing an environment with objects generating sounds, obtaining a target description of the environment seen on the image training signal, inputting the audio training signal to the neural network so that the neural network outputs a training description of the environment, and comparing the target description of the environment with the training description of the environment.

Method for training a neural network to describe an environment on the basis of an audio signal, and the corresponding neural network

A system and a method for training a semantic segmentation model includes obtaining a plurality of sets of images each having an index z for visibility, iteratively training the model. Iteratively training the model includes (a) for each z above 1, obtaining preliminary semantic segmentation labels for each image of the set of images of index z−1 by applying the model to each image of the set of images of index z−1, (b) processing each preliminary semantic segmentation labels using semantic segmentation labels obtained using the model on a selected image of index 1, and obtaining processed semantic segmentation labels, (c) training the model using the set of images of index z−1 and the associated processed semantic segmentation labels, and (d) performing steps (a) to (c) for z+1.

System and method for training a model to perform semantic segmentation on low visibility images using high visibility images having a close camera view

A depth maps prediction system comprising a neural network () configured to receive images (I) of a scene at successive time steps (t−1, t, t+1, . . . ) and comprising three sub-networks: an encoder (), a ConvLSTM () and a decoder (). The neural network () is configured so that at each time step: a) the encoder sub-network () processes an image (I) and outputs a low resolution initial image representation (X); b) the CONVLSTM sub-network () processes the initial image representation (X), values for a previous time step (t−1) of an internal state (C(t−1)) and of an LSTM hidden variable data (H(t−1)) of the ConvLSTM sub-network, and outputs updated values of the internal state (C(t)) and of the LSTM hidden variable data (H(t)); andc) the decoder sub-network () inputs the LSTM output data (LOD) and generates a predicted dense depth map (D″) for the inputted image (I).

Depth maps prediction system and training method for such a system

A system and a method for training a model to be used for semantic segmentation of images, comprising:

Method and a system training a model to perform semantic segmentation on foggy images

Growing community of inventors

Zurich, Switzerland

Dengxin Dai