In this paper we propose to use a feature map transformation network for the task of monocular 3D object detection. Given a monocular camera image, the transformation network encodes features of the scene in an abstract, perspective-invariant latent …
The size and complexity of the neural networks investigated in computational neuroscience are increasing, leading to a need for efficient neural simulation tools to support their development. Several neuro-simulators have been developed over the …
The cerebellum is thought to be able to learn forward models, which allow to predict the sensory consequences of planned movements and adapt behavior accordingly. Although classically considered as a feedforward structure learning in a supervised …
We present a general framework for fusing pre-trained multisensor object detection networks for perception in autonomous cars at an intermediate stage using perspective invariant features. Key innovation is an autoencoder-inspired Transformer module …
Automatic processing of emotion information through deep neural networks (DNN) can have great benefits for human-machine interaction. Vice versa, machine learning can profit from concepts known from human information processing (e.g., visual …
We present a novel architecture for intermediate fusion of Lidar and camera data for neural network-based object detection. Key component is a transformer module which learns a transformation of feature maps from one sensor space to another. This …
Recent advances in deep reinforcement learning methods have attracted a lot of attention, because of their ability to use raw signals such as video streams as inputs, instead of pre-processed state variables. However, the most popular methods …