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Monocular 3D Object Detection Using Feature Map Transformation: Towards Learning Perspective-Invariant Scene Representations

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 …

Scalable simulation of rate-coded and spiking neural networks on shared memory systems

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 …

Forward Models in the Cerebellum using Reservoirs and Perturbation Learning

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 …

Feature Map Transformation for Fusion of Multi-Sensor Object Detection Networks for Autonomous Driving

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 …

Recurrent Spatial Attention for Facial Emotion Recognition

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 …

Fusion of Camera and Lidar Data for Object Detection using Neural Networks

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 …

Training a deep policy gradient-based neural network with asynchronous learners on a simulated robotic problem

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 …