4 accepted papers
3D detection of vehicles is an essential component for autonomous driving applications. Nevertheless, collecting the supervised training data for learning 3D vehicle detectors would be costly (e.g. utilization of expensive LiDAR sensors) and labor-intensive (for human annotation). In comparison to 3
Neuroscience studies have revealed neural processes involving rhythm perception, suggesting that brain encodes rhythmic sounds and embeds information in neural activity. In this work, we investigate how to extract rhythmic information embedded in the brain responses and to decode the original audio…
Faces manifest large variations in many aspects, such as identity, expression, pose, and face styling. Therefore, it is a great challenge to disentangle and extract these characteristics from facial images, especially in an unsupervised manner. In this work, we introduce cycle-consistency in facial…
Recent work has shown that depth estimation from a stereo pair of images can be formulated as a supervised learning task to be resolved with convolutional neural networks (CNNs). However, current architectures rely on patch-based Siamese networks, lacking the means to exploit context information for…