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Context-empowered Visual Attention Prediction in Pedestrian Scenarios
In this paper, we present Context-SalNET, a novel encoder-decoder architecture that explicitly addresses three key challenges of visual attention prediction in pedestrians: First, Context-SalNET explicitly models the context factors urgency and safety preference in the latent space of the encoder-decoder model.
CamLessMonoDepth: Monocular Depth Estimation with Unknown Camera Parameters
Recent advances in monocular depth estimation have shown that gaining such knowledge from a single camera input is possible by training deep neural networks to predict inverse depth and pose, without the necessity of ground truth data.
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