Incremental Cross-Modality Deep Learning for Pedestrian Recognition

At the selection stage of each active learning cycle, we mea- sure the relevance of labelling a particular image pair by rank- ing the aforementioned cross-modality prediction

Action units describe muscles or muscle groups in the face, are set or unset Uncertainty Modelling in Knowledge Engineering and Decision Making Downloaded from

• Train all pedestrian samples also using the pedestrian actions tags (cross/not cross) with the CNN as mentioned above for detection and action recognition based on the Joint

We presented in this paper a novel depth evaluation proto- col better suited for autonomous driving applications in road scenes as well as an evaluation of BTS and Monodepth2,

In this paper, we systematically depicted different cross- modality learning approaches of four methods based on Con- volutional Neural Networks for pedestrian recognition: (1)

We propose different training methods based on Cross-Modality deep learning of Convolutional Neural Networks (CNNs): (1) a correlated model, (2) an incremental model and, (3)

[1] Sebastien Villon, Marc Chaumont, Gerard Subsol, Sebastien Villeger, Thomas Claverie, David Mouillot, "Coral reef fish detection and recognition in underwater videos

Our best single model was a convolutional neural network trained to predict emotions from static frames using two large data sets, the Toronto Face Database and our own set of