Aircraft taxiing conflict is a threat to the safety of airport operations, mainly due to the human error in control command infor-mation.
In order to deploy deep models in a computationally efficient manner, model quantization approaches have been frequently used.
Previous stereo 3D object detection approaches cannot describe the complete shape details of the detected objects and often fails for the small objects.
We show the regularization effect of second-order momentum in Adam is crucial to revitalize the weights that are dead due to the activation saturation in BNNs.
The latter question motivates us to incorporate geometry knowledge with a new loss function based on a projective invariant.
Ranked #1 on Vehicle Pose Estimation on KITTI
GSNet utilizes a unique four-way feature extraction and fusion scheme and directly regresses 6DoF poses and shapes in a single forward pass.
Ranked #1 on 3D Car Instance Understanding on ApolloCar3D
End-to-end deep representation learning has achieved remarkable accuracy for monocular 3D human pose estimation, yet these models may fail for unseen poses with limited and fixed training data.
Ranked #11 on Weakly-supervised 3D Human Pose Estimation on Human3.6M
Residual representation learning simplifies the optimization problem of learning complex functions and has been widely used by traditional convolutional neural networks.
Ranked #2 on Age Estimation on CACD
Deep neural decision forest (NDF) achieved remarkable performance on various vision tasks via combining decision tree and deep representation learning.
We propose Deep Hierarchical Machine (DHM), a model inspired from the divide-and-conquer strategy while emphasizing representation learning ability and flexibility.