Search Results for author:
Found 11 papers, 7 papers with code
Aircraft Ground Taxiing Deduction and Conflict Early Warning Method Based on Control Command Information
Aircraft taxiing conflict is a threat to the safety of airport operations, mainly due to the human error in control command infor-mation.
SDQ: Stochastic Differentiable Quantization with Mixed Precision
In order to deploy deep models in a computationally efficient manner, model quantization approaches have been frequently used.
Stereo Neural Vernier Caliper
We propose a new object-centric framework for learning-based stereo 3D object detection.
Joint stereo 3D object detection and implicit surface reconstruction
Previous stereo 3D object detection approaches cannot describe the complete shape details of the detected objects and often fails for the small objects.
How Do Adam and Training Strategies Help BNNs Optimization?
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.
Exploring intermediate representation for monocular vehicle pose estimation
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: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision
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
Cascaded deep monocular 3D human pose estimation with evolutionary training data
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
Facial age estimation by deep residual decision making
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
Visualizing the decision-making process in deep neural decision forest
Deep neural decision forest (NDF) achieved remarkable performance on various vision tasks via combining decision tree and deep representation learning.
Deep Hierarchical Machine: a Flexible Divide-and-Conquer Architecture
We propose Deep Hierarchical Machine (DHM), a model inspired from the divide-and-conquer strategy while emphasizing representation learning ability and flexibility.
