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Found 7 papers, 0 papers with code
Toward industrial use of continual learning : new metrics proposal for class incremental learning
In this paper, we investigate continual learning performance metrics used in class incremental learning strategies for continual learning (CL) using some high performing methods.
R-AGNO-RPN: A LIDAR-Camera Region Deep Network for Resolution-Agnostic Detection
In this paper, R-AGNO-RPN, a region proposal network built on fusion of 3D point clouds and RGB images is proposed for 3D object detection regardless of point cloud resolution.
Learning Sparse Filters in Deep Convolutional Neural Networks with a l1/l2 Pseudo-Norm
Moreover, the trade-off between the sparsity and the accuracy is compared to other loss regularization terms based on the l1 or l2 norm as well as the SSL, NISP and GAL methods and shows that our approach is outperforming them.
2D Wasserstein Loss for Robust Facial Landmark Detection
The recent performance of facial landmark detection has been significantly improved by using deep Convolutional Neural Networks (CNNs), especially the Heatmap Regression Models (HRMs).
SMC Faster R-CNN: Toward a scene-specialized multi-object detector
The suggested framework uses different strategies based on the SMC filter steps to approximate iteratively the target distribution as a set of samples in order to specialize the Faster R-CNN detector towards a target scene.
Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image
In this paper, we present a novel approach, called Deep MANTA (Deep Many-Tasks), for many-task vehicle analysis from a given image.
Ranked #2 on
Vehicle Pose Estimation
on KITTI Cars Hard
(using extra training data)
