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Found 7 papers, 1 papers with code
Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research
We present Kaolin, a PyTorch library aiming to accelerate 3D deep learning research.
Residual Parameter Transfer for Deep Domain Adaptation
The goal of Deep Domain Adaptation is to make it possible to use Deep Nets trained in one domain where there is enough annotated training data in another where there is little or none.
Flight Dynamics-based Recovery of a UAV Trajectory using Ground Cameras
Our main contribution is a new bundle adjustment procedure which in addition to optimizing the camera poses, regularizes the point trajectory using a prior based on motion dynamics (or specifically flight dynamics).
Beyond Sharing Weights for Deep Domain Adaptation
To this end, we introduce a two-stream architecture, where one operates in the source domain and the other in the target domain.
Direct Prediction of 3D Body Poses from Motion Compensated Sequences
We propose an efficient approach to exploiting motion information from consecutive frames of a video sequence to recover the 3D pose of people.
On Rendering Synthetic Images for Training an Object Detector
We propose a novel approach to synthesizing images that are effective for training object detectors.
Flying Objects Detection from a Single Moving Camera
We propose an approach to detect flying objects such as UAVs and aircrafts when they occupy a small portion of the field of view, possibly moving against complex backgrounds, and are filmed by a camera that itself moves.
