Search Results for author:
Found 10 papers, 1 papers with code
A Two-Block RNN-based Trajectory Prediction from Incomplete Trajectory
However, most works rely on a key assumption that each video is successfully preprocessed by detection and tracking algorithms and the complete observed trajectory is always available.
Data-Efficient Framework for Real-world Multiple Sound Source 2D Localization
Moreover, learning for different microphone array layouts makes the task more complicated due to the infinite number of possible layouts.
Learning Multiple Sound Source 2D Localization
In this paper, we propose novel deep learning based algorithms for multiple sound source localization.
Ensemble of Discriminators for Domain Adaptation in Multiple Sound Source 2D Localization
This paper introduces an ensemble of discriminators that improves the accuracy of a domain adaptation technique for the localization of multiple sound sources.
On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective
In this paper, we first prove that the focal loss is classification-calibrated, i. e., its minimizer surely yields the Bayes-optimal classifier and thus the use of the focal loss in classification can be theoretically justified.
Unifying Heterogeneous Classifiers with Distillation
In this paper, we study the problem of unifying knowledge from a set of classifiers with different architectures and target classes into a single classifier, given only a generic set of unlabelled data.
Inverse Composition Discriminative Optimization for Point Cloud Registration
Rigid Point Cloud Registration (PCReg) refers to the problem of finding the rigid transformation between two sets of point clouds.
Discriminative Optimization: Theory and Applications to Computer Vision Problems
This approach faces two main challenges: (i) designing a cost function with a local optimum at an acceptable solution, and (ii) developing an efficient numerical method to search for one (or multiple) of these local optima.
Discriminative Optimization: Theory and Applications to Point Cloud Registration
This approach faces two main challenges: (1) designing a cost function with a local optimum at an acceptable solution, and (2) developing an efficient numerical method to search for one (or multiple) of these local optima.
Motion From Structure (MfS): Searching for 3D Objects in Cluttered Point Trajectories
Object detection has been a long standing problem in computer vision, and state-of-the-art approaches rely on the use of sophisticated features and/or classifiers.
