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Found 6 papers, 0 papers with code
SQE: a Self Quality Evaluation Metric for Parameters Optimization in Multi-Object Tracking
We present a novel self quality evaluation metric SQE for parameters optimization in the challenging yet critical multi-object tracking task.
Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project
Although many methods perform well in single camera tracking, multi-camera tracking remains a challenging problem with less attention.
SOT for MOT
Another contribution is that we show with a deep learning based appearance model, it is easy to associate detections of the same object efficiently and also with high accuracy.
Exploiting Local Structures with the Kronecker Layer in Convolutional Networks
In this paper, we propose and study a technique to reduce the number of parameters and computation time in convolutional neural networks.
Incidental Scene Text Understanding: Recent Progresses on ICDAR 2015 Robust Reading Competition Challenge 4
Different from focused texts present in natural images, which are captured with user's intention and intervention, incidental texts usually exhibit much more diversity, variability and complexity, thus posing significant difficulties and challenges for scene text detection and recognition algorithms.
Compression of Fully-Connected Layer in Neural Network by Kronecker Product
In this paper we propose and study a technique to reduce the number of parameters and computation time in fully-connected layers of neural networks using Kronecker product, at a mild cost of the prediction quality.
