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Found 10 papers, 0 papers with code
Structured learning of metric ensembles with application to person re-identification
However, seeking an optimal combination of visual features which is generic yet adaptive to different benchmarks is a unsoved problem, and metric learning models easily get over-fitted due to the scarcity of training data in person re-identification.
Fast detection of multiple objects in traffic scenes with a common detection framework
The proposed framework consists of a dense feature extractor and detectors of three important classes.
Learning to rank in person re-identification with metric ensembles
We propose an effective structured learning based approach to the problem of person re-identification which outperforms the current state-of-the-art on most benchmark data sets evaluated.
Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning
Experimental results on both synthetic and real-world data sets demonstrate the effectiveness of our approach, and we show that it is possible to train state-of-the-art pedestrian detectors using the proposed structured ensemble learning method with spatially pooled features.
Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features
The combination of these factors leads to a pedestrian detector which outperforms all competitors on all of the standard benchmark datasets.
Large-margin Learning of Compact Binary Image Encodings
The use of high-dimensional features has become a normal practice in many computer vision applications.
Efficient pedestrian detection by directly optimize the partial area under the ROC curve
We propose a novel ensemble learning method which achieves a maximal detection rate at a user-defined range of false positive rates by directly optimizing the partial AUC using structured learning.
A scalable stage-wise approach to large-margin multi-class loss based boosting
In this work, we propose a scalable and simple stage-wise multi-class boosting method, which also directly maximizes the multi-class margin.
Asymmetric Pruning for Learning Cascade Detectors
In this work, we present a new approach to train an effective node classifier in a cascade detector.
Face Detection with Effective Feature Extraction
There is an abundant literature on face detection due to its important role in many vision applications.
