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Found 5 papers, 5 papers with code
Improved Hard Example Mining Approach for Single Shot Object Detectors
Hard example mining methods generally improve the performance of the object detectors, which suffer from imbalanced training sets.
Effect of Parameter Optimization on Classical and Learning-based Image Matching Methods
After a fair comparison, the experimental results on HPatches dataset reveal that the performance gap between classical and learning-based methods is not that significant.
DFM: A Performance Baseline for Deep Feature Matching
A novel image matching method is proposed that utilizes learned features extracted by an off-the-shelf deep neural network to obtain a promising performance.
Semi-Automatic Annotation For Visual Object Tracking
For detection, we use an off-the-shelf object detector which is trained iteratively with the annotations generated by the proposed method, and we perform object detection on each frame independently.
Effect of Annotation Errors on Drone Detection with YOLOv3
Following the recent advances in deep networks, object detection and tracking algorithms with deep learning backbones have been improved significantly; however, this rapid development resulted in the necessity of large amounts of annotated labels.
