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Found 7 papers, 4 papers with code
BASE: Probably a Better Approach to Multi-Object Tracking
The field of visual object tracking is dominated by methods that combine simple tracking algorithms and ad hoc schemes.
Unsupervised Learning of Anomaly Detection from Contaminated Image Data using Simultaneous Encoder Training
In this work, we evaluate the effects of anomaly contaminations in the training data on state-of-the-art GAN-based anomaly detection methods.
Memory-Efficient Global Refinement of Decision-Tree Ensembles and its Application to Face Alignment
Ren et al. recently introduced a method for aggregating multiple decision trees into a strong predictor by interpreting a path taken by a sample down each tree as a binary vector and performing linear regression on top of these vectors stacked together.
Learning Local Descriptors by Optimizing the Keypoint-Correspondence Criterion: Applications to Face Matching, Learning from Unlabeled Videos and 3D-Shape Retrieval
Current best local descriptors are learned on a large dataset of matching and non-matching keypoint pairs.
Constructing Binary Descriptors with a Stochastic Hill Climbing Search
Binary descriptors of image patches provide processing speed advantages and require less storage than methods that encode the patch appearance with a vector of real numbers.
Fast Localization of Facial Landmark Points
We describe a method that can accurately estimate the positions of relevant facial landmarks in real-time even on hardware with limited processing power, such as mobile devices.
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
We describe a method for visual object detection based on an ensemble of optimized decision trees organized in a cascade of rejectors.
