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Found 15 papers, 7 papers with code
An Analysis of Sketched IRLS for Accelerated Sparse Residual Regression
This paper studies the problem of sparse residual regression, i. e., learning a linear model using a norm that favors solutions in which the residuals are sparsely distributed.
Rethinking Unsupervised Outlier Detection via Multiple Thresholding
In the realm of unsupervised image outlier detection, assigning outlier scores holds greater significance than its subsequent task: thresholding for predicting labels.
Locally varying distance transform for unsupervised visual anomaly detection
Unsupervised anomaly detection on image data is notoriously unstable.
Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly
Unsupervised Anomaly Detection with Specified Settings -- 10% anomaly
+3
Distance Based Image Classification: A solution to generative classification's conundrum?
We use the model to develop a classification scheme which suppresses the impact of noise while preserving semantic cues.
Relation Preserving Triplet Mining for Stabilising the Triplet Loss in Re-identification Systems
This leads us to introduce Relation Preserving Triplet Mining (RPTM), a feature-matching guided triplet mining scheme, that ensures that triplets will respect the natural subgroupings within an object ID.
Ranked #2 on
Vehicle Re-Identification
on VeRi-776
Shell Theory: A Statistical Model of Reality
The foundational assumption of machine learning is that the data under consideration is separable into classes; while intuitively reasonable, separability constraints have proven remarkably difficult to formulate mathematically.
Ranked #1 on
Unsupervised Anomaly Detection with Specified Settings -- 10% anomaly
on STL-10
(using extra training data)
Deep unsupervised anomaly detection
This paper proposes a novel method to detect anomalies in large datasets under a fully unsupervised setting.
Dual-SLAM: A framework for robust single camera navigation
As local pose estimation is ill-conditioned, local pose estimation failures happen regularly, making the overall SLAM system brittle.
Hierarchical Models: Intrinsic Separability in High Dimensions
It has long been noticed that high dimension data exhibits strange patterns.
Dimensionality's Blessing: Clustering Images by Underlying Distribution
Many high dimensional vector distances tend to a constant.
Image Matching: An Application-oriented Benchmark
To this end, we present a uniform benchmark with novel evaluation metrics and a large-scale dataset for evaluating the overall performance of image matching methods.
GMS: Grid-based Motion Statistics for Fast, Ultra-Robust Feature Correspondence
Incorporating smoothness constraints into feature matching is known to enable ultra-robust matching.
DAISY Filter Flow: A Generalized Discrete Approach to Dense Correspondences
Fundamental challenges to such an image or scene alignment task are often multifold, which render many existing techniques fall short of producing dense correspondences robustly and efficiently.
BING: Binarized Normed Gradients for Objectness Estimation at 300fps
Training a generic objectness measure to produce a small set of candidate object windows, has been shown to speed up the classical sliding window object detection paradigm.
ImageSpirit: Verbal Guided Image Parsing
This allows us to formulate the image parsing problem as one of jointly estimating per-pixel object and attribute labels from a set of training images.
