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Found 9 papers, 0 papers with code
Learning Neural Volumetric Pose Features for Camera Localization
We introduce a novel neural volumetric pose feature, termed PoseMap, designed to enhance camera localization by encapsulating the information between images and the associated camera poses.
PCDNF: Revisiting Learning-based Point Cloud Denoising via Joint Normal Filtering
Motivated by the essential interplay between point cloud denoising and normal filtering, we revisit point cloud denoising from a multitask perspective, and propose an end-to-end network, named PCDNF, to denoise point clouds via joint normal filtering.
LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph Construction
Experiments on five benchmarks validate the effectiveness of LogLG for detecting anomalies on unlabeled log data and demonstrate that LogLG, as the state-of-the-art weakly supervised method, achieves significant performance improvements compared to existing methods.
Towards Understanding Catastrophic Overfitting in Fast Adversarial Training
After adversarial training was proposed, a series of works focus on improving the compunational efficiency of adversarial training for deep neural networks (DNNs).
Dissecting Local Properties of Adversarial Examples
Adversarial examples have attracted significant attention over the years, yet a sufficient understanding is in lack, especially when analyzing their performances in combination with adversarial training.
Clustering Activity-Travel Behavior Time Series using Topological Data Analysis
Over the last few years, traffic data has been exploding and the transportation discipline has entered the era of big data.
A Scalable Heuristic for Fastest-Path Computation on Very Large Road Maps
Fastest-path queries between two points in a very large road map is an increasingly important primitive in modern transportation and navigation systems, thus very efficient computation of these paths is critical for system performance and throughput.
Efficient Fastest-Path Computations in Road Maps
We introduce a simple, but very effective, heuristic function based on a small number of values assigned to each graph vertex.
A Self-Balanced Min-Cut Algorithm for Image Clustering
In the new method, a self-balanced min-cut model is proposed in which the Exclusive Lasso is implicitly introduced as a balance regularizer in order to produce balanced partition.
