Search Results for author:
Found 14 papers, 3 papers with code
ScAR: Scaling Adversarial Robustness for LiDAR Object Detection
Universal adversarial attack methods such as Fast Sign Gradient Method (FSGM) and Projected Gradient Descend (PGD) are popular for LiDAR object detection, but they are often deficient compared to task-specific adversarial attacks.
Strong-Weak Integrated Semi-supervision for Unsupervised Single and Multi Target Domain Adaptation
The extension from single-target to multi-target domain adaptation is accomplished by exploring the class-wise distance relationship between domains and replacing the strong representative set with much stronger samples from peer domains via peer scaffolding.
TransCAR: Transformer-based Camera-And-Radar Fusion for 3D Object Detection
The second module learns radar features from multiple radar scans and then applies transformer decoder to learn the interactions between radar features and vision-updated queries.
Integrated Multiscale Domain Adaptive YOLO
In this paper, we introduce a novel MultiScale Domain Adaptive YOLO (MS-DAYOLO) framework that employs multiple domain adaptation paths and corresponding domain classifiers at different scales of the recently introduced YOLOv4 object detector.
Multiscale Domain Adaptive YOLO for Cross-Domain Object Detection
The area of domain adaptation has been instrumental in addressing the domain shift problem encountered by many applications.
Multi-Object Tracking using Poisson Multi-Bernoulli Mixture Filtering for Autonomous Vehicles
The ability of an autonomous vehicle to perform 3D tracking is essential for safe planing and navigation in cluttered environments.
CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection
There have been significant advances in neural networks for both 3D object detection using LiDAR and 2D object detection using video.
Object Detection Under Rainy Conditions for Autonomous Vehicles: A Review of State-of-the-Art and Emerging Techniques
Advanced automotive active-safety systems, in general, and autonomous vehicles, in particular, rely heavily on visual data to classify and localize objects such as pedestrians, traffic signs and lights, and other nearby cars, to assist the corresponding vehicles maneuver safely in their environments.
Semi-supervised Collaborative Ranking with Push at Top
We propose a semi-supervised collaborative ranking model, dubbed \texttt{S$^2$COR}, to improve the quality of cold-start recommendation.
Dictionary and Image Recovery from Incomplete and Random Measurements
In particular, we rely on the spatial diversity of compressive measurements to guarantee that the solution is unique with a high probability.
On Hyperspectral Classification in the Compressed Domain
In this paper, we study the problem of hyperspectral pixel classification based on the recently proposed architectures for compressive whisk-broom hyperspectral imagers without the need to reconstruct the complete data cube.
Single Image Super Resolution via Manifold Approximation
Third, and to further achieve lower computational complexity, we perform hierarchical clustering on the optimal subset based on Grassmann manifold distances.
Representative Selection for Big Data via Sparse Graph and Geodesic Grassmann Manifold Distance
We refer to the proposed representative selection framework as a Sparse Graph and Grassmann Manifold (SGGM) based approach.
RPCA-KFE: Key Frame Extraction for Consumer Video based Robust Principal Component Analysis
Key frame extraction algorithms consider the problem of selecting a subset of the most informative frames from a video to summarize its content.
