198 citations · 565 across the 20 of their papers we have counts for
21 papers
DALI: Domain Adaptive LiDAR Object Detection via Distribution-level and Instance-level Pseudo Label Denoising
Xiaohu Lu, Hayder Radha
Object detection using LiDAR point clouds relies on a large amount of human-annotated samples when training the underlying detectors' deep neural networks. However, generating 3D b…
ScAR: Scaling Adversarial Robustness for LiDAR Object Detection
Xiaohu Lu, Hayder Radha
The adversarial robustness of a model is its ability to resist adversarial attacks in the form of small perturbations to input data. Universal adversarial attack methods such as Fa…
Strong-Weak Integrated Semi-supervision for Unsupervised Single and Multi Target Domain Adaptation
Xiaohu Lu, Hayder Radha
Unsupervised domain adaptation (UDA) focuses on transferring knowledge learned in the labeled source domain to the unlabeled target domain. Despite significant progress that has be…
TransCAR: Transformer-based Camera-And-Radar Fusion for 3D Object Detection
Su Pang, Daniel Morris, Hayder Radha
Despite radar's popularity in the automotive industry, for fusion-based 3D object detection, most existing works focus on LiDAR and camera fusion. In this paper, we propose TransCA…
Integrated Multiscale Domain Adaptive YOLO
Mazin Hnewa, Hayder Radha
The area of domain adaptation has been instrumental in addressing the domain shift problem encountered by many applications. This problem arises due to the difference between the d…
Multiscale Domain Adaptive YOLO for Cross-Domain Object Detection
Mazin Hnewa, Hayder Radha
The area of domain adaptation has been instrumental in addressing the domain shift problem encountered by many applications. This problem arises due to the difference between the d…
Multi-Object Tracking using Poisson Multi-Bernoulli Mixture Filtering for Autonomous Vehicles
Su Pang, Hayder Radha
The ability of an autonomous vehicle to perform 3D tracking is essential for safe planing and navigation in cluttered environments. The main challenges for multi-object tracking (M…
CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection
Su Pang, Daniel Morris, Hayder Radha
There have been significant advances in neural networks for both 3D object detection using LiDAR and 2D object detection using video. However, it has been surprisingly difficult to…
Object Detection Under Rainy Conditions for Autonomous Vehicles: A Review of State-of-the-Art and Emerging Techniques
Mazin Hnewa, Hayder Radha
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, tra…
Design of Robust Path-Following Control System for Self-driving Vehicles Using Extended High-Gain Observer
Yasir K. Al-Nadawi, Hothaifa Al-Qassab, Daniel Kent +3
In the real-world, self-driving vehicles are required to achieve steering maneuvers in both uncontrolled and uncertain environments while maintaining high levels of safety and pass…
Semi-supervised Collaborative Ranking with Push at Top
Iman Barjasteh, Rana Forsati, Abdol-Hossein Esfahanian +1
Existing collaborative ranking based recommender systems tend to perform best when there is enough observed ratings for each user and the observation is made completely at random.…
New Guarantees for Blind Compressed Sensing
Mohammad Aghagolzadeh, Hayder Radha
Blind Compressed Sensing (BCS) is an extension of Compressed Sensing (CS) where the optimal sparsifying dictionary is assumed to be unknown and subject to estimation (in addition t…
On Hyperspectral Classification in the Compressed Domain
Mohammad Aghagolzadeh, Hayder Radha
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…
Dictionary and Image Recovery from Incomplete and Random Measurements
Mohammad Aghagolzadeh, Hayder Radha
This paper tackles algorithmic and theoretical aspects of dictionary learning from incomplete and random block-wise image measurements and the performance of the adaptive dictionar…
The Entropy of Attention and Popularity in YouTube Videos
Jonathan Scott Morgan, Iman Barjasteh, Cliff Lampe +1
The vast majority of YouTube videos never become popular, languishing in obscurity with few views, no likes, and no comments. We use information theoretical measures based on entro…
Single Image Super Resolution via Manifold Approximation
Chinh Dang, Hayder Radha
Image super-resolution remains an important research topic to overcome the limitations of physical acquisition systems, and to support the development of high resolution displays.…
Trending Videos: Measurement and Analysis
Iman Barjasteh, Ying Liu, Hayder Radha
Unlike popular videos, which would have already achieved high viewership numbers by the time they are declared popular, YouTube trending videos represent content that targets viewe…
Representative Selection for Big Data via Sparse Graph and Geodesic Grassmann Manifold Distance
Chinh Dang, Hayder Radha
This paper addresses the problem of identifying a very small subset of data points that belong to a significantly larger massive dataset (i.e., Big Data). The small number of selec…
RPCA-KFE: Key Frame Extraction for Consumer Video based Robust Principal Component Analysis
Chinh Dang, Abdolreza Moghadam, Hayder Radha
Key frame extraction algorithms consider the problem of selecting a subset of the most informative frames from a video to summarize its content.
A Tight Estimate for Decoding Error-Probability of LT Codes Using Kovalenko's Rank Distribution
Ki-Moon Lee, Hayder Radha, Beom-Jin Kim
A new approach for estimating the Decoding Error-Probability (DEP) of LT codes with dense rows is derived by using the conditional Kovalenko's rank distribution. The estimate by th…
Kovalenko's Full-Rank Limit and Overhead as Lower Bounds for Error-Performances of LDPC and LT Codes over Binary Erasure Channels
Ki-Moon Lee, Hayder Radha, Beom-Jin Kim
We present Kovalenko's full-rank limit as a tight lower bound for decoding error probability of LDPC codes and LT codes over BEC. From the limit, we derive a full-rank overhead as…