103 citations · 275 across the 25 of their papers we have counts for
11 papers · 1 filter
Addressing the Challenges of Open-World Object Detection
David Pershouse, Feras Dayoub, Dimity Miller +1
We address the challenging problem of open world object detection (OWOD), where object detectors must identify objects from known classes while also identifying and continually lea…
ParticleNeRF: A Particle-Based Encoding for Online Neural Radiance Fields
Jad Abou-Chakra, Feras Dayoub, Niko Sünderhauf
While existing Neural Radiance Fields (NeRFs) for dynamic scenes are offline methods with an emphasis on visual fidelity, our paper addresses the online use case that prioritises r…
Retrospectives on the Embodied AI Workshop
Matt Deitke, Dhruv Batra, Yonatan Bisk +36
We present a retrospective on the state of Embodied AI research. Our analysis focuses on 13 challenges presented at the Embodied AI Workshop at CVPR. These challenges are grouped i…
SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection
Samuel Wilson, Tobias Fischer, Feras Dayoub +2
We address the problem of out-of-distribution (OOD) detection for the task of object detection. We show that residual convolutional layers with batch normalisation produce Sensitiv…
FSNet: A Failure Detection Framework for Semantic Segmentation
Quazi Marufur Rahman, Niko Sünderhauf, Peter Corke +1
Semantic segmentation is an important task that helps autonomous vehicles understand their surroundings and navigate safely. During deployment, even the most mature segmentation mo…
Uncertainty for Identifying Open-Set Errors in Visual Object Detection
Dimity Miller, Niko Sünderhauf, Michael Milford +1
Deployed into an open world, object detectors are prone to open-set errors, false positive detections of object classes not present in the training dataset. We propose GMM-Det, a r…
SWA Object Detection
Haoyang Zhang, Ying Wang, Feras Dayoub +1
Do you want to improve 1.0 AP for your object detector without any inference cost and any change to your detector? Let us tell you such a recipe. It is surprisingly simple: train y…
Online Monitoring of Object Detection Performance During Deployment
Quazi Marufur Rahman, Niko Sünderhauf, Feras Dayoub
During deployment, an object detector is expected to operate at a similar performance level reported on its testing dataset. However, when deployed onboard mobile robots that opera…
Per-frame mAP Prediction for Continuous Performance Monitoring of Object Detection During Deployment
Quazi Marufur Rahman, Niko Sünderhauf, Feras Dayoub
Performance monitoring of object detection is crucial for safety-critical applications such as autonomous vehicles that operate under varying and complex environmental conditions.…
VarifocalNet: An IoU-aware Dense Object Detector
Haoyang Zhang, Ying Wang, Feras Dayoub +1
Accurately ranking the vast number of candidate detections is crucial for dense object detectors to achieve high performance. Prior work uses the classification score or a combinat…
Class Anchor Clustering: a Loss for Distance-based Open Set Recognition
Dimity Miller, Niko Sünderhauf, Michael Milford +1
In open set recognition, deep neural networks encounter object classes that were unknown during training. Existing open set classifiers distinguish between known and unknown classe…