134 citations · 725 across the 66 of their papers we have counts for
13 papers · 1 filter
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…
The Robotic Vision Scene Understanding Challenge
David Hall, Ben Talbot, Suman Raj Bista +4
Being able to explore an environment and understand the location and type of all objects therein is important for indoor robotic platforms that must interact closely with humans. H…
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.…
Keypoint-Aligned Embeddings for Image Retrieval and Re-identification
Olga Moskvyak, Frederic Maire, Feras Dayoub +1
Learning embeddings that are invariant to the pose of the object is crucial in visual image retrieval and re-identification. The existing approaches for person, vehicle, or animal…
BenchBot: Evaluating Robotics Research in Photorealistic 3D Simulation and on Real Robots
Ben Talbot, David Hall, Haoyang Zhang +4
We introduce BenchBot, a novel software suite for benchmarking the performance of robotics research across both photorealistic 3D simulations and real robot platforms. BenchBot pro…
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…
Control of the Final-Phase of Closed-Loop Visual Grasping using Image-Based Visual Servoing
Jesse Haviland, Feras Dayoub, Peter Corke
This paper considers the final approach phase of visual-closed-loop grasping where the RGB-D camera is no longer able to provide valid depth information. Many current robotic grasp…
Learning landmark guided embeddings for animal re-identification
Olga Moskvyak, Frederic Maire, Feras Dayoub +1
Re-identification of individual animals in images can be ambiguous due to subtle variations in body markings between different individuals and no constraints on the poses of animal…
Robot Navigation in Unseen Spaces using an Abstract Map
Ben Talbot, Feras Dayoub, Peter Corke +1
Human navigation in built environments depends on symbolic spatial information which has unrealised potential to enhance robot navigation capabilities. Information sources such as…
What can robotics research learn from computer vision research?
Peter Corke, Feras Dayoub, David Hall +2
The computer vision and robotics research communities are each strong. However progress in computer vision has become turbo-charged in recent years due to big data, GPU computing,…
Close-Proximity Underwater Terrain Mapping Using Learning-based Coarse Range Estimation
Bilal Arain, Feras Dayoub, Paul Rigby +1
This paper presents a novel approach to underwater terrain mapping for Autonomous Underwater Vehicles (AUVs) operating in close proximity to complex 3D environments. The proposed m…