activity
20152021
most citedPeduncle Detection of Sweet Pepper for Autonomous Crop Harvesting - Combined Colour and 3D Information

124 citations · 286 across the 15 of their papers we have counts for

collaborators

29 papers

cs.CV2021

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…

cs.CV20216 cited

Going Deeper into Semi-supervised Person Re-identification

Olga Moskvyak, Frederic Maire, Feras Dayoub +1

Person re-identification is the challenging task of identifying a person across different camera views. Training a convolutional neural network (CNN) for this task requires annotat…

cs.CV20214 cited

Semi-supervised Keypoint Localization

Olga Moskvyak, Frederic Maire, Feras Dayoub +1

Knowledge about the locations of keypoints of an object in an image can assist in fine-grained classification and identification tasks, particularly for the case of objects that ex…

cs.RO2021103 cited

Semantics for Robotic Mapping, Perception and Interaction: A Survey

Sourav Garg, Niko Sünderhauf, Feras Dayoub +9

For robots to navigate and interact more richly with the world around them, they will likely require a deeper understanding of the world in which they operate. In robotics and rela…

cs.RO2021

Run-Time Monitoring of Machine Learning for Robotic Perception: A Survey of Emerging Trends

Quazi Marufur Rahman, Peter Corke, Feras Dayoub

As deep learning continues to dominate all state-of-the-art computer vision tasks, it is increasingly becoming an essential building block for robotic perception. This raises impor…

cs.CV20206 cited

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…