3.5k citations · 10.5k across the 119 of their papers we have counts for
140 papers
Semantic Segmentation on 3D Point Clouds with High Density Variations
Ryan Faulkner, Luke Haub, Simon Ratcliffe +2
LiDAR scanning for surveying applications acquire measurements over wide areas and long distances, which produces large-scale 3D point clouds with significant local density variati…
Assessing Domain Gap for Continual Domain Adaptation in Object Detection
Anh-Dzung Doan, Bach Long Nguyen, Surabhi Gupta +3
To ensure reliable object detection in autonomous systems, the detector must be able to adapt to changes in appearance caused by environmental factors such as time of day, weather,…
ActiveRMAP: Radiance Field for Active Mapping And Planning
Huangying Zhan, Jiyang Zheng, Yi Xu +2
A high-quality 3D reconstruction of a scene from a collection of 2D images can be achieved through offline/online mapping methods. In this paper, we explore active mapping from the…
Predicting Topological Maps for Visual Navigation in Unexplored Environments
Huangying Zhan, Hamid Rezatofighi, Ian Reid
We propose a robotic learning system for autonomous exploration and navigation in unexplored environments. We are motivated by the idea that even an unseen environment may be famil…
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation
Yuyuan Liu, Choubo Ding, Yu Tian +4
Semantic segmentation models classify pixels into a set of known (``in-distribution'') visual classes. When deployed in an open world, the reliability of these models depends on th…
SC-DepthV3: Robust Self-supervised Monocular Depth Estimation for Dynamic Scenes
Libo Sun, Jia-Wang Bian, Huangying Zhan +3
Self-supervised monocular depth estimation has shown impressive results in static scenes. It relies on the multi-view consistency assumption for training networks, however, that is…
CropMix: Sampling a Rich Input Distribution via Multi-Scale Cropping
Junlin Han, Lars Petersson, Hongdong Li +1
We present a simple method, CropMix, for the purpose of producing a rich input distribution from the original dataset distribution. Unlike single random cropping, which may inadver…
You Only Cut Once: Boosting Data Augmentation with a Single Cut
Junlin Han, Pengfei Fang, Weihao Li +5
We present You Only Cut Once (YOCO) for performing data augmentations. YOCO cuts one image into two pieces and performs data augmentations individually within each piece. Applying…
PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labels
Filipe R. Cordeiro, Vasileios Belagiannis, Ian Reid +1
The most competitive noisy label learning methods rely on an unsupervised classification of clean and noisy samples, where samples classified as noisy are re-labelled and "MixMatch…
Weakly Supervised Training of Monocular 3D Object Detectors Using Wide Baseline Multi-view Traffic Camera Data
Matthew Howe, Ian Reid, Jamie Mackenzie
Accurate 7DoF prediction of vehicles at an intersection is an important task for assessing potential conflicts between road users. In principle, this could be achieved by a single…
ODAM: Object Detection, Association, and Mapping using Posed RGB Video
Kejie Li, Daniel DeTone, Steven Chen +6
Localizing objects and estimating their extent in 3D is an important step towards high-level 3D scene understanding, which has many applications in Augmented Reality and Robotics.…
JRDB-Act: A Large-scale Dataset for Spatio-temporal Action, Social Group and Activity Detection
Mahsa Ehsanpour, Fatemeh Saleh, Silvio Savarese +2
The availability of large-scale video action understanding datasets has facilitated advances in the interpretation of visual scenes containing people. However, learning to recognis…
Unsupervised Scale-consistent Depth Learning from Video
Jia-Wang Bian, Huangying Zhan, Naiyan Wang +5
We propose a monocular depth estimator SC-Depth, which requires only unlabelled videos for training and enables the scale-consistent prediction at inference time. Our contributions…
TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild
Vida Adeli, Mahsa Ehsanpour, Ian Reid +4
Joint forecasting of human trajectory and pose dynamics is a fundamental building block of various applications ranging from robotics and autonomous driving to surveillance systems…
Rotation Coordinate Descent for Fast Globally Optimal Rotation Averaging
Álvaro Parra, Shin-Fang Chng, Tat-Jun Chin +2
Under mild conditions on the noise level of the measurements, rotation averaging satisfies strong duality, which enables global solutions to be obtained via semidefinite programmin…
Looking Beyond Two Frames: End-to-End Multi-Object Tracking Using Spatial and Temporal Transformers
Tianyu Zhu, Markus Hiller, Mahsa Ehsanpour +4
Tracking a time-varying indefinite number of objects in a video sequence over time remains a challenge despite recent advances in the field. Most existing approaches are not able t…
ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised Learning
Ragav Sachdeva, Filipe R Cordeiro, Vasileios Belagiannis +2
We propose a new training algorithm, ScanMix, that explores semantic clustering and semi-supervised learning (SSL) to allow superior robustness to severe label noise and competitiv…
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment
Filipe R. Cordeiro, Ragav Sachdeva, Vasileios Belagiannis +2
Deep neural network models are robust to a limited amount of label noise, but their ability to memorise noisy labels in high noise rate problems is still an open issue. The most co…
Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification
Fengbei Liu, Yu Tian, Filipe R. Cordeiro +3
The training of deep learning models generally requires a large amount of annotated data for effective convergence and generalisation. However, obtaining high-quality annotations i…
DF-VO: What Should Be Learnt for Visual Odometry?
Huangying Zhan, Chamara Saroj Weerasekera, Jia-Wang Bian +2
Multi-view geometry-based methods dominate the last few decades in monocular Visual Odometry for their superior performance, while they have been vulnerable to dynamic and low-text…
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…
MOLTR: Multiple Object Localisation, Tracking, and Reconstruction from Monocular RGB Videos
Kejie Li, Hamid Rezatofighi, Ian Reid
Semantic aware reconstruction is more advantageous than geometric-only reconstruction for future robotic and AR/VR applications because it represents not only where things are, but…
EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels
Ragav Sachdeva, Filipe R. Cordeiro, Vasileios Belagiannis +2
The efficacy of deep learning depends on large-scale data sets that have been carefully curated with reliable data acquisition and annotation processes. However, acquiring such lar…
HM4: Hidden Markov Model with Memory Management for Visual Place Recognition
Anh-Dzung Doan, Yasir Latif, Tat-Jun Chin +1
Visual place recognition needs to be robust against appearance variability due to natural and man-made causes. Training data collection should thus be an ongoing process to allow c…