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Found 37 papers, 13 papers with code
PoIFusion: Multi-Modal 3D Object Detection via Fusion at Points of Interest
In this work, we present PoIFusion, a simple yet effective multi-modal 3D object detection framework to fuse the information of RGB images and LiDAR point clouds at the point of interest (abbreviated as PoI).
Wasserstein Distance-based Expansion of Low-Density Latent Regions for Unknown Class Detection
We present a novel approach that effectively identifies unknown objects by distinguishing between high and low-density regions in latent space.
Segment Beyond View: Handling Partially Missing Modality for Audio-Visual Semantic Segmentation
The model consists of a vision teacher utilising panoramic information, an auditory teacher with 8-channel audio, and an audio-visual student that takes views with limited FoV and binaural audio as input and produce semantic segmentation for objects outside FoV.
Improving Online Source-free Domain Adaptation for Object Detection by Unsupervised Data Acquisition
Effective object detection in mobile robots is challenged by deployment in diverse and unfamiliar environments.
Federated Neural Radiance Fields
The ability of neural radiance fields or NeRFs to conduct accurate 3D modelling has motivated application of the technique to scene representation.
Addressing the Challenges of Open-World Object Detection
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 learning to detect novel objects.
Predicting Class Distribution Shift for Reliable Domain Adaptive Object Detection
Motivated by this, we propose a framework for explicitly addressing class distribution shift to improve pseudo-label reliability in self-training.
ParticleNeRF: A Particle-Based Encoding for Online Neural Radiance Fields
ParticleNeRF is the first online dynamic NeRF and achieves fast adaptability with better visual fidelity than brute-force online InstantNGP and other baseline approaches on dynamic scenes with online constraints.
SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection
We address the problem of out-of-distribution (OOD) detection for the task of object detection.
Hyperdimensional Feature Fusion for Out-Of-Distribution Detection
We introduce powerful ideas from Hyperdimensional Computing into the challenging field of Out-of-Distribution (OOD) detection.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
FSNet: A Failure Detection Framework for Semantic Segmentation
Semantic segmentation is an important task that helps autonomous vehicles understand their surroundings and navigate safely.
Going Deeper into Semi-supervised Person Re-identification
To reduce the need for labeled data, we focus on a semi-supervised approach that requires only a subset of the training data to be labeled.
Uncertainty for Identifying Open-Set Errors in Visual Object Detection
We also introduce a methodology for converting existing object detection datasets into specific open-set datasets to evaluate open-set performance in object detection.
Semi-supervised Keypoint Localization
Keypoint representations are learnt with a semantic keypoint consistency constraint that forces the keypoint detection network to learn similar features for the same keypoint across the dataset.
Semantics for Robotic Mapping, Perception and Interaction: A Survey
In robotics and related research fields, the study of understanding is often referred to as semantics, which dictates what does the world "mean" to a robot, and is strongly tied to the question of how to represent that meaning.
SWA Object Detection
In this technique report, we systematically investigate the effects of applying SWA to object detection as well as instance segmentation.
Online Monitoring of Object Detection Performance During Deployment
During deployment, an object detector is expected to operate at a similar performance level reported on its testing dataset.
Per-frame mAP Prediction for Continuous Performance Monitoring of Object Detection During Deployment
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
In this paper, we propose to learn an Iou-aware Classification Score (IACS) as a joint representation of object presence confidence and localization accuracy.
Ranked #24 on
Object Detection
on COCO-O
Keypoint-Aligned Embeddings for Image Retrieval and Re-identification
Learning embeddings that are invariant to the pose of the object is crucial in visual image retrieval and re-identification.
BenchBot: Evaluating Robotics Research in Photorealistic 3D Simulation and on Real Robots
We introduce BenchBot, a novel software suite for benchmarking the performance of robotics research across both photorealistic 3D simulations and real robot platforms.
Robotics
Class Anchor Clustering: a Loss for Distance-based Open Set Recognition
We also show that our anchored class centres achieve higher open set performance than learnt class centres, particularly on object-based datasets and large numbers of training classes.
Robot Navigation in Unseen Spaces using an Abstract Map
Symbolic navigation performance of humans and a robot is evaluated in a real-world built environment.
Control of the Final-Phase of Closed-Loop Visual Grasping using Image-Based Visual Servoing
IBVS robustly moves the camera to a goal pose defined implicitly in terms of an image-plane feature configuration.
Learning landmark guided embeddings for animal re-identification
Our method outperforms the same model without body landmarks input by 26% and 18% on the synthetic and the real datasets respectively.
What can robotics research learn from computer vision research?
The computer vision and robotics research communities are each strong.
The Probabilistic Object Detection Challenge
We introduce a new challenge for computer and robotic vision, the first ACRV Robotic Vision Challenge, Probabilistic Object Detection.
Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors
The proposed method raises an alarm when it discovers a failure by the object detector to detect a traffic sign.
Robust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant Embeddings
We present a novel system for visual re-identification based on unique natural markings that is robust to occlusions, viewpoint and illumination changes.
Probabilistic Object Detection: Definition and Evaluation
We introduce Probabilistic Object Detection, the task of detecting objects in images and accurately quantifying the spatial and semantic uncertainties of the detections.
Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection
There has been a recent emergence of sampling-based techniques for estimating epistemic uncertainty in deep neural networks.
A Rapidly Deployable Classification System using Visual Data for the Application of Precision Weed Management
In this work, we obviate this assumption and introduce a rapidly deployable approach able to operate on any field without any weed species assumptions prior to deployment.
Dropout Sampling for Robust Object Detection in Open-Set Conditions
Dropout Variational Inference, or Dropout Sampling, has been recently proposed as an approximation technique for Bayesian Deep Learning and evaluated for image classification and regression tasks.
Episode-Based Active Learning with Bayesian Neural Networks
We investigate different strategies for active learning with Bayesian deep neural networks.
Towards Unsupervised Weed Scouting for Agricultural Robotics
This greatly limits deployability as classification systems must be retrained for any field with a different set of weed species present within them.
Peduncle Detection of Sweet Pepper for Autonomous Crop Harvesting - Combined Colour and 3D Information
This paper presents a 3D visual detection method for the challenging task of detecting peduncles of sweet peppers (Capsicum annuum) in the field.
On the Performance of ConvNet Features for Place Recognition
Computer vision datasets are very different in character to robotic camera data, real-time performance is essential, and performance priorities can be different.
