Search Results for author:
Found 14 papers, 4 papers with code
Domain Adaptation for Satellite-Borne Hyperspectral Cloud Detection
However, prior to deployment, new missions that employ new sensors will not have enough representative datasets to train a CNN model, while a model trained solely on data from previous missions will underperform when deployed to process the data on the new missions.
Assessing Domain Gap for Continual Domain Adaptation in Object Detection
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, and seasons.
A Hybrid Quantum-Classical Algorithm for Robust Fitting
While our usage of quantum computing does not surmount the fundamental intractability of robust fitting, by providing error bounds our algorithm is a practical improvement over randomised heuristics.
Autonomy and Perception for Space Mining
Future Moon bases will likely be constructed using resources mined from the surface of the Moon.
Learning to Predict Repeatability of Interest Points
Many robotics applications require interest points that are highly repeatable under varying viewpoints and lighting conditions.
HM4: Hidden Markov Model with Memory Management for Visual Place Recognition
However, this creates an unboundedly-growing database that poses time and memory scalability challenges for place recognition methods.
In defense of OSVOS
Through adding a common module, video loss, which we formulate with various forms of constraints (including weighted BCE loss, high-dimensional triplet loss, as well as a novel mixed instance-aware video loss), to train the parent network in the step (2), the network is then better prepared for the step (3), i. e. online fine-tuning on the target instance.
Scalable Place Recognition Under Appearance Change for Autonomous Driving
Our experiments show that, compared to state-of-the-art techniques, our method has much greater potential for large-scale place recognition for autonomous driving.
Visual Localization Under Appearance Change: Filtering Approaches
Our approaches rely on local features with an encoding technique to represent an image as a single vector.
G2D: from GTA to Data
This document describes G2D, a software that enables capturing videos from Grand Theft Auto V (GTA V), a popular role playing game set in an expansive virtual city.
On-device Scalable Image-based Localization via Prioritized Cascade Search and Fast One-Many RANSAC
In order to overcome the resource constraints of mobile devices, we propose a system design that leverages the scalability advantage of image retrieval and accuracy of 3D model-based localization.
Compact Hash Code Learning with Binary Deep Neural Network
This design has overcome a challenging problem in some previous works: optimizing non-smooth objective functions because of binarization.
Binary Hashing with Semidefinite Relaxation and Augmented Lagrangian
This paper proposes two approaches for inferencing binary codes in two-step (supervised, unsupervised) hashing.
Learning to Hash with Binary Deep Neural Network
Our resulting optimization with these binary, independence, and balance constraints is difficult to solve.
