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Found 9 papers, 6 papers with code
Transformers in Unsupervised Structure-from-Motion
Transformers have revolutionized deep learning based computer vision with improved performance as well as robustness to natural corruptions and adversarial attacks.
Continual Learning of Unsupervised Monocular Depth from Videos
Spatial scene understanding, including monocular depth estimation, is an important problem in various applications, such as robotics and autonomous driving.
Dynamically Modular and Sparse General Continual Learning
Real-world applications often require learning continuously from a stream of data under ever-changing conditions.
AI-Driven Road Maintenance Inspection v2: Reducing Data Dependency & Quantifying Road Damage
Road infrastructure maintenance inspection is typically a labor-intensive and critical task to ensure the safety of all road users.
Adversarial Attacks on Monocular Pose Estimation
While studies evaluating the impact of adversarial attacks on monocular depth estimation exist, a systematic demonstration and analysis of adversarial perturbations against pose estimation are lacking.
Transformers in Self-Supervised Monocular Depth Estimation with Unknown Camera Intrinsics
While recent works have compared transformers against their CNN counterparts for tasks such as image classification, no study exists that investigates the impact of using transformers for self-supervised monocular depth estimation.
A Comprehensive Study of Vision Transformers on Dense Prediction Tasks
Convolutional Neural Networks (CNNs), architectures consisting of convolutional layers, have been the standard choice in vision tasks.
Highlighting the Importance of Reducing Research Bias and Carbon Emissions in CNNs
We highlight that there is a need to rethink the design and evaluation of CNNs to alleviate the issue of research bias and carbon emissions.
Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth Estimation
Dense depth estimation is essential to scene-understanding for autonomous driving.
