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Found 23 papers, 4 papers with code
Computer vision tasks for intelligent aerospace missions: An overview
Computer vision tasks are crucial for aerospace missions as they help spacecraft to understand and interpret the space environment, such as estimating position and orientation, reconstructing 3D models, and recognizing objects, which have been extensively studied to successfully carry out the missions.
Barron Space for Graph Convolution Neural Networks
In this paper, we introduce a Barron space of functions on a compact domain of graph signals.
GasMono: Geometry-Aided Self-Supervised Monocular Depth Estimation for Indoor Scenes
This paper tackles the challenges of self-supervised monocular depth estimation in indoor scenes caused by large rotation between frames and low texture.
IBAFormer: Intra-batch Attention Transformer for Domain Generalized Semantic Segmentation
Domain generalized semantic segmentation (DGSS) is a critical yet challenging task, where the model is trained only on source data without access to any target data.
CMDA: Cross-Modality Domain Adaptation for Nighttime Semantic Segmentation
However, limited by the low dynamic range of conventional cameras, images fail to capture structural details and boundary information in low-light conditions.
Causal reasoning in typical computer vision tasks
Deep learning has revolutionized the field of artificial intelligence.
Learning to Augment: Hallucinating Data for Domain Generalized Segmentation
Domain generalized semantic segmentation (DGSS) is an essential but highly challenging task, in which the model is trained only on source data and any target data is not available.
RoMa: Robust Dense Feature Matching
The aim is to learn a robust model, i. e., a model able to match under challenging real-world changes.
Molecular Joint Representation Learning via Multi-modal Information
To further fusing such multi-modal imformation, the correspondence between learned chemical feature from different representation should be considered.
Learning Nonlinear Couplings in Network of Agents from a Single Sample Trajectory
We consider a class of stochastic dynamical networks whose governing dynamics can be modeled using a coupling function.
Towards Generalization on Real Domain for Single Image Dehazing via Meta-Learning
In contrast, we present a domain generalization framework based on meta-learning to dig out representative and discriminative internal properties of real hazy domains without test-time training.
Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview
Specifically, the preliminaries on few/zero-shot visual semantic segmentation, including the problem definitions, typical datasets, and technical remedies, are briefly reviewed and discussed.
Graph Fourier transform based on singular value decomposition of directed Laplacian
The proposed GFT is consistent with the conventional GFT in the undirected graph setting, and on directed circulant graphs, the proposed GFT is the classical discrete Fourier transform, up to some rotation, permutation and phase adjustment.
Wiener filters on graphs and distributed polynomial approximation algorithms
In this paper, we consider Wiener filters to reconstruct deterministic and (wide-band) stationary graph signals from their observations corrupted by random noises, and we propose distributed algorithms to implement Wiener filters and inverse filters on networks in which agents are equipped with a data processing subsystem for limited data storage and computation power, and with a one-hop communication subsystem for direct data exchange only with their adjacent agents.
CoDGraD: A Code-based Distributed Gradient Descent Scheme for Decentralized Convex Optimization
In this paper, we consider a large network containing many regions such that each region is equipped with a worker with some data processing and communication capability.
Learn to Adapt for Monocular Depth Estimation
To boost the transferability of depth estimation models, we propose an adversarial depth estimation task and train the model in the pipeline of meta-learning.
Unsupervised Monocular Depth Estimation in Highly Complex Environments
Meanwhile, we further tackle the effects of unstable image transfer quality on domain adaptation, and an image adaptation approach is proposed to evaluate the quality of transferred images and re-weight the corresponding losses, so as to improve the performance of the adapted depth model.
Multi-task GANs for Semantic Segmentation and Depth Completion with Cycle Consistency
Semantic segmentation and depth completion are two challenging tasks in scene understanding, and they are widely used in robotics and autonomous driving.
Masked GANs for Unsupervised Depth and Pose Prediction with Scale Consistency
This paper proposes a masked generative adversarial network (GAN) for unsupervised monocular depth and ego-motion estimation. The MaskNet and Boolean mask scheme are designed in this framework to eliminate the effects of occlusions and impacts of visual field changes on the reconstruction loss and adversarial loss, respectively.
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey
Then, we further review the performance of RL and meta-learning from the aspects of accuracy or transferability or both of them in autonomous systems, involving pedestrian tracking, robot navigation and robotic manipulation.
Monocular Depth Estimation Based On Deep Learning: An Overview
With the rapid development of deep neural networks, monocular depth estimation based on deep learning has been widely studied recently and achieved promising performance in accuracy.
Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey
Second, we review the visual-based environmental perception and understanding methods based on deep learning, including deep learning-based monocular depth estimation, monocular ego-motion prediction, image enhancement, object detection, semantic segmentation, and their combinations with traditional vSLAM frameworks.
Deep Direct Visual Odometry
Extensive experiments on the KITTI dataset show that the proposed constraints can effectively improve the scale-consistency of TrajNet when compared with previous unsupervised monocular methods, and integration with TrajNet makes the initialization and tracking of DSO more robust and accurate.
