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Found 10 papers, 1 papers with code
Vector-Symbolic Architecture for Event-Based Optical Flow
Based on this HD feature descriptor, we propose a novel feature matching framework for event-based optical flow, encompassing both model-based (VSA-Flow) and self-supervised learning (VSA-SM) methods.
Unsupervised Visual Odometry and Action Integration for PointGoal Navigation in Indoor Environment
To improve the PointGoal navigation accuracy without GPS signal, we use visual odometry (VO) and propose a novel action integration module (AIM) trained in unsupervised manner.
Orientation and Context Entangled Network for Retinal Vessel Segmentation
In this paper, we propose a robust Orientation and Context Entangled Network (denoted as OCE-Net), which has the capability of extracting complex orientation and context information of the blood vessels.
Deep Pneumonia: Attention-Based Contrastive Learning for Class-Imbalanced Pneumonia Lesion Recognition in Chest X-rays
Computer-aided X-ray pneumonia lesion recognition is important for accurate diagnosis of pneumonia.
Learning Generalized Visual Odometry Using Position-Aware Optical Flow and Geometric Bundle Adjustment
The experiments show that the proposed system not only achieves comparable performance with other state-of-the-art self-supervised learning-based methods on the KITTI dataset, but also significantly improves the generalization capability compared with geometry-based, learning-based and hybrid VO systems on the noisy KITTI and the challenging outdoor (KAIST) scenes.
TSN-CA: A Two-Stage Network with Channel Attention for Low-Light Image Enhancement
In this paper, we propose a Two-Stage Network with Channel Attention (denoted as TSN-CA) to enhance the brightness of the low-light image and restore the enhanced images from various kinds of degradation.
DA-DRN: Degradation-Aware Deep Retinex Network for Low-Light Image Enhancement
We propose a Degradation-Aware Module (DA Module) which can guide the training process of the decomposer and enable the decomposer to be a restorer during the training phase without additional computational cost in the test phase.
BLNet: A Fast Deep Learning Framework for Low-Light Image Enhancement with Noise Removal and Color Restoration
We propose a Noise and Color Bias Control module (NCBC Module) that contains a convolutional neural network and two loss functions (noise loss and color loss).
Thermal Infrared Image Colorization for Nighttime Driving Scenes with Top-Down Guided Attention
Furthermore, a new metric is devised to evaluate the geometric consistency in the translation process.
Clustering by Deep Nearest Neighbor Descent (D-NND): A Density-based Parameter-Insensitive Clustering Method
A large bandwidth could lead to the over-smoothed density estimation in which the number of density peaks could be less than the true clusters, while a small bandwidth could lead to the under-smoothed density estimation in which spurious density peaks, or called the "ripple noise", would be generated in the estimated density.
