Search Results for author:
Found 7 papers, 4 papers with code
Learning to Control Camera Exposure via Reinforcement Learning
Adjusting camera exposure in arbitrary lighting conditions is the first step to ensure the functionality of computer vision applications.
Complementary Random Masking for RGB-Thermal Semantic Segmentation
Also, the proposed self-distillation loss encourages the network to extract complementary and meaningful representations from a single modality or complementary masked modalities.
Ranked #2 on
Thermal Image Segmentation
on PST900
DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning
In this paper, we propose a multi-objective camera ISP framework that utilizes Deep Reinforcement Learning (DRL) and camera ISP toolbox that consist of network-based and conventional ISP tools.
Maximizing Self-supervision from Thermal Image for Effective Self-supervised Learning of Depth and Ego-motion
Based on the analysis, we propose an effective thermal image mapping method that significantly increases image information, such as overall structure, contrast, and details, while preserving temporal consistency.
Depth Completion using Plane-Residual Representation
The basic framework of depth completion is to predict a pixel-wise dense depth map using very sparse input data.
Self-Supervised Depth and Ego-Motion Estimation for Monocular Thermal Video Using Multi-Spectral Consistency Loss
Based on the proposed module, the photometric consistency loss can provide complementary self-supervision to networks.
An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search
Deep reinforcement learning (DRL) algorithms and evolution strategies (ES) have been applied to various tasks, showing excellent performances.
