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Found 7 papers, 4 papers with code
Recursive LMMSE-Based Iterative Soft Interference Cancellation for MIMO Systems to Save Computations and Memories
Firstly, a reordered description is given for the linear minimum mean square error (LMMSE)-based iterative soft interference cancellation (ISIC) detection process for Mutipleinput multiple-output (MIMO) wireless communication systems, which is based on the equivalent channel matrix.
Explicit Attention-Enhanced Fusion for RGB-Thermal Perception Tasks
Specifically, we consider the following cases: i) both RGB data and thermal data, ii) only one of the types of data, and iii) none of them generate discriminative features.
Ranked #2 on
Thermal Image Segmentation
on Noisy RS RGB-T Dataset
Improved Recursive Algorithms for V-BLAST to Save Computations and Memories
In the existing algorithm with speed advantage, the proposed algorithm I with speed advantage replaces Improvement I with Improvement V, while the proposed algorithm II with both speed advantage and memory saving replaces Improvements I and II with Improvements V and VI, respectively.
Abnormal Occupancy Grid Map Recognition using Attention Network
The occupancy grid map is a critical component of autonomous positioning and navigation in the mobile robotic system, as many other systems' performance depends heavily on it.
FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time Semantic Segmentation
To better extract detail spatial information, we propose a two-stage Feature-Enhanced Attention Network (FEANet) for the RGB-T semantic segmentation task.
Ranked #14 on
Semantic Segmentation
on FMB Dataset
A Two-stage Unsupervised Approach for Low light Image Enhancement
However, most of them suffer from the following problems: 1) the need of pairs of low light and normal light images for training, 2) the poor performance for dark images, 3) the amplification of noise.
Low-Light Image Enhancement
Simultaneous Localization and Mapping
+1
Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale Environment
The core problem of visual multi-robot simultaneous localization and mapping (MR-SLAM) is how to efficiently and accurately perform multi-robot global localization (MR-GL).
