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Found 10 papers, 6 papers with code
Real-Time AIoT for UAV Antenna Interference Detection via Edge-Cloud Collaboration
However, the data transmission delay in the existing cloud-only (CO) artificial intelligence (AI) mode fails to meet the low latency requirements for real-time performance.
CV-MOS: A Cross-View Model for Motion Segmentation
When performing the motion object segmentation (MOS) task, effectively leveraging motion information from objects becomes a primary challenge in improving the recognition of moving objects.
MV-MOS: Multi-View Feature Fusion for 3D Moving Object Segmentation
To effectively exploit complementary information, the motion branches of the proposed model combines motion features from both bird's eye view (BEV) and range view (RV) representations.
OverlapMamba: Novel Shift State Space Model for LiDAR-based Place Recognition
In a novel way, we employ a stochastic reconstruction approach to build shift state space models, compressing the visual representation.
MambaMOS: LiDAR-based 3D Moving Object Segmentation with Motion-aware State Space Model
In this paper, we propose a novel LiDAR-based 3D Moving Object Segmentation with Motion-aware State Space Model, termed MambaMOS.
Speech Emotion Recognition Via CNN-Transformer and Multidimensional Attention Mechanism
In this paper, to model local and global information at different levels of granularity in speech and capture temporal, spatial and channel dependencies in speech signals, we propose a Speech Emotion Recognition network based on CNN-Transformer and multi-dimensional attention mechanisms.
YOLO-Ant: A Lightweight Detector via Depthwise Separable Convolutional and Large Kernel Design for Antenna Interference Source Detection
In this article, an antenna dataset is created to address important antenna interference source detection issues and serves as the basis for subsequent research.
MF-MOS: A Motion-Focused Model for Moving Object Segmentation
Moving object segmentation (MOS) provides a reliable solution for detecting traffic participants and thus is of great interest in the autonomous driving field.
Bone Marrow Cell Recognition: Training Deep Object Detection with A New Loss Function
Therefore, it is crucial to study a robust bone marrow cell detection algorithm for a quantitative automatic analysis system.
Robust Edge-Direct Visual Odometry based on CNN edge detection and Shi-Tomasi corner optimization
This solution used CNN edge detection and Shi-Tomasi corner optimization to extract information from the image.
