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Found 7 papers, 2 papers with code
Contour Loss for Instance Segmentation via k-step Distance Transformation Image
Instance segmentation aims to locate targets in the image and segment each target area at pixel level, which is one of the most important tasks in computer vision.
Context-Aware Dynamic Feature Extraction for 3D Object Detection in Point Clouds
Varying density of point clouds increases the difficulty of 3D detection.
Adaptive and Azimuth-Aware Fusion Network of Multimodal Local Features for 3D Object Detection
We adopt different modalities of LiDAR data to generate richer features and present an adaptive and azimuth-aware network to aggregate local features from image, bird's eye view maps and point cloud.
M4CD: A Robust Change Detection Method for Intelligent Visual Surveillance
Secondly, multiple heterogeneous features (including brightness variation, chromaticity variation, and texture variation) are extracted by comparing the input frame with the background model, and a multi-source learning strategy is designed to online estimate the probability distributions for both foreground and background.
Scene-Specific Pedestrian Detection Based on Parallel Vision
In order to make the generic scene pedestrian detectors work well in specific scenes, the labeled data from specific scenes are needed to adapt the models to the specific scenes.
Training and Testing Object Detectors with Virtual Images
In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data.
The ParallelEye Dataset: Constructing Large-Scale Artificial Scenes for Traffic Vision Research
As a result, we present a viable implementation pipeline for constructing large-scale artificial scenes for traffic vision research.
