Co-Training for Deep Object Detection: Comparing Single-Modal and Multi-Modal Approaches
Top-performing computer vision models are powered by convolutional neural networks (CNNs). Training an accurate CNN highly depends on both the raw sensor data and their associated ground truth (GT). Collecting such GT is usually done through human labeling, which is time-consuming and does not scale...
Main Authors: | Jose L. Gómez, Gabriel Villalonga, Antonio M. López |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-05-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/21/9/3185 |
Similar Items
-
Co-Training for On-Board Deep Object Detection
by: Gabriel Villalonga, et al.
Published: (2020-01-01) -
Multi-Modal 3D Object Detection in Autonomous Driving: A Survey
by: Deng, J., et al.
Published: (2023) -
A 3D Object Detection Based on Multi-Modality Sensors of USV
by: Yingying Wu, et al.
Published: (2019-02-01) -
The Notion of Subjective and Objective Modality in Language
by: T. A. Selezneva
Published: (2013-06-01) -
Centralised and Decentralised Sensor Fusion-Based Emergency Brake Assist
by: Ankur Deo, et al.
Published: (2021-08-01)