3 papers
cs.CV2021
Co-training for Deep Object Detection: Comparing Single-modal and Multi-modal Approaches
Jose L. Gómez, Gabriel Villalonga, Antonio M. López
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…
eess.IV2019
Variable Rate Deep Image Compression with Modulated Autoencoder
Fei Yang, Luis Herranz, Joost van de Weijer +3
Variable rate is a requirement for flexible and adaptable image and video compression. However, deep image compression methods are optimized for a single fixed rate-distortion trad…
cs.CV2018
Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving
Jiaolong Xu, Peng Wang, Heng Yang +1
Autonomous driving has harsh requirements of small model size and energy efficiency, in order to enable the embedded system to achieve real-time on-board object detection. Recent d…