118 citations · 184 across the 5 of their papers we have counts for
5 papers
Bridging the gap between image coding for machines and humans
Nam Le, Honglei Zhang, Francesco Cricri +5
Image coding for machines (ICM) aims at reducing the bitrate required to represent an image while minimizing the drop in machine vision analysis accuracy. In many use cases, such a…
Image coding for machines: an end-to-end learned approach
Nam Le, Honglei Zhang, Francesco Cricri +2
Over recent years, deep learning-based computer vision systems have been applied to images at an ever-increasing pace, oftentimes representing the only type of consumption for thos…
Learned Image Coding for Machines: A Content-Adaptive Approach
Nam Le, Honglei Zhang, Francesco Cricri +3
Today, according to the Cisco Annual Internet Report (2018-2023), the fastest-growing category of Internet traffic is machine-to-machine communication. In particular, machine-to-ma…
Theoretical Guarantees of Deep Embedding Losses Under Label Noise
Nam Le, Jean-Marc Odobez
Collecting labeled data to train deep neural networks is costly and even impractical for many tasks. Thus, research effort has been focused in automatically curated datasets or uns…
Improving speaker turn embedding by crossmodal transfer learning from face embedding
Nam Le, Jean-Marc Odobez
Learning speaker turn embeddings has shown considerable improvement in situations where conventional speaker modeling approaches fail. However, this improvement is relatively limit…