118 citations · 412 across the 17 of their papers we have counts for
17 papers
Competitive Learning for Achieving Content-specific Filters in Video Coding for Machines
Honglei Zhang, Jukka I. Ahonen, Nam Le +2
This paper investigates the efficacy of jointly optimizing content-specific post-processing filters to adapt a human oriented video/image codec into a codec suitable for machine vi…
NN-VVC: Versatile Video Coding boosted by self-supervisedly learned image coding for machines
Jukka I. Ahonen, Nam Le, Honglei Zhang +5
The recent progress in artificial intelligence has led to an ever-increasing usage of images and videos by machine analysis algorithms, mainly neural networks. Nonetheless, compres…
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…
Leveraging progressive model and overfitting for efficient learned image compression
Honglei Zhang, Francesco Cricri, Hamed Rezazadegan Tavakoli +2
Deep learning is overwhelmingly dominant in the field of computer vision and image/video processing for the last decade. However, for image and video compression, it lags behind th…
LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization
Honglei Zhang, Fangyuan Luo, Jun Wu +2
Federated recommender system (FRS), which enables many local devices to train a shared model jointly without transmitting local raw data, has become a prevalent recommendation para…
Adaptation and Attention for Neural Video Coding
Nannan Zou, Honglei Zhang, Francesco Cricri +6
Neural image coding represents now the state-of-the-art image compression approach. However, a lot of work is still to be done in the video domain. In this work, we propose an end-…
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…
Lossless Image Compression Using a Multi-Scale Progressive Statistical Model
Honglei Zhang, Francesco Cricri, Hamed R. Tavakoli +3
Lossless image compression is an important technique for image storage and transmission when information loss is not allowed. With the fast development of deep learning techniques,…
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…
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge
Heng Chang, Yu Rong, Tingyang Xu +6
With the success of the graph embedding model in both academic and industry areas, the robustness of graph embedding against adversarial attack inevitably becomes a crucial problem…
Mask-GVAE: Blind Denoising Graphs via Partition
Jia Li, Mengzhou Liu, Honglei Zhang +4
We present Mask-GVAE, a variational generative model for blind denoising large discrete graphs, in which "blind denoising" means we don't require any supervision from clean graphs.…
Dirichlet Graph Variational Autoencoder
Jia Li, Tomasyu Yu, Jiajin Li +5
Graph Neural Networks (GNNs) and Variational Autoencoders (VAEs) have been widely used in modeling and generating graphs with latent factors. However, there is no clear explanation…
Robust Data Hiding Using Inverse Gradient Attention
Honglei Zhang, Hu Wang, Yuanzhouhan Cao +2
Data hiding is the procedure of encoding desired information into a certain types of cover media (e.g. images) to resist potential noises for data recovery, while ensuring the embe…
Learning to Learn to Compress
Nannan Zou, Honglei Zhang, Francesco Cricri +5
In this paper we present an end-to-end meta-learned system for image compression. Traditional machine learning based approaches to image compression train one or more neural networ…
End-to-End Learning for Video Frame Compression with Self-Attention
Nannan Zou, Honglei Zhang, Francesco Cricri +5
One of the core components of conventional (i.e., non-learned) video codecs consists of predicting a frame from a previously-decoded frame, by leveraging temporal correlations. In…
Adversarial Attack on Community Detection by Hiding Individuals
Jia Li, Honglei Zhang, Zhichao Han +3
It has been demonstrated that adversarial graphs, i.e., graphs with imperceptible perturbations added, can cause deep graph models to fail on node/graph classification tasks. In th…
A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models
Heng Chang, Yu Rong, Tingyang Xu +5
With the great success of graph embedding model on both academic and industry area, the robustness of graph embedding against adversarial attack inevitably becomes a central proble…