RESEARCH PAPER / May 2024
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["Wireless communication",
"Machine learning/ Deep learning /Artificial Intelligence"]
The futures of AI and wireless networks are intricately intertwined. On the one hand, AI is a potent tool for automating the deployment and management of wireless networks. The next-generation wireless network, on the other hand, can support the training and deployment of AI models by providing an ocean of...
RESEARCH PAPER / May 2024
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["Wireless communication",
"Machine learning/ Deep learning /Artificial Intelligence",
"5G",
"6G"]
"Compressed beamforming algorithm is used in the current Wi-Fi standard to reduce the beamforming feedback overhead (BFO). However, with each new amendment of the standard the number of supported antennas in Wi-Fi devices increases, leading to increased BFO and hampering the throughput despite using compressed beamforming. In this paper, a...
RESEARCH PAPER / May 2024
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["Machine learning/ Deep learning /Artificial Intelligence",
"Security",
"Wireless communication"]
Deep learning based automatic modulation classification (AMC) has received significant attention owing to its potential applications in both military and civilian use cases. Recently, data-driven subsampling techniques have been utilized to overcome the challenges associated with computational complexity and training time for AMC. Beyond these direct advantages of data-driven subsampling,...
RESEARCH PAPER / May 2024
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["Wireless communication",
"Machine learning/ Deep learning /Artificial Intelligence"]
While advances in Machine Learning have revolutionized certain areas (computer vision, robotics, natural language processing, etc.), the application in wireless communications has been less dramatic. One limiting factor is the (potentially) high computational complexity. Yet another important inhibitor is the lack of realistic datasets. To fully understand the potential of...
RESEARCH PAPER / Apr 2024
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["Wireless communication",
"Machine learning/ Deep learning /Artificial Intelligence",
"Radio frequency"]
The challenging propagation environment, combined with the hardware limitations of mmWave systems, gives rise to the need for accurate initial access beam alignment strategies with low latency and high achievable beamforming gain. Much of the recent work in this area either focuses on onesided beam alignment, or, joint beam alignment...
RESEARCH PAPER / Apr 2024
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["Machine learning/ Deep learning /Artificial Intelligence",
"Computer Vision",
"Image processing"]
RESEARCH PAPER / Apr 2024
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["Compression",
"Volumetric Imaging",
"Machine learning/ Deep learning /Artificial Intelligence"]
"Learning-based point cloud (PC) compression is a promising research avenue to reduce the transmission and storage costs for PC applications. Existing learning-based methods to compress PCs attributes employ variational autoencoders (VAE) or normalizing flows (NF) to learn compact signal representations. However, VAEs leverage a lower-dimensional bottleneck that limit the maximum...
RESEARCH PAPER / Apr 2024
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["Compression",
"Video coding",
"Machine learning/ Deep learning /Artificial Intelligence"]
"The last standard Versatile Video Codec (VVC), aims to im- prove the compression efficiency by saving around 50% of bitrate at the same quality compared to its predecessor High Efficiency Video Codec (HEVC). However, this comes with a significant rise in computational complexity due to the new added tools in...
RESEARCH PAPER / Mar 2024
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["Compression",
"Machine learning/ Deep learning /Artificial Intelligence"]
Achieving successful variable bitrate compression with computationally simple algorithms from a single end-to-end learned image or video compression model remains a challenge. Many approaches have been proposed, including conditional auto-encoders, channel-adaptive gains for the latent tensor or uniformly quantizing all elements of the latent tensor. This paper follows the traditional...
RESEARCH PAPER / Mar 2024
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["Volumetric Imaging",
"Machine learning/ Deep learning /Artificial Intelligence"]
The universality of the point cloud format enables many 3D applications, making the compression of point clouds a critical phase in practice. Sampled as discrete 3D points, a point cloud approximates 2D surface(s) embedded in 3D with a finite bit-depth. However, the point distribution of a practical point cloud changes...
RESEARCH PAPER / Feb 2024
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["Wireless communication",
"5G",
"Machine learning/ Deep learning /Artificial Intelligence"]
The ubiquitous deployment of 4G/5G technology has made it a critical infrastructure for society that will facilitate the delivery and adoption of emerging applications and use cases (extended reality, automation, robotics, to name but a few). These new applications require high throughput and low latency in both uplink and downlink...
RESEARCH PAPER / Jan 2024
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["Machine learning/ Deep learning /Artificial Intelligence",
"5G",
"Wireless communication"]
"Abstract—3rd Generation Partnership Project (3GPP) Release 18 has initiated a comprehensive study of Artificial Intelligence (AI)/Machine Learning (ML) use cases for Air Interface, e.g., Channel State Information (CSI) feedback enhancement, beam management, and positioning accuracy enhancement. In order to advance the adoption of AI/ML in 5G and towards 6G, it...
RESEARCH PAPER / Jan 2022
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Immersive / AR/VR/MR,
Light Field,
Volumetric Imaging,
Machine learning/ Deep learning /Artificial Intelligence
Recently, learning methods have been designed to create Multiplane Images (MPIs) for view synthesis. While MPIs are extremely powerful and facilitate high quality renderings, a great amount of memory is required, making them impractical for many applications. In this paper, we propose a learning method that optimizes the available memory...
RESEARCH PAPER / Dec 2021
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Computer Vision,
Machine learning/ Deep learning /Artificial Intelligence
This paper describes the MediaEval 2021 Predicting Media Memorability task. After first being proposed at MediaEval 2018, the Predicting Media Memorability task is in its 4th edition this year, as the prediction of short-term and long-term video memorability remains a challenging task. This year, two datasets of videos are used:...
RESEARCH PAPER / Oct 2021
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Computer Graphics,
Machine learning/ Deep learning /Artificial Intelligence
Human character animation is often critical in entertainment content production, including video games, virtual reality or fiction films. To this end, deep neural networks drive most recent advances through deep learning (DL) and deep reinforcement learning (DRL). In this article, we propose a comprehensive survey on the state-of-the-art approaches based...
RESEARCH PAPER / Oct 2021
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Audio processing,
Neural network,
Machine learning/ Deep learning /Artificial Intelligence
Music source separation is the task of isolating individual instruments which are mixed in a musical piece. This task is particularly challenging, and even state-of-the-art models can hardly generalize to unseen test data. Nevertheless, prior knowledge about individual sources can be used to better adapt a generic source separation model...
RESEARCH PAPER / Oct 2021
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Computer Vision,
Neural network,
Machine learning/ Deep learning /Artificial Intelligence
High quality facial attribute editing in videos is a challenging problem as it requires the modifications to be realistic and consistent throughout the video frames. Previous works address the problem with auto-encoder architectures and rely on adversarial training to ensure the attribute editing and the temporal consistency of the results....
RESEARCH PAPER / Sep 2021
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Video coding,
Compression,
Machine learning/ Deep learning /Artificial Intelligence Neural network
Despite many modern applications of Deep Neural Networks (DNNs), the large number of parameters in the hidden layers makes them unattractive for deployment on devices with storage capacity constraints. In this paper we propose a Data-Driven Low-rank (DDLR) method to reduce the number of parameters of pretrained DNNs and expedite...
RESEARCH PAPER / Sep 2021
/
Video coding,
Machine learning/ Deep learning /Artificial Intelligence,
Image processing,
Computer Graphics
Recently, learning methods have been designed to create Multiplane Images (MPIs) for view synthesis. While MPIs are extremely powerful and facilitate high quality renderings, a great amount of memory is required, making them impractical for many applications. In this paper, we propose a learning method that optimizes the available memory...
RESEARCH PAPER / Sep 2021
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Optics,
Machine learning/ Deep learning /Artificial Intelligence,
Image processing
In recent years, we have seen the development of integrated plenoptic sensors, where multiple pixels are placed under one microlens. It is mainly used by cameras and smartphones to drive the autofocus of the main lens, and it often takes the form of dual-pixels with 2 rectangular sub-pixels. We study...
RESEARCH PAPER / Aug 2021
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Neural network,
Machine learning/ Deep learning /Artificial Intelligence,
Computer Vision
Deep neural networks (DNNs) have recently achieved great success in many machine learning tasks including computer vision and speech recognition. However, existing DNN models are computationally expensive and memory demanding, hindering their deployment in devices with low memory and computational resources or in applications with strict latency requirements. In addition,...
RESEARCH PAPER / Aug 2021
/
Video coding,
Compression,
Machine learning/ Deep learning /Artificial Intelligence
Deep bi-prediction blending. This paper presents a learning-based method to improve bi-prediction in video coding. In conventional video coding solutions, block-based motion compensation blocks from already decoded reference pictures stand out as the main tool used to predict the current frame. Especially, bi-predicted blocks, i.e. blocks that combine two different...
RESEARCH PAPER / Jul 2021
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5G,
Wireless communication,
Machine learning/ Deep learning /Artificial Intelligence
Building upon on a digital transformation, Industry 4.0 (I4.0) aims to build the factories of the future, which feature additional flexibility, increasingly connected infrastructures and automated processes. 5G is playing a paramount role in this transformation, as it can offer high bandwidth, reliable and low latency wireless connectivity to meet...
RESEARCH PAPER / Dec 2020
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["Volumetric Imaging",
"Machine learning/ Deep learning /Artificial Intelligence"]
We present a novel learning-based approach to synthesize new views of a light field image. In particular, given the four corner views of a light field, the presented method estimates any in-between view. We use three sequential convolutional neural networks for feature extraction, scene geometry estimation and view selection. Compared...
RESEARCH PAPER / Dec 2020
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5G,
Machine learning/ Deep learning /Artificial Intelligence,
Network and Communications
This document describes the winning solution to the GNN Challenge 2020 organized by the Barcelona Neural Networking Center for the ITU Artificial Intelligence/Machine Learning in 5G Challenge. We first describe our methodology, then give the set of hyper-parameters that allowed us to achieve the best score with an average relative...
In this paper we address the problem of view synthesis from large baseline light fields, by turning a sparse set of input views into a Multi-plane Image (MPI). Because available datasets are scarce, we propose a lightweight network that does not require extensive training. Unlike latest approaches, our model does...
This paper presents CompressAI, an open-source library that provides custom operations, layers, models and tools to research, develop, and evaluate end-to-end image and video codecs. In particular, CompressAI includes pre-trained models and evaluation tools to compare learned methods with traditional codecs. Multiple models from the state-of-the-art on learned end-to-end image...
arxiv version of https://interdigital.sharepoint.com/sites/RI/Lists/ID Publications/DispForm.aspx?ID=794
RESEARCH PAPER / May 2017
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Machine learning/ Deep learning /Artificial Intelligence,
Computing and Optimization
Learning from multi-label data in an interactive framework is a challenging problem as algorithms must withstand some additional constraints: in particular, learning from few training examples in a limited time. A recent study of multi-label classifier behaviors in this context has identified the potential of the ensemble method “Random Forest...
Generating complex discrete distributions remains as one of the challenging problems in machine learning. Existing techniques for generating complex distributions with high degrees of freedom depend on standard generative models like Generative Adversarial Networks (GAN), Wasserstein GAN, and associated variations. Such models are based on an optimization involving the distance...
