3 citations · 10 across the 13 of their papers we have counts for
13 papers
Feature Compression for Machines with Range-Based Channel Truncation and Frame Packing
Juan Merlos, Fabien Racapé, Hyomin Choi +2
This paper proposes a method that enhances the compression performance of the current model under development for the upcoming MPEG standard on Feature Coding for Machines (FCM). T…
Efficient Feature Compression for Machines with Global Statistics Preservation
Md Eimran Hossain Eimon, Hyomin Choi, Fabien Racapé +4
The split-inference paradigm divides an artificial intelligence (AI) model into two parts. This necessitates the transfer of intermediate feature data between the two halves. Here,…
DeDelayed: Deleting Remote Inference Delay via On-Device Correction
Dan Jacobellis, Mateen Ulhaq, Fabien Racapé +2
Video comprises the vast majority of bits that are generated daily, and is the primary signal driving current innovations in robotics, remote sensing, and wearable technology. Yet,…
Learned Compression for Images and Point Clouds
Mateen Ulhaq
Over the last decade, deep learning has shown great success at performing computer vision tasks, including classification, super-resolution, and style transfer. Now, we apply it to…
Learned Compression of Encoding Distributions
Mateen Ulhaq, Ivan V. Bajić
The entropy bottleneck introduced by Ballé et al. is a common component used in many learned compression models. It encodes a transformed latent representation using a static distr…
Scalable Human-Machine Point Cloud Compression
Mateen Ulhaq, Ivan V. Bajić
Due to the limited computational capabilities of edge devices, deep learning inference can be quite expensive. One remedy is to compress and transmit point cloud data over the netw…
Learned Point Cloud Compression for Classification
Mateen Ulhaq, Ivan V. Bajić
Deep learning is increasingly being used to perform machine vision tasks such as classification, object detection, and segmentation on 3D point cloud data. However, deep learning i…
Mobile-Cloud Inference for Collaborative Intelligence
Mateen Ulhaq
As AI applications for mobile devices become more prevalent, there is an increasing need for faster execution and lower energy consumption for deep learning model inference. Histor…
Learned Disentangled Latent Representations for Scalable Image Coding for Humans and Machines
Ezgi Ozyilkan, Mateen Ulhaq, Hyomin Choi +1
As an increasing amount of image and video content will be analyzed by machines, there is demand for a new codec paradigm that is capable of compressing visual input primarily for…
Frequency-aware Learned Image Compression for Quality Scalability
Hyomin Choi, Fabien Racape, Shahab Hamidi-Rad +2
Spatial frequency analysis and transforms serve a central role in most engineered image and video lossy codecs, but are rarely employed in neural network (NN)-based approaches. We…
Joint Image Compression and Denoising via Latent-Space Scalability
Saeed Ranjbar Alvar, Mateen Ulhaq, Hyomin Choi +1
When it comes to image compression in digital cameras, denoising is traditionally performed prior to compression. However, there are applications where image noise may be necessary…
Analysis of Latent-Space Motion for Collaborative Intelligence
Mateen Ulhaq, Ivan V. Bajić
When the input to a deep neural network (DNN) is a video signal, a sequence of feature tensors is produced at the intermediate layers of the model. If neighboring frames of the inp…
Shared Mobile-Cloud Inference for Collaborative Intelligence
Mateen Ulhaq, Ivan V. Bajić
As AI applications for mobile devices become more prevalent, there is an increasing need for faster execution and lower energy consumption for neural model inference. Historically,…