118 citations · 288 across the 19 of their papers we have counts for
23 papers
CASR: Content-Adaptive Neural Super-Resolution Post-Filter for Versatile Video Coding via Low-Rank Overfitting
Khoa Pham-Dinh, Francesco Cricri, Maria Santamaria +5
The use of super-resolution as a post-processing step following a video codec allows videos to be encoded at reduced spatial resolution at the encoder side and to be reconstructed…
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…
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…
Efficient Adaptation of Neural Network Filter for Video Compression
Yat-Hong Lam, Alireza Zare, Francesco Cricri +2
We present an efficient finetuning methodology for neural-network filters which are applied as a postprocessing artifact-removal step in video coding pipelines. The fine-tuning is…
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…
Compressing Weight-updates for Image Artifacts Removal Neural Networks
Yat Hong Lam, Alireza Zare, Caglar Aytekin +4
In this paper, we present a novel approach for fine-tuning a decoder-side neural network in the context of image compression, such that the weight-updates are better compressible.…
A Compression Objective and a Cycle Loss for Neural Image Compression
Caglar Aytekin, Francesco Cricri, Antti Hallapuro +3
In this manuscript we propose two objective terms for neural image compression: a compression objective and a cycle loss. These terms are applied on the encoder output of an autoen…
Compressibility Loss for Neural Network Weights
Caglar Aytekin, Francesco Cricri, Emre Aksu
In this paper we apply a compressibility loss that enables learning highly compressible neural network weights. The loss was previously proposed as a measure of negated sparsity of…
Block-optimized Variable Bit Rate Neural Image Compression
Caglar Aytekin, Xingyang Ni, Francesco Cricri +3
In this work, we propose an end-to-end block-based auto-encoder system for image compression. We introduce novel contributions to neural-network based image compression, mainly in…
Object Detection in Equirectangular Panorama
Wenyan Yang, Yanlin Qian, Francesco Cricri +2
We introduced a high-resolution equirectangular panorama (360-degree, virtual reality) dataset for object detection and propose a multi-projection variant of YOLO detector. The mai…
Saliency-Enhanced Robust Visual Tracking
Caglar Aytekin, Francesco Cricri, Emre Aksu
Discrete correlation filter (DCF) based trackers have shown considerable success in visual object tracking. These trackers often make use of low to mid level features such as histo…
A Theoretical Investigation of Graph Degree as an Unsupervised Normality Measure
Caglar Aytekin, Francesco Cricri, Lixin Fan +1
For a graph representation of a dataset, a straightforward normality measure for a sample can be its graph degree. Considering a weighted graph, degree of a sample is the sum of th…
Depth Masked Discriminative Correlation Filter
Uğur Kart, Joni-Kristian Kämäräinen, Jiří Matas +2
Depth information provides a strong cue for occlusion detection and handling, but has been largely omitted in generic object tracking until recently due to lack of suitable benchma…
Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representations
Caglar Aytekin, Xingyang Ni, Francesco Cricri +1
Clustering is essential to many tasks in pattern recognition and computer vision. With the advent of deep learning, there is an increasing interest in learning deep unsupervised re…
Memory-Efficient Deep Salient Object Segmentation Networks on Gridized Superpixels
Caglar Aytekin, Xingyang Ni, Francesco Cricri +2
Computer vision algorithms with pixel-wise labeling tasks, such as semantic segmentation and salient object detection, have gone through a significant accuracy increase with the in…
Video Ladder Networks
Francesco Cricri, Xingyang Ni, Mikko Honkala +2
We present the Video Ladder Network (VLN) for efficiently generating future video frames. VLN is a neural encoder-decoder model augmented at all layers by both recurrent and feedfo…