no code implementations • 8 Oct 2022 • Honglei Zhang, Francesco Cricri, Hamed Rezazadegan Tavakoli, Emre Aksu, Miska M. Hannuksela
Nevertheless, the proposed LIC systems are still inferior to the state-of-the-art traditional techniques, for example, the Versatile Video Coding (VVC/H. 266) standard, due to either their compression performance or decoding complexity.
no code implementations • 16 Dec 2021 • Nannan Zou, Honglei Zhang, Francesco Cricri, Ramin G. Youvalari, Hamed R. Tavakoli, Jani Lainema, Emre Aksu, Miska Hannuksela, Esa Rahtu
In this work, we propose an end-to-end learned video codec that introduces several architectural novelties as well as training novelties, revolving around the concepts of adaptation and attention.
no code implementations • 24 Aug 2021 • Honglei Zhang, Francesco Cricri, Hamed R. Tavakoli, Nannan Zou, Emre Aksu, Miska M. Hannuksela
Recently, multi-scale autoregressive models have been proposed to address this limitation.
no code implementations • 23 Aug 2021 • Nam Le, Honglei Zhang, Francesco Cricri, Ramin Ghaznavi-Youvalari, Hamed Rezazadegan Tavakoli, Esa Rahtu
One possible solution approach consists of adapting current human-targeted image and video coding standards to the use case of machine consumption.
no code implementations • 23 Aug 2021 • Nam Le, Honglei Zhang, Francesco Cricri, Ramin Ghaznavi-Youvalari, Esa Rahtu
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 those images.
no code implementations • 31 Jul 2020 • Nannan Zou, Honglei Zhang, Francesco Cricri, Hamed R. -Tavakoli, Jani Lainema, Miska Hannuksela, Emre Aksu, Esa Rahtu
In a second phase, the Model-Agnostic Meta-learning approach is adapted to the specific case of image compression, where the inner-loop performs latent tensor overfitting, and the outer loop updates both encoder and decoder neural networks based on the overfitting performance.
no code implementations • 28 Jul 2020 • Yat-Hong Lam, Alireza Zare, Francesco Cricri, Jani Lainema, Miska Hannuksela
We present an efficient finetuning methodology for neural-network filters which are applied as a postprocessing artifact-removal step in video coding pipelines.
no code implementations • 20 Apr 2020 • Nannan Zou, Honglei Zhang, Francesco Cricri, Hamed R. -Tavakoli, Jani Lainema, Emre Aksu, Miska Hannuksela, Esa Rahtu
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.
no code implementations • 24 May 2019 • Caglar Aytekin, Francesco Cricri, Antti Hallapuro, Jani Lainema, Emre Aksu, Miska Hannuksela
In this manuscript we propose two objective terms for neural image compression: a compression objective and a cycle loss.
no code implementations • 10 May 2019 • Yat Hong Lam, Alireza Zare, Caglar Aytekin, Francesco Cricri, Jani Lainema, Emre Aksu, Miska Hannuksela
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.
no code implementations • 3 May 2019 • Caglar Aytekin, Francesco Cricri, Emre Aksu
In this paper we apply a compressibility loss that enables learning highly compressible neural network weights.
no code implementations • 28 May 2018 • Caglar Aytekin, Xingyang Ni, Francesco Cricri, Jani Lainema, Emre Aksu, Miska Hannuksela
In this work, we propose an end-to-end block-based auto-encoder system for image compression.
1 code implementation • 21 May 2018 • Wenyan Yang, Yanlin Qian, Francesco Cricri, Lixin Fan, Joni-Kristian Kamarainen
We introduced a high-resolution equirectangular panorama (360-degree, virtual reality) dataset for object detection and propose a multi-projection variant of YOLO detector.
no code implementations • 26 Feb 2018 • Uğur Kart, Joni-Kristian Kämäräinen, Jiří Matas, Lixin Fan, Francesco Cricri
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 benchmark datasets and applications.
no code implementations • 8 Feb 2018 • Caglar Aytekin, Francesco Cricri, Emre Aksu
In this work, we propose an improvement over DCF based trackers by combining saliency based and other features based filter responses.
no code implementations • 1 Feb 2018 • Caglar Aytekin, Xingyang Ni, Francesco Cricri, Emre Aksu
We show the effect of l2 normalization on anomaly detection accuracy.
no code implementations • 24 Jan 2018 • Caglar Aytekin, Francesco Cricri, Lixin Fan, Emre Aksu
In order to have an in-depth theoretical understanding, in this manuscript, we investigate the graph degree in spectral graph clustering based and kernel based point of views and draw connections to a recent kernel method for the two sample problem.
no code implementations • 27 Dec 2017 • Caglar Aytekin, Xingyang Ni, Francesco Cricri, Lixin Fan, Emre Aksu
By using these encoded images, we train a memory-efficient network using only 0. 048\% of the number of parameters that other deep salient object detection networks have.
1 code implementation • 6 Dec 2016 • Francesco Cricri, Xingyang Ni, Mikko Honkala, Emre Aksu, Moncef Gabbouj
Thanks to the recurrent connections, the decoder can exploit temporal summaries generated from all layers of the encoder.