81 citations · 282 across the 19 of their papers we have counts for
24 papers
AIM 2024 Challenge on Video Saliency Prediction: Methods and Results
Andrey Moskalenko, Alexey Bryncev, Dmitry Vatolin +30
This paper reviews the Challenge on Video Saliency Prediction at AIM 2024. The goal of the participants was to develop a method for predicting accurate saliency maps for the provid…
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…
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-…
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…
Understanding Visual Saliency in Mobile User Interfaces
Luis A. Leiva, Yunfei Xue, Avya Bansal +4
For graphical user interface (UI) design, it is important to understand what attracts visual attention. While previous work on saliency has focused on desktop and web-based UIs, mo…
A Compact Deep Architecture for Real-time Saliency Prediction
Saman Zabihi, Hamed Rezazadegan Tavakoli, Ali Borji
Saliency computation models aim to imitate the attention mechanism in the human visual system. The application of deep neural networks for saliency prediction has led to a drastic…
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…
Image Captioning through Image Transformer
Sen He, Wentong Liao, Hamed R. Tavakoli +3
Automatic captioning of images is a task that combines the challenges of image analysis and text generation. One important aspect in captioning is the notion of attention: How to d…
Deep Saliency Models : The Quest For The Loss Function
Alexandre Bruckert, Hamed R. Tavakoli, Zhi Liu +2
Recent advances in deep learning have pushed the performances of visual saliency models way further than it has ever been. Numerous models in the literature present new ways to des…
DAVE: A Deep Audio-Visual Embedding for Dynamic Saliency Prediction
Hamed R. Tavakoli, Ali Borji, Esa Rahtu +1
This paper studies audio-visual deep saliency prediction. It introduces a conceptually simple and effective Deep Audio-Visual Embedding for dynamic saliency prediction dubbed ``DAV…
PeyeDF: an Eye-Tracking Application for Reading and Self-Indexing Research
Marco Filetti, Hamed R. Tavakoli, Niklas Ravaja +1
PeyeDF is a Portable Document Format (PDF) reader with eye tracking support, available as free and open source software. It is especially useful to researchers investigating readin…
Digging Deeper into Egocentric Gaze Prediction
Hamed R. Tavakoli, Esa Rahtu, Juho Kannala +1
This paper digs deeper into factors that influence egocentric gaze. Instead of training deep models for this purpose in a blind manner, we propose to inspect factors that contribut…
Understanding and Visualizing Deep Visual Saliency Models
Sen He, Hamed R. Tavakoli, Ali Borji +2
Recently, data-driven deep saliency models have achieved high performance and have outperformed classical saliency models, as demonstrated by results on datasets such as the MIT300…
Geometric Image Correspondence Verification by Dense Pixel Matching
Zakaria Laskar, Iaroslav Melekhov, Hamed R. Tavakoli +2
This paper addresses the problem of determining dense pixel correspondences between two images and its application to geometric correspondence verification in image retrieval. The…
Human Attention in Image Captioning: Dataset and Analysis
Sen He, Hamed R. Tavakoli, Ali Borji +1
In this work, we present a novel dataset consisting of eye movements and verbal descriptions recorded synchronously over images. Using this data, we study the differences in human…
Semantic Matching by Weakly Supervised 2D Point Set Registration
Zakaria Laskar, Hamed R. Tavakoli, Juho Kannala
In this paper we address the problem of establishing correspondences between different instances of the same object. The problem is posed as finding the geometric transformation th…
Bottom-up Attention, Models of
Ali Borji, Hamed R. Tavakoli, Zoya Bylinskii
In this review, we examine the recent progress in saliency prediction and proposed several avenues for future research. In spite of tremendous efforts and huge progress, there is s…
Saliency Revisited: Analysis of Mouse Movements versus Fixations
Hamed R. Tavakoli, Fawad Ahmed, Ali Borji +1
This paper revisits visual saliency prediction by evaluating the recent advancements in this field such as crowd-sourced mouse tracking-based databases and contextual annotations.…
Investigating Natural Image Pleasantness Recognition using Deep Features and Eye Tracking for Loosely Controlled Human-computer Interaction
Hamed R. Tavakoli, Jorma Laaksonen, Esa Rahtu
This paper revisits recognition of natural image pleasantness by employing deep convolutional neural networks and affordable eye trackers. There exist several approaches to recogni…
Towards Instance Segmentation with Object Priority: Prominent Object Detection and Recognition
Hamed R. Tavakoli, Jorma Laaksonen
This manuscript introduces the problem of prominent object detection and recognition inspired by the fact that human seems to priorities perception of scene elements. The problem d…
Exploiting inter-image similarity and ensemble of extreme learners for fixation prediction using deep features
Hamed R. -Tavakoli, Ali Borji, Jorma Laaksonen +1
This paper presents a novel fixation prediction and saliency modeling framework based on inter-image similarities and ensemble of Extreme Learning Machines (ELM). The proposed fram…