Journal of Visual Communication and Image Representation Special Issue – Call for Papers

Journal of Visual Communication and Image Representation Special Issue on

Multimodal Learning for Visual Intelligence: From Emerging Techniques to Real-World Applications

In recent years, the integration of vision with complementary modalities such as language, audio, and sensor signals has emerged as a key enabler for intelligent systems that operate in unstructured environments. The emergence of foundation models and cross-modal pretraining has brought a paradigm shift to the field, making it timely to revisit the core challenges and innovative techniques in multimodal visual understanding.

This Special Issue aims to collect cutting-edge research and engineering practices that advance the understanding and development of visual intelligence systems through multimodal learning. The focus is on the deep integration of visual information with complementary modalities such as text, audio, and sensor data, enabling more comprehensive perception and reasoning in real-world environments. We encourage contributions from both academia and industry that address current challenges and propose novel methodologies for multimodal visual understanding.

Topics of interest include, but are not limited to:

  • Multimodal data alignment and fusion strategies with a focus on visual-centric modalities
  • Foundation models for multimodal visual representation learning
  • Generation and reconstruction techniques in visually grounded multimodal scenarios
  • Spatiotemporal modeling and relational reasoning of visual-centric multimodal data
  • Lightweight multimodal visual models for resource-constrained environments
  • Key technologies for visual-language retrieval and dialogue systems
  • Applications of multimodal visual computing in healthcare, transportation, robotics, and surveillance

Guest editors:

Guanghui Yue, PhD
Shenzhen University, Shenzhen, China
Email: yueguanghui@szu.edu.cn

Weide Liu, PhD
Harvard University, Cambridge, Massachusetts, USA
Emai: weide001@e.ntu.edu.sg

Ziyang Wang, PhD
The Alan Turing Institute, London, UK
Emai: zwang@turing.ac.uk

Hadi Amirpour, PhD
Alpen-Adria University, Klagenfurt, Austria
Emai: hadi.amirpour@aau.at

Zhedong Zheng, PhD
University of Macau, Macau, China
Email: zhedongzheng@um.edu.mo

Wei Zhou, PhD
Cardiff University, Cardiff, UK
Email: zhouw26@cardiff.ac.uk

Timeline:

Submission Open Date 30/05/2025

Final Manuscript Submission Deadline 30/11/2025

Editorial Acceptance Deadline 30/05/2026

Keywords: Multimodal Learning, Visual-Language Models, Cross-Modal Pretraining, Multimodal Fusion and Alignment, Spatiotemporal Reasoning, Lightweight Multimodal Models, Applications in Healthcare and Robotics

 

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Reza Farahani gave a tutorial at the 16th ACM/SPEC International Conference on Performance Engineering (ICPE)

Dr. Reza Farahani presented 3-hour tutorial titled “Serverless Orchestration on the Edge-Cloud Continuum: Challenges and Solutions” at the 16th ACM/SPEC International Conference on Performance Engineering (ICPE) on May 5.

Abstract: Serverless computing simplifies application development by abstracting infrastructure management, allowing developers to focus on building application functionality while infrastructure providers handle tasks, such as resource scaling and provisioning. Orchestrating serverless applications across the edge-cloud continuum, however, poses challenges such as managing heterogeneous resources with varying computational capacities and energy constraints, ensuring low-latency execution, dynamically allocating workloads based on real-time metrics, and maintaining fault tolerance and scalability across multiple edge and cloud instances. This tutorial first explores foundational serverless computing concepts, including Function-as-a-Service (FaaS), Backend-as-a-Service (BaaS), and their integration into distributed edge-cloud systems. It then introduces advancements in multi-cloud orchestration, edge-cloud integration strategies, and resource allocation techniques, focusing on their applicability in real-world scenarios. It addresses the challenges of orchestrating serverless applications across edge-cloud environments, mainly using dynamic workload distribution models, multi-objective scheduling algorithms, and energy-optimized orchestration. Practical demonstrations employ Kubernetes, serverless platforms such as GCP Functions, AWS Lambda, AWS Step Functions, OpenFaaS, and OpenWhisk, along with monitoring tools like Prometheus and Grafana, to deploy and execute real-world application workflows, providing participants with hands-on experience and insights into evaluating and refining energy- and performance-aware serverless orchestration strategies.

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IEEE ICIP’25: Machine Learning-Based Decoding Energy Modeling for VVC Streaming

Machine Learning-Based Decoding Energy Modeling for VVC Streaming

2025 IEEE International Conference on Image Processing (ICIP)

14-17 September, Anchorage, Alaska, USA

https://2025.ieeeicip.org/

Reza Farahani (AAU Klagenfurt, Austria), Vignesh V Menon (Fraunhofer HHI, Germany), and Christian Timmerer (AAU Klagenfurt, Austria)

Abstract: Efficient video streaming requires jointly optimizing encoding parameters (bitrate, resolution, compression efficiency) and decoding constraints (computational load, energy consumption) to balance quality and power efficiency, particularly for resource-constrained devices. However, hardware heterogeneity, including differences in CPU/GPU architectures, thermal management, and dynamic power scaling, makes absolute energy models unreliable, particularly for predicting decoding consumption. This paper introduces the Relative Decoding Energy Index (RDEI), a metric that normalizes decoding energy consumption against a baseline encoding configuration, eliminating device-specific dependencies to enable cross-platform comparability and guide energy-efficient streaming adaptations. We use a dataset of 1000 video sequences to extract complexity features capturing spatial and temporal variations, employ Versatile Video Coding (VVC) open-source toolchain using VVenC/VVdeC with various resolutions, framerate, encoding preset and quantization parameter (QP) sets, and model RDEI using Random Forest (RF), XGBoost, Linear Regression (LR), and Shallow Neural Networks (NN) for decoding energy prediction. Experimental results demonstrate that RDEI-based predictions provide accurate decoding energy estimates across different hardware, ensuring cross-device comparability in VVC streaming.

Keywords: Video Streaming; Energy Prediction; Versatile Video Coding (VVC); Video Complexity Analysis.

 

 

 

 

 

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Paper accepted: SCAREY: Location-Aware Service Lifecycle Management

Authors: Kurt Horvath, Dragi Kimovski, Radu Prodan

Venue: 2025 IEEE International Conference on Edge Computing and Communications (IEEE EDGE 2025), July 7-12 Helsinki, Finland

Abstract: Scheduling services within the computing continuum is complex due to the dynamic interplay of the Edge, Fog, and Cloud resources, each offering distinct computational and networking advantages. This paper introduces SCAREY, a user location-aided service  lifecycle management framework based on state machines. SCAREY addresses critical service discovery, provisioning, placement, and monitoring challenges by providing unified dynamic state machine-based lifecycle management, allowing instances to transition between discoverable and non-discoverable states based on demand. It incorporates a scalable service deployment algorithm to adjust the number of instances and employs network measurements to optimize service placement, ensuring minimal latency and enhancing sustainability. Real-world evaluations demonstrate a 73% improvement in service discovery and acquisition times, 45% cheaper operating costs and over 57% lesser power consumption and lower CO2 emissions compared to existing related methods.


 

 

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Invited Guest Talk

On 12 May, Dr Felix Schniz held an invited guest talk at the Department of Culturology, Faculty of Arts, University of Ljubljana.

In his talk, Felix discussed to challenges and necessities of Critical Theory thinking on an age of digital pessimism with a focus in game studies:

The Frankfurt School is a long-standing moral pillar of cultural studies. As the lines between humankind, virtuality, and technology blur more and more, however, the Frankfurt School’s stance towards the cultural industry incrementally enters a fundamental crisis of purpose. In this guest lecture, I elaborate on the crisis of digi-pessimism by drawing a bridge from the early days of Frankfurt School thinking to its contemporary work on interactive virtual artworks on the example of video games. I outline the origins of the school and its vital key terms along with its most prominent thinkers, its importance in the analysis of cultural artefacts, and its challenges when confronted with a medium that is as highly capitalistic in its conception as it is subversive. Focusing on Bloodborne (FromSoftware 2015), I portray the difficulties of navigating a virtual world that fuses pop-culture gothic horror with practices of anti-capitalist resistance and the meaning of such spaces in the face of contemporary political and technological raptures.

 

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Paper accepted: EnergyLess: An Energy-Aware Serverless Workflow Batch Orchestration on the Computing Continuum

We are happy to announce that our paper “EnergyLess: An Energy-Aware Serverless Workflow Batch Orchestration on the Computing Continuum” (by Reza Farahani and Radu Prodan) has been accepted for IEEE CLOUD 2025, which will take place in Helsinki, Finland, in July 2025.

Venue: IEEE International Conference on Cloud Computing 2025 (IEEE CLOUD 2025)

Abstract: Serverless cloud computing is increasingly adopted for workflow management, optimizing resource utilization for providers while lowering costs for customers. Integrating edge computing into this paradigm enhances scalability and efficiency, enabling seamless workflow distribution across geographically dispersed resources on the computing continuum. However, existing serverless workflow orchestration methods on the computing continuum often prioritize time and cost objectives, neglecting energy consumption and carbon footprint. This paper introduces EnergyLess, a multi-objective concurrent serverless workflow batch orchestration service for the computing continuum. EnergyLess decomposes workflow functions within a batch into finer-grained sub-functions and schedules either the original or sub-function versions to appropriate regions and instances on the continuum, improving energy consumption, carbon footprint, economic cost, and completion time while considering individual workflow requirements and resource constraints. We formulate the problem as a mixed-integer nonlinear programming (MINLP) model and propose three lightweight heuristic algorithms for function decomposition and scheduling. Evaluations on a large- scale computing continuum testbed with realistic workflows, spanning AWS Lambda, Google Cloud Functions (GCF), and 325 fog and edge instances across six regions demonstrate that EnergyLess improves cost efficiency by 75 %, completion time by 6%, energy consumption by 15%, and CO2 emissions by 20% for a batch size of 300, compared to three baseline methods.

 

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A Webbing Journey is launching in Early Access on May 19th, on PC via Steam and got a new trailer too!

Big news for spider fans: The viral sensation and wacky physics game A Webbing Journey is launching in Early Access on May 19th, on PC via Steam and got a new trailer too!

Congrats to Sebastian and his team!

A Webbing Journey has been drumming up excitement over the past months with gathering millions of views in regular viral social media posts! The demo is currently available on Steam and has a stunning, overwhelmingly positive review rating of 99% (over 500 reviews!). It comes from indie dev team Fire Totem Games and publisher Future Friends Games (Exo One, SUMMERHOUSE, The Cabin Factory).

 

 

Pattern Recognition Special Issue – Call for Papers

Pattern Recognition Special Issue on

Advances in Multimodal-Driven Video Understanding and Assessment

The rapid growth of video content across various domains has led to an increasing demand for more intelligent and efficient video understanding and assessment techniques. This Special Issue focuses on the integration of multimodal information, such as audio, text, and sensor data, with video to enhance processing, analysis, and interpretation. Multimodal-driven approaches are crucial for numerous real-world applications, including automated surveillance, content recommendation, and healthcare diagnostics.

This Special Issue invites cutting-edge research on topics such as video capture, compression, transmission, enhancement, and quality assessment, alongside advancements in deep learning, multimodal fusion, and real-time processing frameworks. By exploring innovative methodologies and emerging applications, we aim to provide a comprehensive perspective on the latest developments in this dynamic and evolving field.

Topics of interest include but are not limited to:

  • Multimodal-driven video capture techniques
  • Video compression and efficient transmission for/using multimodal data
  • Deep learning-based video enhancement and super-resolution
  • Multimodal action and activity recognition
  • Audio-visual and text-video fusion methods
  • Video quality assessment with multimodal cues
  • Video captioning and summarization using multimodal data
  • Real-time multimodal video processing frameworks
  • Explainability and interpretability in multimodal video models
  • Applications in surveillance, healthcare, and autonomous systems

Guest editors:

Wei Zhou, PhD
Cardiff University, Cardiff, United Kingdom
Email: zhouw26@cardiff.ac.uk

Yakun Ju, PhD
University of Leicester, Leicester, United Kingdom
Email: yj174@leicester.ac.uk

Hadi Amirpour, PhD
University of Klagenfurt, Klagenfurt, Austria
Email: hadi.amirpour@aau.at

Bruce Lu, PhD
University of Western Australia, Perth, Australia
Email: bruce.lu@uwa.edu.au

Jun Liu, PhD
Lancaster University, Lancaster, United Kingdom
Email: j.liu81@lancaster.ac.uk

Important dates 

Submission Portal Open: April 04, 2025

Submission Deadline: October 30, 2025

Acceptance Deadline: May 30, 2026

Keywords:

Multimodal video analysis, video understanding, deep learning, video quality assessment, action recognition, real-time video processing, audio-visual learning, text-video processing

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Paper accepted: Osmotic Learning: A Self-Supervised Paradigm for Decentralized Contextual Data Representation

Authors: Mario Colosi (University of Messina, Italy), Reza Farahani (University of Klagenfurt, Austria), Maria Fazio (University of Messina, Italy), Radu Prodan (University of Innsbruck, Austria), Massimo Villari (University of Messina, Italy)

Venue: International Joint Conference on Neural Networks (IJCNN), 30 June – 5 July 2025, Rome, Italy

Abstract: Data within a specific context gains deeper significance beyond its isolated interpretation. In distributed systems, interdependent data sources reveal hidden relationships and latent structures, representing valuable information for many applications. This paper introduces Osmotic Learning (OSM-L), a self-supervised distributed learning paradigm designed to uncover higher-level latent knowledge from distributed data. The core of OSM-L is osmosis, a process that synthesizes dense and compact representation by extracting contextual information, eliminating the need for raw data exchange between distributed entities. OSM-L iteratively aligns local data representations, enabling information diffusion and convergence into a dynamic equilibrium that captures contextual patterns. During training, it also identifies correlated data groups, functioning as a decentralized clustering mechanism. Experimental results confirm OSM-L’s convergence and representation capabilities on structured datasets, achieving over 0.99 accuracy in local information alignment while preserving contextual integrity.

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Journal article accepted: ACM TOMM: HTTP Adaptive Streaming: A Review on Current Advances and Future Challenges

ACM Transactions on Multimedia Computing, Communications, and Applications

 

Christian Timmerer (AAU, AT), Hadi Amirpour (AAU, AT), Farzad Tashtarian (AAU, AT), Samira Afzal (AAU, AT), Amr Rizk (Leibniz University Hannover, DE), Michael Zink (University of Massachusetts Amherst, US), and Hermann Hellwagner (AAU, AT)

Abstract: Video streaming has evolved from push-based, broad-/multicasting approaches with dedicated hard-/software infrastructures to pull-based unicast schemes utilizing existing Web-based infrastructure to allow for better scalability. In this article, we provide an overview of the foundational principles of HTTP adaptive streaming (HAS), from video encoding to end user consumption, while focusing on the key advancements in adaptive bitrate algorithms, quality of experience (QoE), and energy efficiency. Furthermore, the article highlights the ongoing challenges of optimizing network infrastructure, minimizing latency, and managing the environmental impact of video streaming. Finally, future directions for HAS, including immersive media streaming and neural network-based video codecs, are discussed, positioning HAS at the forefront of next-generation video delivery technologies.

Keywords: HTTP Adaptive Streaming, HAS, DASH, Video Coding, Video Delivery, Video Consumption, Quality of Experience, QoE

 

https://athena.itec.aau.at/2025/03/acm-tomm-http-adaptive-streaming-a-review-on-current-advances-and-future-challenges/