MediaEval 2025
Multimedia Benchmark Workshop

Table of Contents

NewsImages

Overview paper:

• NewsImages in MediaEval 2025 - Comparing Image Retrievaland Generation for News Articles
  Lucien Heitz, Luca Rossetto, Benjamin Kille, AndreasLommatzsch, Mehdi Elahi, Duc-Tien Dang-Nguyen

Working notes papers:

• Comparing CLIP-Based Image Retrieval and Stable DiffusionTurbo for News Article Thumbnails
  Sakthi Mukesh Thanga Mariappan, Muthulakshmi Ramasamy andBeulah Arul

• Cross-Modal Image Recommendation for News Articles byMultimodal Foundation Models-Based Retrieval-Reranking
  Damianos Galanopoulos, Andreas Goulas, Vasileios Mezaris

• NewsImages: FAISS-Based Image Retrieval and StableDiffusion Generation for Diverse News Articles
  Mirunalini P, Yuvashree P H, Harini J, Kawvya M K

• Improving Text-to-image Retrieval of News Articles,Class-aware Fine-tuning of CLIP and the Use of Lead Text
  Bram Bakker, Xiaomeng Wang

• Diffusion-Based Approaches for NewsImage Generation: AComparative Study of SDXL Variants
  Xiaomeng Wang, Bram Bakker

• Beyond Similarity: Two-Stage Retrieval for News Image Search
  Bulat Khaertdinov, Aashutosh Ganesh, Mirela Popa, NavaTintarev

• CLIP-Based News Image Retrieval with Keyword-EnrichedPrompts for MediaEval 2025 NewsImages Task
  Ramcharan Swaminathan, Sakthivel Thangapandiyan, MirunaliniP, Saravanan Esakki

• Dual-Pipeline Multimedia Enrichment for News Articles viaImage Generation and Retrieval
  Dhannya S M, Lakshmi Priya S, Kavinsoorya Ks, Jeeva G, Arivuchezhiyan E, Ashwath Ram T

• Balancing Relevance and Compliance: Text-to-Image Methodsfor News Articles Visualization
  Alishba Subhani, Mahrukh Khan, Muhammad Rafi, MuhammadAtif Tahir

• Less is More: A Dual-Filtering Perspective on Prompt Designfor News Thumbnail Generation
  Nguyen Thanh Khoi

• A Systematic Evaluation of Vision-Language Models for NewsImage Retrieval: A Two-Stage Retrieve-and-Rerank PipelineAnalysis
  Lakshmi Priya Swaminatha Rao, Dhannya SanthakumariMadhavan, Shrika Thota, Thirumurugan Ravialagappan, VishalMuralidharan, Shanmugam Karthikeyen Shivaanee

• NewsImages: Retrieval and Generative AI for News Thumbnails
  Sanghamithra Namboodiri, Sadhana J, Samyuktha V

• A Comparative Study of Vision-Language Models for NewsImage-Headline Matching
  Hemnath Dillibabu, Naren Karthik Kandasamy, AadhithyaSrinivasan Rajashree

Quest for insight papers:

• NewsImages: CLIP-FAISS Retrieval and Diffusion-BasedGeneration for Editorial News Thumbnails
  Prabavathy Balasundaram, Harshavarthan S, Bharathi A andSwedha S

Synthetic Images

Overview paper:

• Synthetic Images: Advancing Detection of Generative AI Usedin Real-World Online Images
  Konda Nitish, Jayasree R, Mirunalini P, Ponsubash Raj R

Working notes papers:

• A CLIP-based Approach for Synthetic Image Detection underDistribution Shift
  Qiushi Li, Andrea Ciamarra, Roberto Caldelli, StefanoBerretti

• An EfficientNet Framework : Methods and Results forSynthetic Image Detection and Manipulation Localization
  Sakthi Mukesh Thanga Mariappan, Muthulakshmi Ramasamy andBeulah Arul

• A three-way Decision-based Synthetic Image Detection
  Zeshan Khan, Zunaira Zainab

• Synthetic vs Real Image Detection using Vision Transformers and CNN-based Architectures
  Noor Ul Huda, Ayesha Fayyaz, Uswa Asad, Yasir SaleemAfridi

• Advancing Detection of Generative AI Used in Real-WorldOnline Images: A ConvNeXt-Based ML Approach for MediaEval2025
  Mirunalini P, Rohith S, Sayed Afras S, Sathiya Priyan R

• Synthetic Images: Advancing detection of generative AI usedin real-world online images
  Avantikaa R, Sangeetha S K, Madhuri B, Vijaya Lakshmi A

• A resolution-agnostic three-stage framework for ImageForgery Detection and Localization
  Minh-Hoang Le, Minh-Khoa Le-Phan, Khanh-Ngoc Vu-Nguyen, Minh-Triet Tran, Trong-Le Do

• Synthetic Images: Advancing Detection of Generative AI Usedin Real-World Online Images
  Amrit Gopinath, Amudhan A, Adithya Sivakumar

• Mattaru Net: Lightweight synthetic image detection usingcontrastive distilled learning
  Ngoc-Tuan Nguyen, Phuc-Nguyen Lam-Gia, Nhat-Khai Hoang, Duc-Nghi Hoang, Hai-Dang Nguyen

• Detection of generative AI used in real-world online images
  J Bhuvana, S Siddharth Chandrasekar, Ramanan Mahendran andJ Pragatheesh

• Real Vs Synthetic Classification using ResNet: MediaEval 25Working notes
  Hafiz Muhammad Owais Raza, Muhammad Atif Tahir, RizwanAhmed Khan

• Synthetic Images at MediaEval 2025: Advancing detection ofgenerative AI in real-world online images
  Olga Papadopoulou, Manos Schinas, Riccardo Corvi, DimitriosKarageorgiou, Christos Koutlis, Fabrizio Guillaro, Efstratios Gavves, Hannes Mareen, Luisa Verdoliva andSymeon Papadopoulos

• YOLOv11-Based Synthetic Image Detection and ManipulationLocalization
  Dr. Mirunalinee T T, Muthu Anushree Elavarasan, Jayavarshini K S, Keerthana R, Baakyashri R S

Medico

Overview paper:

• Medico 2025: Visual Question Answering for GastrointestinalImaging
  Sushant Gautam, Vajira Lasantha Thambawita, Michael A.Riegler, Pål Halvorsen, Steven Hicks

Working notes papers:

• A Novel Approach to Medical Visual Question Answering:Integrating Disease Classification, Co-Attention Fusion,and Answer Generation
  Zeshan Khan

• Medico 2025: Visual Question Answering (with multimodalexplanations) for Gastrointestinal Imaging
  Lakshmi Priya S, Dhannya S M, Moogambigai A, Nikhil KarthikS, Pandiarajan D

• From Answers to Explanations: Self-Probing EfficientlyFine-Tuned Vision-Language Models for Medical VQA at Medico2025
  Sujata Gaihre, Amir Thapa Magar

• Curriculum-Guided Fine-Tuning for Multimodal VQA in GIEndoscopy (Team Lama4Vision)
  Mahdi Azmoodeh-Kalati, Mohammad Sadegh Maghareh, Saba Alaviand Reza Lashgari

• Exploring Vision-Language Models for Medical VQA onGastrointestinal Images: A LoRA Fine-Tuning Study
  Vishal S, Rajalakshmi Sivanaiah, Angel Deborah Suseelan

• Multi-Task Learning for Visually Grounded Reasoning inGastrointestinal VQA
  Itbaan Safwan, Muhammad Annas Shaikh, Muhammad Haaris, Ramail Khan, Muhammad Atif Tahir

• X-VQA for GI Diagnostics: Multimodal VQA withConfidence-Aware Explanations
  Krishna Tewari, Sukomal Pal

• LoRA-Enhanced PaliGemma for Efficient GastrointestinalVQA: MediaEval Medico 2025
  Prabhash Jha, Firoj Paudel, Debesh Jha

• Enhancing encoder-decoder architecture to visual questionanswering task for gastrointestinal images
  Minh-Triet Bui, Nam-Tran Nguyen-Thuy, Hai-Dang Nguyen andMinh-Triet Tran

• Medico 2025: Visual Question Answering (with multimodalexplanations) for Gastrointestinal Imaging
  Sarath Chandran K R, Sivasriraman P, Vishnu Murugesh V andVishwajith L K

Quest for insight papers:

• Towards Reliable VQA Evaluation: Addressing Image and Q\&ALeakage in Kvasir-VQA-x1 (Team Lama4Vision)
  Mohammad Sadegh Maghareh, Mahdi Azmoodeh-Kalati, Saba Alaviand Reza Lashgari

Memorability

Overview paper:

• Overview of The MediaEval 2025 Predicting Movie andCommercial Memorability Task
  Iván Martín-Fernández, Mihai Gabriel Constantin, Claire-Hélène Demarty, Manuel Gil-Martín, Sebastian Halder, Bogdan Ionescu, Rukiye Savran Kiziltepe, AnaMatran-Fernandez, Alba G. Seco de Herrera

Working notes papers:

• Exploring Movie Recall Prediction Using FunctionalDescriptors of the EEG Signal
  Iván Martín-Fernández, Mario Lobo-Alonso, SergioEsteban-Romero, Manuel Gil-Martín, FernandoFernández-Martínez

• MediaEval 2025 : A Multimodal Approach for Predicting Movieand Commercial Memorability using Stacking and GradientBoosting
  Sakthi Mukesh Thanga Mariappan, Muthulakshmi Ramasamy andBeulah Arul

• Predicting Movie and Commercial Memorability
  J Bhuvana, Ramanan Mahendran, S Siddharth Chandrasekar andJ Pragatheesh

• Exploring Visual, Textual, And Engagement Features forMemorability Predictions: Working Notes
  Mahnoor Adeel, Kisa Fatima, Muhammad Ibrahim Ayoubi, Mustafa Usmani, Muhammad Atif Tahir

• Multimodal Feature Fusion for Video and Brand Memorability
  Elif Nur Tekay, Irem Azra Isleyen, Murat Karakus, RukiyeSavran Kiziltepe

• Memorability: Predicting movie and commercial memorabilityusing visual and audio features
  Prabavathy Balasundaram, Dhanush Parthasarathy, TuhinaShaw, Nunna Jahnvi, Praveen M

• DACS-UM-RTL: Early Fusion and Pre-text task learning forVideo Memorability Prediction
  Aashutosh Ganesh, Iris Huijben, Bulat Khaertdinov, IskajJanssen, Mirela Popa, Nava Tintarev

• LLM-Based Fusion of Multi-modal features for CommercialMemorability Prediction
  Aleksandar Pramov

• Exploring Visual, Textual, And Engagement Features forMemorability Predictions
  Mahnoor Adeel, Kisa Fatima, Muhammad Ibrahim Ayoubi, Mustafa Usmani, Muhammad Atif Tahir

MultiSumm

Overview paper:

• MultiSumm: An AI-Powered System for Automated MultimodalNews Summarization
  Dhannya S M, Lakshmi Priya S, Shalini M, AvanthikaVijayakumar, Ranjan S

Working notes papers:

• LLM in Multi-Document Summarisation : MultiSumm Task atMediaEval 2025
  Anastasiia Potiagalova, Gareth Jones

Quest for insight papers:

• MultiSumm - Multimodal Summarisation Task at MediaEval 2025
  Anastasiia Potyagalova, Gareth Jones