Computer Vision – ECCV 2024 Workshops

Self-supervised disentangled representation learning of artistic style through Neural Style Transfer.- Similar paintings retrieval from individual and multiple poses.- NeAT: Neural Artistic Tracing for high resolution Style Transfer.- DIFF-NST: Diffusion Interleaving For deFormable Neural Style Transfer.- Analysis of Hybrid Compositions in Animation Film with Weakly Supervised Learning.- BackFlip: The Impact of Local and Global Data Augmentations on Artistic Image Aesthetic Assessment.- Cultural Heritage 3D Reconstruction with Diffusion Networks.- Context-Infused Visual Grounding for Art.- ColorwAI: Generative Colorways of Textiles through GAN and Diffusion Disentanglement.- Novel Artistic Scene-Centric Datasets for Effective Transfer Learning in Fragrant Spaces.- Evaluating Usability and Engagement of Large Language Models in Virtual Reality for Traditional Scottish Curling.- EUFCC-CIR: a composed image retrieval dataset for GLAM collections.- µgat: Improving Single-Page Document Parsing by Providing Multi-Page Context.- Pixels of Faith: Exploiting Visual Saliency to Detect Religious Image Manipulation.- Automatic Die Studies for Ancient Numismatics.- San Vitale Challenge: Automatic Reconstruction of Ancient Colored Glass Windows.- The Role of Generative Systems in Historical Photography Management: A Case Study on Catalan Archives.- An approach for dataset extension for object detection in artworks using open-vocabulary models.- A Data-Centric Module for Neural Rendering.- Structured Analysis of Alphabets in Historical Handwritten Ciphers.- Visual Motif Identification: Elaboration of a Curated Comparative Dataset and Classification Methods.