Computer Vision - ECCV 2024 Workshops

The multi-volume set LNCS 15623 until LNCS 15646 constitutes the proceedings of the workshops that were held in conjunction with the 18th European Conference on Computer Vision, ECCV 2024, which took place in Milan, Italy, during September 29October 4, 2024.

These LNCS volumes contain 574 accepted papers from 53 of the 73 workshops. The list of workshops and distribution of the workshop papers in the LNCS volumes can be found in the preface that is freely accessible online.

Inhalt

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.