Full Description
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 29-October 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.
Contents
.- TONO: a synthetic dataset for face image compliance to ISO/ICAO standard.
.- mproving Post-Earthquake Crack Detection using Semi-Synthetic Gener ated Images.
.- DiffAugment: Diffusion based Long-Tailed Visual Relationship Recognition.
.- Neural Transcoding Vision Transformers for EEG-to-fMRI Synthesis.
.- RoCOCO: Robustness Benchmark of MS-COCO to Stress-test Image-Text Matching Models.
.- NeRFmentation: NeRF-based Augmentation for Monocular Depth Estima tion.
.- Synthetic to Authentic: Transferring Realism to 3D Face Renderings for Boosting Face Recognition.
.- Time-Resolved MNIST Dataset for Single-Photon Recognition.
.- NToP: NeRF-Powered Large-scale Dataset Generation for 2D and 3D Hu man Pose Estimation in Top-View Fisheye Images.
.- Training and Benchmarking Leukocyte Sub-types Classification Methods with Synthetic Images.
.- DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance Scaling.
.- Contextual Knowledge Pursuit for Faithful Visual Synthesis.
.- SurgicaL-CD: Generating Surgical Images via Unpaired Image Translation with Latent Consistency Diffusion Models.
.- Diffusion-based Synthetic Dataset Generation for Egocentric 3D Human Pose Estimation.
.- BootPIG: Bootstrapping Zero-shot Personalized Image Generation Capabil ities in Pretrained Diffusion Models.
.- A CycleGAN Model to Synthesize Missing and Unpaired MRI Sequences for Under-Represented Multiple Sclerosis Lesions.
.- The Impact of Balancing Real and Synthetic Data on Accuracy and Fairness in Face Recognition.
.- DreamTexture: High-Fidelity Synthetic 3D Data Generation through De coupled Geometry and Texture Synthesis.
.- Control+Shift: Generating Controllable Distribution Shifts.
.- Comparative Analysis of Synthetic and Real Melanoma Images in AI-Driven Diagnosis.
.- How Knowledge Distillation Mitigates the Synthetic Gap in Fair Face Recog nition.
.- Synthetic Generation of Dermatoscopic Images with GAN and Closed-Form Factorization.
.- FABRIC: Personalizing Diffusion Models with Iterative Feedback.
.- TaskCLIP: Extend Large Vision-Language Model for Task Oriented Object Detection.
