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
Diffusion-based Light Field Synthesis.- Diffusion-Promoted HDR Video Reconstruction.- Lightweight Deep Learning Model for Defective Pixel Detection and Recovery from the Image Sensors.- IPAdapter-Instruct: Resolving Ambiguity in Image-based Conditioning using Instruct Prompts.- Frequency-Aware Guidance for Blind Image Restoration via Diffusion Models.- MM2Latent: Text-to-facial image generation and editing in GANs with multimodal assistance.- RMT-BVQA: Recurrent Memory Transformer-based Blind Video Quality Assessment for Enhanced Video Content.- Detecting Forged Sentinel-2 Images Through Parallax-Based Cloud Analysis.- PRISM: Progressive Restoration for Scene Graph-based Image Manipulation.- DAVIDE: Depth-Aware Video Deblurring.- RenDetNet: Weakly-supervised Shadow Detection with Shadow Caster Verification.- Higher fidelity perceptual image and video compression with a latent conditioned residual denoising diffusion model.- Autoregressive High-Order Finite Difference Modulo Imaging: High-Dynamic Range for Computer Vision Applications.- QSD: Query-Selection Denoising score for Image Edit-ing in Latent Diffusion Model.- PDB Unet: A spatio temporal video Fixed Pattern Noise removal network.- Reversible and Cascaded Lightweight Colour Constancy: Jointly Addressing Illumination Correction and White Balance.- Hybrid Spatial-spectral Neural Network for Hyperspectral Image Denoising.- Solving Inverse Problem With Unspecified Forward Operator Using Diffusion Models.- A Disentangled Approach to Predict the Aesthetic Outcomes of Breast Cancer Treatment.- LAR-IQA: A Lightweight, Accurate, and Robust No-Reference Image Quality Assessment Model.- Pushing Joint Image Denoising and Classification to the Edge.- Self-Supervised HDR Imaging from Motion and Exposure Cues.- Closer to Ground Truth: Realistic Shape and Appearance Labeled Data Generation for Unsupervised Underwater Image Segmentation.- Edge-aware Consistent Stereo Video Depth Estimation.- Low-Cost Stereoscopic Optical-Coding Design for Depth Estimation Using End-to-End Optimization.- 360U-Former: HDR Illumination Estimation with Panoramic Adapted Vision Transformers.- Satellite Image Dehazing Via Masked Image Modeling and Jigsaw Transformation.- UHD-IQA Benchmark Database: Pushing the Boundaries of Blind Photo Quality Assessment.
