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| Autore: |
Leonardis Aleš
|
| Titolo: |
Computer Vision – ECCV 2024 : 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part LX / / edited by Aleš Leonardis, Elisa Ricci, Stefan Roth, Olga Russakovsky, Torsten Sattler, Gül Varol
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| Pubblicazione: | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
| Edizione: | 1st ed. 2025. |
| Descrizione fisica: | 1 online resource (572 pages) |
| Disciplina: | 006.37 |
| Soggetto topico: | Image processing - Digital techniques |
| Computer vision | |
| Image processing | |
| Computer networks | |
| Machine learning | |
| Computers, Special purpose | |
| User interfaces (Computer systems) | |
| Human-computer interaction | |
| Computer Imaging, Vision, Pattern Recognition and Graphics | |
| Image Processing | |
| Computer Communication Networks | |
| Machine Learning | |
| Special Purpose and Application-Based Systems | |
| User Interfaces and Human Computer Interaction | |
| Altri autori: |
RicciElisa
RothȘtefan
RussakovskyOlga
SattlerTorsten
VarolGül
|
| Nota di contenuto: | Sur^2f: A Hybrid Representation for High-Quality and Efficient Surface Reconstruction from Multi-view Images -- HO-Gaussian: Hybrid Optimization of 3D Gaussian Splatting for Urban Scenes -- Pseudo-keypoint RKHS Learning for Self-supervised 6DoF Pose Estimation -- Consistent 3D Line Mapping -- Distributed Active Client Selection With Noisy Clients Using Model Association Scores -- PixOOD: Pixel-Level Out-of-Distribution Detection -- GarmentCodeData: A Dataset of 3D Made-to-Measure Garments With Sewing Patterns -- Towards a Density Preserving Objective Function for Learning on Point Sets -- AnatoMask: Enhancing Medical Image Segmentation with Reconstruction-guided Self-masking -- VF-NeRF: Viewshed Fields for Rigid NeRF Registration -- Task-Driven Uncertainty Quantification in Inverse Problems via Conformal Prediction -- Trainable Highly-expressive Activation Functions -- Region-Aware Sequence-to-Sequence Learning for Hyperspectral Denoising -- Self-Supervised Representation Learning for Adversarial Attack Detection -- Do text-free diffusion models learn discriminative visual representations? -- Clean & Compact: Efficient Data-Free Backdoor Defense with Model Compactness -- DOCCI: Descriptions of Connected and Contrasting Images -- EAS-SNN: End-to-End Adaptive Sampling and Representation for Event-based Detection with Recurrent Spiking Neural Networks -- AttentionHand: Text-driven Controllable Hand Image Generation for 3D Hand Reconstruction in the Wild -- Dataset Quantization with Active Learning based Adaptive Sampling -- LogoSticker: Inserting Logos into Diffusion Models for Customized Generation -- LEROjD: Lidar Extended Radar-Only Object Detection -- ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative Generation -- Match-Stereo-Videos: Bidirectional Alignment for Consistent Dynamic Stereo Matching -- Probabilistic Image-Driven Traffic Modeling via Remote Sensing -- IntrinsicAnything: Learning Diffusion Priors for Inverse Rendering Under Unknown Illumination -- VideoStudio: Generating Consistent-Content and Multi-Scene Videos. |
| Sommario/riassunto: | The multi-volume set of LNCS books with volume numbers 15059 up to 15147 constitutes the refereed proceedings of the 18th European Conference on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024. The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation. |
| Titolo autorizzato: | Computer Vision – ECCV 2024 ![]() |
| ISBN: | 9783031730276 |
| 3031730275 | |
| Formato: | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9910983489403321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |