Leonardis / Ricci / Varol | Computer Vision ¿ ECCV 2024 | Buch | 978-3-031-72760-3 | sack.de

Buch, Englisch, Band 15093, 478 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 844 g

Reihe: Lecture Notes in Computer Science

Leonardis / Ricci / Varol

Computer Vision ¿ ECCV 2024

18th European Conference, Milan, Italy, September 29 ¿ October 4, 2024, Proceedings, Part XXXV
2025
ISBN: 978-3-031-72760-3
Verlag: Springer Nature Switzerland

18th European Conference, Milan, Italy, September 29 ¿ October 4, 2024, Proceedings, Part XXXV

Buch, Englisch, Band 15093, 478 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 844 g

Reihe: Lecture Notes in Computer Science

ISBN: 978-3-031-72760-3
Verlag: Springer Nature Switzerland


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; object recognition; motion estimation.
Leonardis / Ricci / Varol Computer Vision ¿ ECCV 2024 jetzt bestellen!

Zielgruppe


Research

Weitere Infos & Material


Visual Prompting via Partial Optimal Transport.- Modelling Competitive Behaviors in Autonomous Driving Under Generative World Model.- Tendency-driven Mutual Exclusivity for Weakly Supervised Incremental Semantic Segmentation.- AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection.- Pathformer3D: A 3D Scanpath Transformer for 360° Images.- TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detection.- SparseLIF: High-Performance Sparse LiDAR-Camera Fusion for 3D Object Detection.- 3D Gaussian Parametric Head Model.- RING-NeRF : Rethinking Inductive Biases for Versatile and Efficient Neural Fields.- Platypus: A Generalized Specialist Model for Reading Text in Various Forms.- Structured-NeRF: Hierarchical Scene Graph with Neural Representation.- EGIC: Enhanced Low-Bit-Rate Generative Image Compression Guided by Semantic Segmentation.- Plug-and-Play Learned Proximal Trajectory for 3D Sparse-View X-Ray Computed Tomography.- PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Driving.- Test-Time Stain Adaptation with Diffusion Models for Histopathology Image Classification.- Beyond MOT: Semantic Multi-Object Tracking.- Temporal Event Stereo via Joint Learning with Stereoscopic Flow.- SAM-COD: SAM-guided Unified Framework for Weakly-Supervised Camouflaged Object Detection.- Just a Hint: Point-Supervised Camouflaged Object Detection.- ManiGaussian: Dynamic Gaussian Splatting for Multi-task Robotic Manipulation.- Global-Local Collaborative Inference with LLM for Lidar-Based Open-Vocabulary Detection.- Learning High-resolution Vector Representation from Multi-Camera Images for 3D Object Detection.- View-Consistent 3D Editing with Gaussian Splatting.- E3V-K5: An Authentic Benchmark for Redefining Video-Based Energy Expenditure Estimation.- GeoGaussian: Geometry-aware Gaussian Splatting for Scene Rendering.- URS-NeRF: Unordered  Rolling  Shutter Bundle Adjustment  for Neural Radiance Fields.



Ihre Fragen, Wünsche oder Anmerkungen
Vorname*
Nachname*
Ihre E-Mail-Adresse*
Kundennr.
Ihre Nachricht*
Lediglich mit * gekennzeichnete Felder sind Pflichtfelder.
Wenn Sie die im Kontaktformular eingegebenen Daten durch Klick auf den nachfolgenden Button übersenden, erklären Sie sich damit einverstanden, dass wir Ihr Angaben für die Beantwortung Ihrer Anfrage verwenden. Selbstverständlich werden Ihre Daten vertraulich behandelt und nicht an Dritte weitergegeben. Sie können der Verwendung Ihrer Daten jederzeit widersprechen. Das Datenhandling bei Sack Fachmedien erklären wir Ihnen in unserer Datenschutzerklärung.