Del Bue / Tommasi / Canton | Computer Vision - ECCV 2024 Workshops | Buch | 978-3-031-92590-0 | sack.de

Buch, Englisch, 472 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 791 g

Reihe: Lecture Notes in Computer Science

Del Bue / Tommasi / Canton

Computer Vision - ECCV 2024 Workshops

Milan, Italy, September 29-October 4, 2024, Proceedings, Part XII
Erscheinungsjahr 2025
ISBN: 978-3-031-92590-0
Verlag: Springer Nature Switzerland

Milan, Italy, September 29-October 4, 2024, Proceedings, Part XII

Buch, Englisch, 472 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 791 g

Reihe: Lecture Notes in Computer Science

ISBN: 978-3-031-92590-0
Verlag: Springer Nature Switzerland


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.

Del Bue / Tommasi / Canton Computer Vision - ECCV 2024 Workshops jetzt bestellen!

Zielgruppe


Research

Weitere Infos & Material


Modelling the Distribution of Human Motion for Sign Language Assessment.- Enhancing Human-Robot Collaborative Search through Efficient Space Sharing with On-demand Interaction.- Context-Aware Full Body Anonymization using Text-to-Image Diffusion Models.- Hand Gesture Recognition using Dual Graph Hierarchical Edges Representation and Graph Transformer Network.- BurnSafe: Automatic Assistive Tool for Burn Severity Assessment by Semantic Segmentation.- DiffSign: AI-Assisted Generation of Customizable Sign Language Videos With Enhanced Realism.- Safe Resetless Reinforcement Learning: Enhancing Training Autonomy with Risk-Averse Agents.- Multi-view Pose Fusion for Occlusion-Aware 3D Human Pose Estimation.- HAVANA: Hierarchical stochastic neighbor embedding for Accelerated Video ANnotAtions.- Aligning Object Detector Bounding Boxes with Human Preference.- GSK-C2F: Graph Skeleton Modelization for Action Segmentation and Recognition using a Coarse-to-Fine strategy.- Machine Learning Approaches for Analyzing Physiological Data in Remote Patient Monitoring.- OPPH: A Vision-Based Operator for Measuring Body Movements for Personal Healthcare.- VLM-HOI: Vision Language Models for Interpretable Human-Object Interaction Analysis.- Video Editing for Video Retrieval.- REST–HANDS: Rehabilitation with Egocentric Vision using Smartglasses for Treatment of Hands after Surviving Stroke.- Towards Wearable Multi-Modal Human Activity Recognition with Deep Fusion Networks.- Segmenting Object Affordances: Reproducibility and Sensitivity to Scale.- Target-Oriented Object Grasping via Multimodal Human Guidance.- A Light and Smart Wearable Platform with Multimodal Foundation Model for Enhanced Spatial Reasoning.- ExeChecker: Where Did I Go Wrong?.- Assistive Visual Tool: Enhancing Safe Navigation with Video Remapping in AR Headsets.- OpenNav: Efficient Open Vocabulary 3D Object Detection for Smart Wheelchair Navigation.- BodyShapeGPT: SMPL Body Shape Manipulation with LLMs.- Photorealistic Text-to-3D Avatar Generation with Constrained Geometry and Appearance.- MCRE: Multimodal Conditional Representation and Editing for Text-Motion Generation.- Towards motion from video diffusion models.- N Heads Are Better Than One: Exploring Theoretical Performance Bounds of 3D Face Reconstruction Methods.- GECO: GPT-Driven Estimation of 3D Human-Scene Contact in the Wild.- MI-NeRF: Learning a Single NeRF for Multiple Identities.



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.