Buch, Englisch, 382 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 663 g
Milan, Italy, September 29-October 4, 2024, Proceedings, Part XVIII
Buch, Englisch, 382 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 663 g
Reihe: Lecture Notes in Computer Science
ISBN: 978-3-031-91671-7
Verlag: Springer International Publishing
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
- Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion
Weitere Infos & Material
.- DeepClean: Machine Unlearning on the Cheap by Resetting Privacy Sensitive Weights using the Fisher Diagonal.
.- Prompt Sliders for Fine-Grained Control, Editing and Erasing of Concepts in Diffusion Models.
.- Aligning Vision Language Models with Contrastive Learning.
.- Open-set object detection: towards unified problem formulation and benchmarking.
.- Open-Vocabulary Object Detectors: Robustness Challenges under Distribution Shifts.
.- SOOD-ImageNet: a Large-Scale Dataset for Semantic Out-Of-Distribution Image Classification and Semantic Segmentation.
.- Online Stochastic Optimization for Data with Temporal Dependencies.
.- A Lost Opportunity for Vision-Language Models: A Comparative Study of Online Test-Time Adaptation for Vision-Language Models.
.- OSSA: Unsupervised One-Shot Style Adaptation.
.- ZoDi: Zero-Shot Domain Adaptation with Diffusion-Based Image Transfer.
.- Open-set Plankton Recognition.
.- Do Vision Foundation Models Enhance Domain Generalization in Medical Image Segmentation?.
.- On the Potential of Open-Vocabulary Models for Object Detection in Unusual Street Scenes.
.- Source-Free Domain Adaptation for YOLO Object Detection.
.- Task-Specific Adaptation of Segmentation Foundation Model via Prompt Learning.
.- Utilizing Class-Agnostic Point-to-Box Regressors as Object Proposal Generators.
.- Introducing a Class-Aware Metric for Monocular Depth Estimation: An Automotive Perspective.
.- Improving Generalization in Visual Reasoning via Self-Ensemble.
.- BelHouse3D: A Benchmark Dataset for Assessing Occlusion Robustness in 3D Point Cloud Semantic Segmentation.
.- Image Translation with Kernel Prediction Networks for Semantic Segmentation.
.- Robust fine-tuning and adaptation of zero-shot models via adaptive weightspace ensembling.
.- Robustness to Spurious Correlation: A Comprehensive Review.
