Computer Vision – ECCV 2024 Workshops Milan, Italy, September 29–October 4, 2024, Proceedings, Part XVIII /
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...
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| Format: | Elektroniczne E-book |
| Język: | angielski |
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Springer Nature Switzerland : Imprint: Springer,
2025.
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| Wydanie: | 1st ed. 2025. |
| Seria: | Lecture Notes in Computer Science,
15640 |
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| Dostęp online: | https://doi.org/10.1007/978-3-031-91672-4 |
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| 245 | 1 | 0 | |a Computer Vision – ECCV 2024 Workshops |h [electronic resource] : |b Milan, Italy, September 29–October 4, 2024, Proceedings, Part XVIII / |c edited by Alessio Del Bue, Cristian Canton, Jordi Pont-Tuset, Tatiana Tommasi. |
| 250 | |a 1st ed. 2025. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2025. | |
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| 490 | 1 | |a Lecture Notes in Computer Science, |x 1611-3349 ; |v 15640 | |
| 505 | 0 | |a -- 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. | |
| 520 | |a 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. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 650 | 0 | |a Image processing |x Digital techniques. | |
| 650 | 0 | |a Computer vision. | |
| 650 | 0 | |a Image processing. | |
| 650 | 0 | |a User interfaces (Computer systems). | |
| 650 | 0 | |a Human-computer interaction. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Computers, Special purpose. | |
| 650 | 1 | 4 | |a Computer Imaging, Vision, Pattern Recognition and Graphics. |
| 650 | 2 | 4 | |a Image Processing. |
| 650 | 2 | 4 | |a User Interfaces and Human Computer Interaction. |
| 650 | 2 | 4 | |a Machine Learning. |
| 650 | 2 | 4 | |a Special Purpose and Application-Based Systems. |
| 700 | 1 | |a Del Bue, Alessio. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Canton, Cristian. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Pont-Tuset, Jordi. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Tommasi, Tatiana. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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