Computer Vision – ECCV 2024 : 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part XLVIII / / edited by Aleš Leonardis, Elisa Ricci, Stefan Roth, Olga Russakovsky, Torsten Sattler, Gül Varol

• Autore: Leonardis Aleš
• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025
• Edizione: 1st ed. 2025.
• Descrizione fisica: 1 online resource (571 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: SmartControl: Enhancing ControlNet for Handling Rough Visual Conditions -- InterFusion: Text-Driven Generation of 3D Human-Object Interaction -- GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook Retrieval -- DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving -- Flow-Assisted Motion Learning Network for Weakly-Supervised Group Activity Recognition -- NeRF-XL: NeRF at Any Scale with Multi-GPU -- CoSIGN: Few-Step Guidance of ConSIstency Model to Solve General INverse Problems -- The First to Know: How Token Distributions Reveal Hidden Knowledge in Large Vision-Language Models? -- Compositional Substitutivity of Visual Reasoning for Visual Question Answering -- LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Models -- DNI: Dilutional Noise Initialization for Diffusion Video Editing -- Two-Stage Video Shadow Detection via Temporal-Spatial Adaption -- Towards Physical World Backdoor Attacks against Skeleton Action Recognition -- SAM-guided Graph Cut for 3D Instance Segmentation -- Fully Authentic Visual Question Answering Dataset from Online Communities -- Active Generation for Image Classification -- FuseTeacher: Modality-fused Encoders are Strong Vision Supervisors -- Learning Local Pattern Modularization for Point Cloud Reconstruction from Unseen Classes -- Understanding Multi-compositional learning in Vision and Language models via Category Theory -- FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients -- Panel-Specific Degradation Representation for Raw Under-Display Camera Image Restoration -- Unlocking Textual and Visual Wisdom: Open-Vocabulary 3D Object Detection Enhanced by Comprehensive Guidance from Text and Image -- Diffusion-Guided Weakly Supervised Semantic Segmentation -- Weakly-Supervised Spatio-Temporal Video Grounding with Variational Cross-Modal Alignment -- When Pedestrian Detection Meets Multi-Modal Learning: Generalist Model and Benchmark Dataset -- NVS-Adapter: Plug-and-Play Novel View Synthesis from a Single Image -- Segment and Recognize Anything at Any Granularity.
• 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: 9783031731952; 3031731956
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910983304703321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Computer Science, . 1611-3349 ; ; 15106