Computer Vision – ECCV 2024 : 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part LXVIII / / 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 (569 pages)
• Disciplina: 006.37
• Soggetto topico: Image processing - Digital techniques; Computer vision; Image processing; Computer networks; User interfaces (Computer systems); Human-computer interaction; Machine learning; Computers, Special purpose; Computer Imaging, Vision, Pattern Recognition and Graphics; Image Processing; Computer Communication Networks; User Interfaces and Human Computer Interaction; Machine Learning; Special Purpose and Application-Based Systems
• Altri autori: RicciElisa RothȘtefan RussakovskyOlga SattlerTorsten VarolGül
• Nota di contenuto: Reinforcement Learning Friendly Vision-Language Model for Minecraft -- Pseudo-RIS: Distinctive Pseudo-supervision Generation for Referring Image Segmentation -- Training-free Composite Scene Generation for Layout-to-Image Synthesis -- Robustness Preserving Fine-tuning using Neuron Importance -- ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation -- PEA-Diffusion: Parameter-Efficient Adapter with Knowledge Distillation in non-English Text-to-Image Generation -- Similarity of Neural Architectures using Adversarial Attack Transferability -- Dual-Rain: Video Rain Removal using Assertive and Gentle Teachers -- PMT: Progressive Mean Teacher via Exploring Temporal Consistency for Semi-Supervised Medical Image Segmentation -- OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web -- AutoEval-Video: An Automatic Benchmark for Assessing Large Vision Language Models in Open-Ended Video Question Answering -- Reflective Instruction Tuning: Mitigating Hallucinations in Large Vision-Language Models -- Unsupervised Variational Translator for Bridging Image Restoration and High-Level Vision Tasks -- Diffusion Model for Robust Multi-Sensor Fusion in 3D Object Detection and BEV Segmentation -- MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos -- Fast Point Cloud Geometry Compression with Context-based Residual Coding and INR-based Refinement -- Scene-Conditional 3D Object Stylization and Composition -- GenView: Enhancing View Quality with Pretrained Generative Model for Self-Supervised Learning -- Revisit Anything: Visual Place Recognition via Image Segment Retrieval -- EcoMatcher: Efficient Clustering Oriented Matcher for Detector-free Image Matching -- DGD: Dynamic 3D Gaussians Distillation -- Semantic Diversity-aware Prototype-based Learning for Unbiased Scene Graph Generation -- DiffuMatting: Synthesizing Arbitrary Objects with Matting-level Annotation -- Self-Guided Generation of Minority Samples Using Diffusion Models -- DEVIAS: Learning Disentangled Video Representations of Action and Scene -- AD3: Introducing a score for Anomaly Detection Dataset Difficulty assessment using VIADUCT dataset -- RoomTex: Texturing Compositional Indoor Scenes via Iterative Inpainting.
• 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: 9783031731136; 3031731131
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910983385603321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Computer Science, . 1611-3349 ; ; 15126