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

• Record Nr.: UNINA9910983050003321
• Autore: Leonardis Aleš
• Pubbl/distr/stampa: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025
• ISBN: 9783031730214; 3031730216
• Edizione: [1st ed. 2025.]
• Descrizione fisica: 1 online resource (560 pages)
• Collana: Lecture Notes in Computer Science, , 1611-3349 ; ; 15145
• Altri autori (Persone): RicciElisa; RothȘtefan; RussakovskyOlga; SattlerTorsten; VarolGül
• Disciplina: 006.37
• Soggetti: Image processing - Digital techniques; Computer vision; Computer networks; User interfaces (Computer systems); Human-computer interaction; Machine learning; Computers, Special purpose; Computer Imaging, Vision, Pattern Recognition and Graphics; Computer Communication Networks; User Interfaces and Human Computer Interaction; Machine Learning; Special Purpose and Application-Based Systems
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Score Distillation Sampling with Learned Manifold Corrective -- FipTR: A Simple yet Effective Transformer Framework for Future Instance Prediction in Autonomous Driving -- Benchmarking the Robustness of ; Cross-view Geo-localization Models -- GroCo: Ground Constraint for Metric Self-Supervised Monocular Depth -- SUMix: Mixup with Semantic and Uncertain Information -- Flatness-aware Sequential Learning Generates Resilient Backdoors -- Iterative Ensemble Training with Anti-Gradient Control for Mitigating Memorization in Diffusion Models -- IFTR: An Instance-Level Fusion Transformer for Visual Collaborative Perception -- DiffClass: Diffusion-Based Class Incremental Learning -- Convex Relaxations for Manifold-Valued Markov Random Fields with Approximation Guarantees -- Instant 3D Human Avatar Generation using Image Diffusion Models -- PromptFusion: Decoupling Stability and Plasticity for Continual Learning -- Improving Geo-diversity of Generated Images with Contextualized Vendi Score Guidance -- Adapting to Shifting Correlations with Unlabeled Data Calibration -- Masked Generative Video-to-Audio Transformers with Enhanced Synchronicity -- Information Bottleneck Based Data Correction in Continual Learning -- On Spectral Properties of Gradient-based Explanation Methods -- Contextual Correspondence Matters: Bidirectional Graph Matching for Video Summarization -- O2V-Mapping: Online Open-Vocabulary Mapping with Neural Implicit Representation -- Dataset Distillation by Automatic Training Trajectories -- FAFA: Frequency-Aware Flow-Aided Self-Supervision for Underwater Object Pose Estimation -- EMIE-MAP: Large-Scale Road Surface Reconstruction Based on Explicit Mesh and Implicit Encoding -- UniIR: Training and Benchmarking Universal Multimodal Information Retrievers -- SSL-Cleanse: Trojan Detection and Mitigation in Self-Supervised Learning -- Skews in the Phenomenon Space Hinder Generalization in Text-to-Image Generation -- Bones Can't Be Triangles: Accurate and Efficient Vertebrae Keypoint Estimation through Collaborative Error Revision -- latentSplat: Autoencoding Variational Gaussians for Fast Generalizable 3D Reconstruction.
• 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.