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Computer Vision – ECCV 2024 : 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part LXXX / edited by Aleš Leonardis, Elisa Ricci, Stefan Roth, Olga Russakovsky, Torsten Sattler, Gül Varol.
Springer Nature - Springer Computer Science (R0) eBooks 2025 English International Available online
View online- Format:
- Book
- Author/Creator:
- Leonardis, Aleš.
- Series:
- Lecture Notes in Computer Science, 1611-3349 ; 15138
- Language:
- English
- Subjects (All):
- Image processing--Digital techniques.
- Image processing.
- 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.
- Local Subjects:
- Computer Imaging, Vision, Pattern Recognition and Graphics.
- Computer Communication Networks.
- User Interfaces and Human Computer Interaction.
- Machine Learning.
- Special Purpose and Application-Based Systems.
- Physical Description:
- 1 online resource (577 pages)
- Edition:
- 1st ed. 2025.
- Place of Publication:
- Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
- Summary:
- 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.
- Contents:
- Ex2Eg-MAE: A Framework for Adaptation of Exocentric Video Masked Autoencoders for Egocentric Social Role Understanding
- Self-Supervised Audio-Visual Soundscape Stylization
- SAVE: Protagonist Diversification with Structure Agnostic Video Editing
- VideoAgent: Long-form Video Understanding with Large Language Model as Agent
- Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation Learning
- Source-Free Domain-Invariant Performance Prediction
- Improving Robustness to Model Inversion Attacks via Sparse Coding Architectures
- Constructing Concept-based Models to Mitigate Spurious Correlations with Minimal Human Effort
- Direct Distillation between Different Domains
- Contrastive ground-level image and remote sensing pre-training improves representation learning for natural world imagery
- V-Trans4Style: Visual Transition Recommendation for Video Production Style Adaptation
- GRiT: A Generative Region-to-text Transformer for Object Understanding
- LRSLAM: Low-rank Representation of Signed Distance Fields in Dense Visual SLAM System
- Learning Representation for Multitask Learning through Self-Supervised Auxiliary Learning
- Neural Poisson Solver: A Universal and Continuous Framework for Natural Signal Blending
- Geometry Fidelity for Spherical Images
- BAGS: Blur Agnostic Gaussian Splatting through Multi-Scale Kernel Modeling
- CroMo-Mixup: Augmenting Cross-Model Representations for Continual Self-Supervised Learning
- WoVoGen: World Volume-aware Diffusion for Controllable Multi-camera Driving Scene Generation
- Benchmarking Spurious Bias in Few-Shot Image Classifiers
- TurboEdit: Real-time text-based disentangled real image editing
- Soft Shadow Diffusion (SSD): Physics-inspired Learning for 3D Computational Periscopy
- Augmented Neural Fine-tuning for Efficient Backdoor Purification
- REDIR: Refocus-free Event-based De-occlusion Image Reconstruction
- Free-Editor: Zero-shot Text-driven 3D Scene Editing
- DPA-Net: Structured 3D Abstraction from Sparse Views via Differentiable Primitive Assembly
- An Empirical Study and Analysis of Text-to-Image Generation Using Large Language Model-Powered Textual Representation.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 3-031-72989-7
- OCLC:
- 1472988324
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