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Computer Vision – ECCV 2024 : 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part V / 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 ; 15063
- 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.
- Image Processing.
- 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.
- Image Processing.
- Computer Communication Networks.
- User Interfaces and Human Computer Interaction.
- Machine Learning.
- Special Purpose and Application-Based Systems.
- Physical Description:
- 1 online resource (563 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:
- SignAvatars: A Large-scale 3D Sign Language Holistic Motion Dataset and Benchmark
- AttnZero: Efficient Attention Discovery for Vision Transformers
- Auto-GAS: Automated Proxy Discovery for Training-free Generative Architecture Search
- Auto-DAS: Automated Proxy Discovery for Training-free Distillation-aware Architecture Search
- UniDream: Unifying Diffusion Priors for Relightable Text-to-3D Generation
- TimeCraft: Navigate Weakly-Supervised Temporal Grounded Video Question Answering via Bi-directional Reasoning
- Spectral Subsurface Scattering for Material Classification
- nuCraft: Crafting High Resolution 3D Semantic Occupancy for Unified 3D Scene Understanding
- Dynamic Neural Radiance Field From Defocused Monocular Video
- PiTe: Pixel-Temporal Alignment for Large Video-Language Model
- CarFormer: Self-Driving with Learned Object-Centric Representations
- FreeDiff: Progressive Frequency Truncation for Image Editing with Diffusion Models
- Plain-Det: A Plain Multi-Dataset Object Detector
- Alternate Diverse Teaching for Semi-supervised Medical Image Segmentation
- Cs2K: Class-specific and Class-shared Knowledge Guidance for Incremental Semantic Segmentation
- Synchronous Diffusion for Unsupervised Smooth Non-Rigid 3D Shape Matching
- Text-Guided Video Masked Autoencoder
- Diffusion Models for Open-Vocabulary Segmentation
- Textual-Visual Logic Challenge: Understanding and Reasoning in Text-to-Image Generation
- EvSign: Sign Language Recognition and Translation with Streaming Events
- QUAR-VLA: Vision-Language-Action Model for Quadruped Robots
- Zero-shot Object Counting with Good Exemplars
- TextDiffuser-2: Unleashing the Power of Language Models for Text Rendering
- SFPNet: Sparse Focal Point Network for Semantic Segmentation on General LiDAR Point Clouds
- PartSTAD: 2D-to-3D Part Segmentation Task Adaptation
- FutureDepth: Learning to Predict the Future Improves Video Depth Estimation
- LLM as Copilot for Coarse-grained Vision-and-Language Navigation.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 3-031-72652-9
- OCLC:
- 1465274998
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