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Computer Vision – ECCV 2024 : 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part XXIII / 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 ; 15081
- 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 (581 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:
- Weak-to-Strong Compositional Learning from Generative Models for Language-based Object Detection
- Domesticating SAM for Breast Ultrasound Image Segmentation via Spatial-frequency Fusion and Uncertainty Correction
- CanonicalFusion: Generating Drivable 3D Human Avatars from Multiple Images
- Camera Height Doesn't Change: Unsupervised Training for Metric Monocular Road-Scene Depth Estimation
- Uni3DL: A Unified Model for 3D Vision-Language Understanding
- Object-Aware NIR-to-Visible Translation
- PaPr: Training-Free One-Step Patch Pruning with Lightweight ConvNets for Faster Inference
- GENIXER: Empowering Multimodal Large Language Models as a Powerful Data Generator
- BLINK: Multimodal Large Language Models Can See but Not Perceive
- AFF-ttention! Affordances and Attention models for Short-Term Object Interaction Anticipation
- PreLAR: World Model Pre-training with Learnable Action Representation
- Multi-HMR: Multi-Person Whole-Body Human Mesh Recovery in a Single Shot
- De-confounded Gaze Estimation
- Diffusion Models for Monocular Depth Estimation: Overcoming Challenging Conditions
- FreestyleRet: Retrieving Images from Style-Diversified Queries
- ReGround: Improving Textual and Spatial Grounding at No Cost
- CardiacNet: Learning to Reconstruct Abnormalities for Cardiac Disease Assessment from Echocardiogram Videos
- LaMI-DETR: Open-Vocabulary Detection with Language Model Instruction
- Unrolled Decomposed Unpaired Learning for Controllable Low-Light Video Enhancement
- Efficient Image Pre-Training with Siamese Cropped Masked Autoencoders
- VP-SAM: Taming Segment Anything Model for Video Polyp Segmentation via Disentanglement and Spatio-temporal Side Network
- Dataset Enhancement with Instance-Level Augmentations
- FreeMotion: MoCap-Free Human Motion Synthesis with Multimodal Large Language Models
- Chameleon: A Data-Efficient Generalist for Dense Visual Prediction in the Wild
- Reliability in Semantic Segmentation: Can We Use Synthetic Data?
- SCPNet: Unsupervised Cross-modal Homography Estimation via Intra-modal Self-supervised Learning
- SCAPE: A Simple and Strong Category-Agnostic Pose Estimator.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 3-031-73337-1
- OCLC:
- 1467875289
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