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

Format:

Book

Author/Creator:

Leonardis, Aleš.

Contributor:

Ricci, Elisa.

Roth, Ștefan.

Russakovsky, Olga.

Sattler, Torsten.

Varol, Gül.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 15122

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 (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:

Depth-guided NeRF Training via Earth Mover’s Distance

INTRA: Interaction Relationship-aware Weakly Supervised Affordance Grounding

DEPICT: Diffusion-Enabled Permutation Importance for Image Classification Tasks

Meerkat: Audio-Visual Large Language Model for Grounding in Space and Time

Diagnosing and Re-learning for Balanced Multimodal Learning

Contribution-based Low-Rank Adaptation with Pre-training Model for Real Image Restoration

Elucidating the Hierarchical Nature of Behavior with Masked Autoencoders

BeyondScene: Higher-Resolution Human-Centric Scene Generation With Pretrained Diffusion

SpaRP: Fast 3D Object Reconstruction and Pose Estimation from Sparse Views

MMEarth: Exploring Multi-Modal Pretext Tasks For Geospatial Representation Learning

Discovering Unwritten Visual Classifiers with Large Language Models

LITA: Language Instructed Temporal-Localization Assistant

MARs: Multi-view Attention Regularizations for Patch-based Feature Recognition of Space Terrain

Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs

Bridging the Pathology Domain Gap: Efficiently Adapting CLIP for Pathology Image Analysis with Limited Labeled Data

AugUndo: Scaling Up Augmentations for Monocular Depth Completion and Estimation

CARB-Net: Camera-Assisted Radar-Based Network for Vulnerable Road User Detection

SAH-SCI: Self-Supervised Adapter for Efficient Hyperspectral Snapshot Compressive Imaging

Minimalist Vision with Freeform Pixels

All You Need is Your Voice: Emotional Face Representation with Audio Perspective for Emotional Talking Face Generation

LatentEditor: Text Driven Local Editing of 3D Scenes

Single-Photon 3D Imaging with Equi-Depth Photon Histograms

Asynchronous Bioplausible Neuron for Spiking Neural Networks for Event-Based Vision

Viewpoint textual inversion: discovering scene representations and 3D view control in 2D diffusion models

POET: Prompt Offset Tuning for Continual Human Action Adaptation

Domain Generalization of 3D Object Detection by Density-Resampling

IG Captioner: Information Gain Captioners are Strong Zero-shot Classifiers.

ISBN:

3-031-73039-9