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Computational Visual Media: 13th International Conference, CVM 2025, Hong Kong SAR, China, April 1921, 2025, Proceedings, Part III [Mīkstie vāki]

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  • Formāts: Paperback / softback, 458 pages, height x width: 235x155 mm, 157 Illustrations, color; 5 Illustrations, black and white; XVII, 458 p. 162 illus., 157 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 15665
  • Izdošanas datums: 26-Apr-2025
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 9819658144
  • ISBN-13: 9789819658145
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Computational Visual Media: 13th International Conference, CVM 2025, Hong Kong SAR, China, April 1921, 2025, Proceedings, Part IIIPalielināt
  • Formāts: Paperback / softback, 458 pages, height x width: 235x155 mm, 157 Illustrations, color; 5 Illustrations, black and white; XVII, 458 p. 162 illus., 157 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 15665
  • Izdošanas datums: 26-Apr-2025
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 9819658144
  • ISBN-13: 9789819658145
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9789819658145.html
This book constitutes the refereed proceedings of CVM 2025, the 13th International Conference on Computational Visual Media, held in Hong Kong SAR, China, in April 2025.



The 67 full papers were carefully reviewed and selected from 335 submissions. The papers are organized in topical sections as follows:



Part I: Medical Image Analysis, Detection and Recognition, Image Enhancement and Generation, Vision Modeling in Complex Scenarios



Part II: 3D Geometry and Rendering, Generation and Editing, Image Processing and Optimization



Part III: Image and Video Analysis, Multimodal Learning, Geometrical Processing, Applications
Image and Video Analysis


DepthFisheye: Efficient Fine-Tuning of Depth Estimation Models for Fisheye
Cameras.- DIMATrack: Dimension Aware Data Association for Multi-Object
Tracking.- Efficient Transformer Network for Visible and Ultraviolet Object
Tracking.- LightGR-Transformer: Light Grouped Residual Transformer for
Multispectral Object Detection.- ADMMOA: Attribute-Driven Multimodal
Optimization for Face Recognition Adversarial Attacks.- Training-Free
Language-Guided Video Summarization via Multi-Grained Saliency Scoring.- 


Multimodal Learning


Reinforced Label Denoising for Weakly-Supervised Audio-Visual Video Parsing.-
Bridging the Modality Gap: Advancing Multimodal Human Pose Estimation with
Modality-Adaptive Pose Estimator and Novel Benchmark Datasets.-
Momentum-Based Uni-Modal Soft-Label Alignment and Multi-Modal Latent
Projection Networks for Optimizing Image-Text Retrieval.- Multi-Granularity
and Multi-Modal Prompt Learning for Person Re-Identification.- Local and
Global Feature Cross-attention Multimodal Place Recognition.- IML-CMM - A
Multimodal Sentiment Analysis Framework Integrating Intra-Modal Learning and
Cross-Modal Mixup Enhancement.- 


Geometrical Processing


MCFG with GUMAP: A Simple and Effective Clustering Framework on Grassmann
Manifold.- Joint UMAP for Visualization of Time-Dependent Data.- Unsupervised
Domain Adaptation on Point Cloud Classification via Imposing Structural
Manifolds into Representation Space.- 


Applications


Learning Adaptive Basis Fonts to Fuse Content Features for Few-shot Font
Generation.- TaiCrowd: A High-Performance Simulation Framework for Massive
Crowd.-Feature Disentanglement and Fusion Model for Multi-Source Domain
Adaptation with Domain-Specific Features.- A Trademark Retrieval Method Based
on Self-Supervised Learning.- Weaken Noisy Feature: Boosting Semi-Supervised
Learning by Noise Estimation.- Multi-Dimension Full Scene Integrated Visual
Emotion Analysis Network.- Gap-KD: Bridging the Significant Capacity Gap
Between Teacher and Student Model.