Muutke küpsiste eelistusi

Computer Vision ECCV 2024: 18th European Conference, Milan, Italy, September 29October 4, 2024, Proceedings, Part XX 2025 ed. [Pehme köide]

Edited by , Edited by , Edited by , Edited by , Edited by , Edited by
  • Formaat: Paperback / softback, 489 pages, kõrgus x laius: 235x155 mm, 172 Illustrations, color; 205 Illustrations, black and white; LXXXV, 489 p. 377 illus., 172 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 15078
  • Ilmumisaeg: 27-Nov-2024
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 303172660X
  • ISBN-13: 9783031726606
  • Pehme köide
  • Hind: 141,35 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Tavahind: 166,29 €
  • Säästad 15%
  • Raamatu kohalejõudmiseks kirjastusest kulub orienteeruvalt 2-4 nädalat
  • Kogus:
  • Lisa ostukorvi
  • Tasuta tarne
  • Tellimisaeg 2-4 nädalat
  • Lisa soovinimekirja
  • Formaat: Paperback / softback, 489 pages, kõrgus x laius: 235x155 mm, 172 Illustrations, color; 205 Illustrations, black and white; LXXXV, 489 p. 377 illus., 172 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 15078
  • Ilmumisaeg: 27-Nov-2024
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 303172660X
  • ISBN-13: 9783031726606
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 29October 4, 2024.





The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. The papers 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.
Train Till You Drop: Towards Stable and Robust Source-free Unsupervised
3D Domain Adaptation.- Learning to Obstruct Few-Shot Image Classification
over Restricted Classes.- RoofDiffusion:  Constructing Roofs from Severely
Corrupted Point Data via Diffusion.- L-DiffER: Single Image Reflection
Removal with Language-based Diffusion Model.- AdaShield: Safeguarding
Multimodal Large Language Models from Structure-based Attack via Adaptive
Shield Prompting.- OccGen: Generative Multi-modal 3D Occupancy Prediction for
Autonomous Driving.- CrossGLG: LLM Guides One-shot Skeleton-based 3D Action
Recognition in a Cross-level Manner.- HYDRA: A Hyper Agent for Dynamic
Compositional Visual Reasoning.- BrushNet: A Plug-and-Play Image Inpainting
Model with Decomposed Dual-Branch Diffusion.- LayoutDETR: Detection
Transformer Is a Good Multimodal Layout Designer.- Blind image deblurring
with noise-robust kernel estimation.- Binomial Self-compensation for Motion
Error in Dynamic 3D Scanning.- AddMe: Zero-shot Group-photo Synthesis by
Inserting People into Scenes.- Distill Gold from Massive Ores: Bi-level Data
Pruning towards Efficient Dataset Distillation.- VersatileGaussian: Real-time
Neural Rendering for Versatile Tasks using Gaussian Splatting.- Momentum
Auxiliary Network for Supervised Local Learning.- HPFF: Hierarchical Locally
Supervised Learning with Patch Feature Fusion.- Rethinking LiDAR Domain
Generalization: Single Source as Multiple Density Domains.- Improving
Zero-Shot Generalization for CLIP with Variational Adapter.- Realistic Human
Motion Generation with Cross-Diffusion Models.- EgoExo-Fitness: Towards
Egocentric and Exocentric Full-Body Action Understanding.- Any Target Can be
Offense: Adversarial Example Generation via Generalized Latent Infection.-
Towards Reliable Advertising Image Generation Using Human Feedback.-
Topology-Preserving Downsampling of Binary Images.- ColorMAE: Exploring
data-independent masking strategies in Masked AutoEncoders.- Classification
Matters: Improving Video Action Detection with Class-Specific Attention.-
Improving Medical Multi-modal Contrastive Learning with Expert Annotations.