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MultiMedia Modeling: 30th International Conference, MMM 2024, Amsterdam, The Netherlands, January 29 – February 2, 2024, Proceedings, Part I de Stevan Rudinac

MultiMedia Modeling: 30th International Conference, MMM 2024, Amsterdam, The Netherlands, January 29 – February 2, 2024, Proceedings, Part I: Lecture Notes in Computer Science, cartea 14554

Editat de Stevan Rudinac, Alan Hanjalic, Cynthia Liem, Marcel Worring, Björn Þór Jónsson, Bei Liu, Yoko Yamakata
en Limba Engleză Paperback – 28 ian 2024
This book constitutes the refereed proceedings of the 30th International Conference on MultiMedia Modeling, MMM 2024, held in Amsterdam, The Netherlands, during January 29 – February 2, 2024.
The 112 full papers included in this volume were carefully reviewed and selected from 297 submissions. The MMM conference were organized in topics related to multimedia modelling, particularly: audio, image, video processing, coding and compression; multimodal analysis for retrieval applications, and multimedia fusion methods.
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Specificații

ISBN-13: 9783031533044
ISBN-10: 3031533046
Pagini: 505
Ilustrații: XVIII, 505 p. 182 illus., 171 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.73 kg
Ediția:1st ed. 2024
Editura: Springer Nature Switzerland
Colecția Springer
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

Where are Biases? Adversarial Debiasing with Spurious Feature Visualization.- Cross-Modal Hash Retrieval with Category Semantics.- Spatiotemporal Representation Enhanced ViT for Video Recognition.- SCFormer: A Vision Transformer with Split Channel in Sitting Posture Recognition.- Dive into Coarse-to-Fine Strategy in Single Image Deblurring.- TICondition: Expanding Control Capabilities for Text-to-Image Generation with Multi-Modal Conditions.- Enhancing Generative Generalized Zero Shot Learning via Multi-Space Constraints and Adapative Integration.- Joint Image Data Hiding and Rate-Distortion Optimization in Neural Compressed Latent Representations.- GSUNet: A Brain Tumor Segmentation Method Based On 3D Ghost Shuffle U-Net.- ACT: Action-associated and Target-related Representations for Object Navigation.- Foreground Feature Enhancement and Peak & Background Suppression for Fine-Grained Visual Classification.- YOLOv5-SRR: Enhancing YOLOv5 for Effective Underwater Target Detection.- Image Clustering and Generation with HDGMVAE-I.- “Car or Bus?" CLearSeg: CLIP-enhanced Discrimination among Resembling Classes for Few-Shot Semantic Segmentation.- PANDA: Prompt-based Context- and Indoor-aware Pretraining for Vision and Language Navigation.- Cross-Modal Semantic Alignment Learning for Text-based Person Search.- Point Cloud Classification via Learnable Memory Bank.- Adversarially Regularized Low-Light Image Enhancement.- Advancing Incremental Few-shot Semantic Segmentation via Semantic-guided Relation Alignment and Adaptation.- PMGCN:Preserving measuring mapping prototype graph calibration network for few-shot learning.- ARE-CAM: An interpretable approach to quantitatively evaluating the adversarial robustness of deep models based on CAM.- SSK-Yolo:Global feature-driven small object detection network for images.- MetaVSR: A Novel Approach to Video Super-Resolution for Arbitrary Magnification.- From Skulls to Faces: A Deep Generative Framework for Realistic 3D Craniofacial Reconstruction.- Structure-aware Adaptive Hybrid Interaction Modeling for Image-Text Matching.- Using Saliency and Cropping to Improve Video Memorability.- Contextual Augmentation with Bias Adaptive for Few-shot Video
Object Segmentation.- A lightweight local attention network for image super resolution.- Domain Adaptation for Speaker Verification Based on Self-Supervised
Learning with Adversarial Training.- Quality Scalable Video Coding based on Neural Representation.- Hierarchical Bi-Directional Temporal Context Mining for Improved
Video Compression.- MAMixer: Multivariate Time Series Forecasting via Multi-Axis Mixing.- A Custom GAN-based Robust Algorithm for Medical Image Watermarking.- A Detail-guided Multi-source Fusion Network for Remote Sensing Object Detection.- A Secure and Fair Federated Learning Protocol under the Universal Composability Framework.- Bi-directional Interaction and Dense Aggregation Network for RGB-D Salient Object Detection.- Face Forgery Detection via Texture and Saliency Enhancement.