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    MultiMedia Modeling

    30th International Conference, MMM 2024, Amsterdam, The Netherlands, January 29 – February 2, 2024, Proceedings, Part I

    AvStevan Rudinac,Alan Hanjalic

    Häftad, Engelska, 2024

    Del 14554 i serien Lecture Notes in Computer Science

    869 kr

    Beställningsvara. Skickas inom 5-8 vardagar. Fri frakt över 249 kr.

    Beskrivning

    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.

    Produktinformation

    • Utgivningsdatum:2024-01-28
    • Mått:155 x 235 x 29 mm
    • Vikt:785 g
    • Format:Häftad
    • Språk:Engelska
    • Serie:Lecture Notes in Computer Science
    • Antal sidor:505
    • Förlag:Springer International Publishing AG
    • ISBN:9783031533044

    Utforska kategorier

    • Elektronik och kommunikationer inom Naturvetenskap och teknik
    • Systemvetenskap och AI inom Data och IT

    Innehållsförteckning

    • 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 VideoObject Segmentation.- A lightweight local attention network for image super resolution.- Domain Adaptation for Speaker Verification Based on Self-SupervisedLearning with Adversarial Training.- Quality Scalable Video Coding based on Neural Representation.- Hierarchical Bi-Directional Temporal Context Mining for ImprovedVideo 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.