Übersicht			Auf mobile öffnen

Lieferstatus:	i.d.R. innert 5-10 Tagen versandfertig
Veröffentlichung:	Oktober 2022
Genre:	EDV / Informatik
	ArtificialIntelligence / AutonomousVehicles / Computervision / imagecoding / imageprocessing / imagereconstruction / imagesegmentation / Learning / Lehrmittel, Lerntechnologien, E-Learning / machinelearning / Maschinelles Lernen / Maschinelles Sehen, Bildverstehen / mobilerobots / motionplanning / Mustererkennung / Netzwerk-Hardware / neuralnetworks / objectrecognition / patternrecognition / Robotics / signalprocessing mehr...
ISBN:	9783031198380
EAN-Code:	9783031198380
Verlag:	Springer
Einband:	Kartoniert
Sprache:	English
Dimensionen:	H 235 mm / B 155 mm / D 44 mm
Gewicht:	1223 gr
Seiten:	824
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

TALISMAN: Targeted Active Learning for Object Detection with Rare Classes and Slices Using Submodular Mutual Information.- An Efficient Person Clustering Algorithm for Open Checkout-Free Groceries.- POP: Mining POtential Performance of New Fashion Products via Webly Cross-Modal Query Expansion.- Pose Forecasting in Industrial Human-Robot Collaboration.- Actor-Centered Representations for Action Localization in Streaming Videos.- Bandwidth-Aware Adaptive Codec for DNN Inference Offloading in IoT.- Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment.- Towards Scale-Aware, Robust, and Generalizable Unsupervised Monocular Depth Estimation by Integrating IMU Motion Dynamics.- TIPS: Text-Induced Pose Synthesis.- Addressing Heterogeneity in Federated Learning via Distributional Transformation.- Where in the World Is This Image? Transformer-Based Geo-Localization in the Wild.- Colorization for In Situ Marine Plankton Images.- Efficient Deep Visual and Inertial Odometry with Adaptive Visual Modality Selection.- A Sketch Is Worth a Thousand Words: Image Retrieval with Text and Sketch.- A Cloud 3D Dataset and Application-Specific Learned Image Compression in Cloud 3D.- AutoTransition: Learning to Recommend Video Transition Effects.- Online Segmentation of LiDAR Sequences: Dataset and Algorithm.- Open-World Semantic Segmentation for LIDAR Point Clouds.- KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients.- Differentiable Raycasting for Self-Supervised Occupancy Forecasting.- InAction: Interpretable Action Decision Making for Autonomous Driving.- CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection.- CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving.- Motion Inspired Unsupervised Perception and Prediction in Autonomous Driving.- StretchBEV: Stretching Future Instance Prediction Spatially and Temporally.- RCLane: Relay Chain Prediction for Lane Detection.- Drive&Segment: Unsupervised Semantic Segmentation of Urban Scenes via Cross-Modal Distillation.- CenterFormer: Center-based Transformer for 3D Object Detection.- Physical Attack on Monocular Depth Estimation with Optimal Adversarial Patches.- ST-P3: End-to-End Vision-Based Autonomous Driving via Spatial-Temporal Feature Learning.- PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark.- PointFix: Learning to Fix Domain Bias for Robust Online Stereo Adaptation.- BRNet: Exploring Comprehensive Features for Monocular Depth Estimation.- SiamDoGe: Domain Generalizable Semantic Segmentation Using Siamese Network.- Context-Aware Streaming Perception in Dynamic Environments.- Context-Aware Streaming Perception in Dynamic Environments.- MultimodalTransformer for Automatic 3D Annotation and Object Detection.- Dynamic 3D Scene Analysis by Point Cloud Accumulation.- Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection.- JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving Scenes.- Semi-Supervised 3D Object Detection with Proficient Teachers.- Point Cloud Compression with Sibling Context and Surface Priors.