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Lieferstatus:	i.d.R. innert 5-10 Tagen versandfertig
Veröffentlichung:	Oktober 2022
Genre:	EDV / Informatik
	Applications / computerscience / ConferenceProceedings / Informatics / Research
ISBN:	9783031198205
EAN-Code:	9783031198205
Verlag:	Springer
Einband:	Kartoniert
Sprache:	English
Dimensionen:	H 235 mm / B 155 mm / D 44 mm
Gewicht:	1206 gr
Seiten:	812
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

¿GOCA: Guided Online Cluster Assignment for Self-Supervised VideoRepresentation Learning.- Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning.- Revisiting the Critical Factors of Augmentation-Invariant Representation Learning.- CA-SSL: Class-Agnostic Semi-Supervised Learning for Detection and Segmentation.- Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation.- Semantic-Aware Fine-Grained Correspondence.- Self-Supervised Classification Network.- Data Invariants to Understand Unsupervised Out-of-Distribution Detection.- Domain Invariant Masked Autoencoders for Self-Supervised Learning from Multi-Domains.- Semi-Supervised Object Detection via Virtual Category Learning.- Completely Self-Supervised Crowd Counting via Distribution Matching.- Coarse-to-Fine Incremental Few-Shot Learning.- Learning Unbiased Transferability for Domain Adaptation by Uncertainty Modeling.- Learn2Augment: Learning to Composite Videos for Data Augmentation in Action Recognition.- CYBORGS: Contrastively Bootstrapping Object Representations by Grounding in Segmentation.- PSS: Progressive Sample Selection for Open-World Visual Representation Learning.- Improving Self-Supervised Lightweight Model Learning via Hard-Aware Metric Distillation.- Object Discovery via Contrastive Learning for Weakly Supervised Object Detection.- Stochastic Consensus: Enhancing Semi-Supervised Learning with Consistency of Stochastic Classifiers.- DiffuseMorph: Unsupervised Deformable Image Registration Using Diffusion Model.- Semi-Leak: Membership Inference Attacks against Semi-Supervised Learning.- OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning.- Embedding Contrastive Unsupervised Features to Cluster in- and Out-of-Distribution Noise in Corrupted Image Datasets.- Unsupervised Few-Shot Image Classification by Learning Features into Clustering Space.- Towards Realistic Semi-Supervised Learning.- Masked Siamese Networks for Label-Efficient Learning.- Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization.- Understanding Collapse in Non-Contrastive Siamese Representation Learning.- Federated Self-Supervised Learning for Video Understanding.- Towards Efficient and Effective Self-Supervised Learning of Visual Representations.- DSR - A Dual Subspace Re-Projection Network for Surface Anomaly Detection.- PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds.- MVSTER: Epipolar Transformer for Efficient Multi-View Stereo.- RelPose: Predicting Probabilistic Relative Rotation for Single Objects in the Wild.- R2L: Distilling Neural Radiance Field to Neural Light Field for Efficient Novel View Synthesis.- KD-MVS: Knowledge Distillation Based Self-Supervised Learning for Multi-View Stereo.- SALVe: Semantic Alignment Verification for Floorplan Reconstruction from Sparse Panoramas.- RC-MVSNet: Unsupervised Multi-View Stereo with Neural Rendering.- Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes.- NeILF: Neural Incident Light Field for Physically-Based Material Estimation.- ARF: Artistic Radiance Fields.- Multiview Stereo with Cascaded Epipolar RAFT.