Del 15629 i serien Lecture Notes in Computer Science

Computer Vision – ECCV 2024 Workshops

Milan, Italy, September 29–October 4, 2024, Proceedings, Part VII

Häftad, Engelska, 2025

AvAlessio Del Bue,Cristian Canton,Jordi Pont-Tuset,Tatiana Tommasi

2 199 kr

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The multi-volume set LNCS 15623 until LNCS 15646 constitutes the proceedings of the workshops that were held in conjunction with the 18th European Conference on Computer Vision, ECCV 2024, which took place in Milan, Italy, during September 29–October 4, 2024.

Produktinformation

Utgivningsdatum2025-05-27
Mått155 x 235 x 24 mm
Vikt668 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor387
FörlagSpringer International Publishing AG
ISBN9783031917660

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Valeo4Cast: A Modular Approach to End-to-End Forecasting.- AA-SGAN: Adversarially Augmented Social GAN with Synthetic Data.- Autonomous Drone-Person Tracking and Following in Uniform Appearance Scenarios.- Continual Reinforcement Learning with Implicit Generative Replay for Autonomous Driving.- Self-supervised Road Accident Anticipation with Non-decreasing Danger.- 3D Object Detection and Tracking Refinement with Ensemble Methods and Spatiotemporal Filtering.- Conditional Unscented Autoencoders for Trajectory Prediction.- Uncertainty Estimation and Out-of-Distribution Detection for LiDAR Scene Semantic Segmentation.- TrackLidFormer: a Transformer-based Approach for Occluded Object Tracking.- Good Data Is All Imitation Learning Needs.- What Matters in Autonomous Driving Anomaly Detection: A Weakly Supervised Horizon.- High Dynamic Range Modulo Imaging for Robust Object Detection in Autonomous Driving.- RLNet: Adaptive Fusion of 4D Radar and Lidar for 3D Object Detection.- Improving Online Source-Free Domain Adaptation for Object Detection by Unsupervised Data Acquisition.- AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving.- On Camera and LiDAR Positions in End-to-End Autonomous Driving.- ProGBA: Prompt Guided Bayesian Augmentation for Zero-shot Domain Adaptation.- ReGentS: Real-World Safety-Critical Driving Scenario Generation Made Stable.- Loop Mining Large-Scale Unlabeled Data for Corner Case Detection in Autonomous Driving.- HumanSim: Human-Like Multi-Agent Novel Driving Simulation for Corner Case Generation.- Talk to Parallel LiDARs: A Human-LiDAR Interaction Method Based on 3D Visual Grounding.- RoSA Dataset: Road Construct zone Segmentation for Autonomous Driving.- A Multimodal Hybrid Late-Cascade Fusion Network for Enhanced 3D Object Detection.- The Second Visual Object Tracking Segmentation VOTS2024 Challenge Results.