arXiv:2010.15250
Semantic video segmentation for autonomous driving
원문 초록
We aim to solve semantic video segmentation in autonomous driving, namely road detection in real time video, using techniques discussed in (Shelhamer et al., 2016a). While fully convolutional network gives good result, we show that the speed can be halved while preserving the accuracy. The test dataset being used is KITTI, which consists of real footage from Germany's streets.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and Grounder
Memory-Augmented SAM2 for Training-Free Surgical Video Segmentation
MUVOD: A Novel Multi-view Video Object Segmentation Dataset and A Benchmark for 3D Segmentation
Leader360V: The Large-scale, Real-world 360 Video Dataset for Multi-task Learning in Diverse Environment
A Comprehensive Survey on Video Scene Parsing:Advances, Challenges, and Prospects
SeC: Advancing Complex Video Object Segmentation via Progressive Concept Construction
토론
이 논문과 연결된 포럼 글이 아직 없습니다.