arXiv:1803.07066
Learning Region Features for Object Detection
원문 초록
While most steps in the modern object detection methods are learnable, the region feature extraction step remains largely hand-crafted, featured by RoI pooling methods. This work proposes a general viewpoint that unifies existing region feature extraction methods and a novel method that is end-to-end learnable. The proposed method removes most heuristic choices and outperforms its RoI pooling counterparts. It moves further towards fully learnable object detection.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Towards Reliable Detection of Empty Space: Conditional Marked Point Processes for Object Detection
Multiple Object Stitching for Unsupervised Representation Learning
Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study
Edge-Enabled Collaborative Object Detection for Real-Time Multi-Vehicle Perception
Multispectral Detection Transformer with Infrared-Centric Sensor Fusion
A Real-Time System for Egocentric Hand-Object Interaction Detection in Industrial Domains
토론
이 논문과 연결된 포럼 글이 아직 없습니다.