Exploring Person Context and Local Scene Context for Object Detection
원문 초록
In this paper we explore two ways of using context for object detection. The first model focusses on people and the objects they commonly interact with, such as fashion and sports accessories. The second model considers more general object detection and uses the spatial relationships between objects and between objects and scenes. Our models are able to capture precise spatial relationships between the context and the object of interest, and make effective use of the appearance of the contextual region. On the newly released COCO dataset, our models provide relative improvements of up to 5% over CNN-based state-of-the-art detectors, with the gains concentrated on hard cases such as small objects (10% relative improvement).
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.