Exploring Person Context and Local Scene Context for Object Detection

Abstract

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).

References in corpus (4)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Going Deeper with Convolutions
• Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
• Contextual Action Recognition with R*CNN

Cited by in corpus (10)

• Object Detection in 20 Years: A Survey
• Beyond Skip Connections: Top-Down Modulation for Object Detection
• Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks
• Relation Networks for Object Detection
• Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships
• GTA: Global Temporal Attention for Video Action Understanding
• Actor-Centric Relation Network
• Spatial Memory for Context Reasoning in Object Detection
• Object-Level Context Modeling For Scene Classification with Context-CNN
• StuffNet: Using 'Stuff' to Improve Object Detection