DeepAI AI Chat
Log In Sign Up

Relation Networks for Object Detection

by   Han Hu, et al.

Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era. All state-of-the-art object detection systems still rely on recognizing object instances individually, without exploiting their relations during learning. This work proposes an object relation module. It processes a set of objects simultaneously through interaction between their appearance feature and geometry, thus allowing modeling of their relations. It is lightweight and in-place. It does not require additional supervision and is easy to embed in existing networks. It is shown effective on improving object recognition and duplicate removal steps in the modern object detection pipeline. It verifies the efficacy of modeling object relations in CNN based detection. It gives rise to the first fully end-to-end object detector.


A Review of methods for Textureless Object Recognition

Textureless object recognition has become a significant task in Computer...

Object Recognition in Different Lighting Conditions at Various Angles by Deep Learning Method

Existing computer vision and object detection methods strongly rely on n...

Cascade Region Proposal and Global Context for Deep Object Detection

Deep region-based object detector consists of a region proposal step and...

Approximation of dilation-based spatial relations to add structural constraints in neural networks

Spatial relations between objects in an image have proved useful for str...

DeepScores and Deep Watershed Detection: current state and open issues

This paper gives an overview of our current Optical Music Recognition (O...

Learning a Metacognition for Object Detection

In contrast to object recognition models, humans do not blindly trust th...

DisARM: Displacement Aware Relation Module for 3D Detection

We introduce Displacement Aware Relation Module (DisARM), a novel neural...