Open-Set Object Detection Using Classification-free Object Proposal and Instance-level Contrastive Learning with Appendix

11/21/2022
by   Zhongxiang Zhou, et al.
0

Detecting both known and unknown objects is a fundamental skill for robot manipulation in unstructured environments. Open-set object detection (OSOD) is a promising direction to handle the problem consisting of two subtasks: objects and background separation, and open-set object classification. In this paper, we present Openset RCNN to address the challenging OSOD. To disambiguate unknown objects and background in the first subtask, we propose to use classification-free region proposal network (CF-RPN) which estimates the objectness score of each region purely using cues from object's location and shape preventing overfitting to the training categories. To identify unknown objects in the second subtask, we propose to represent them using the complementary region of known categories in a latent space which is accomplished by a prototype learning network (PLN). PLN performs instance-level contrastive learning to encode proposals to a latent space and builds a compact region centering with a prototype for each known category. Further, we note that the detection performance of unknown objects can not be unbiasedly evaluated on the situation that commonly used object detection datasets are not fully annotated. Thus, a new benchmark is introduced by reorganizing GraspNet-1billion, a robotic grasp pose detection dataset with complete annotation. Extensive experiments demonstrate the merits of our method. We finally show that our Openset RCNN can endow the robot with an open-set perception ability to support robotic rearrangement tasks in cluttered environments. More details can be found in https://sites.google.com/view/openest-rcnn/

READ FULL TEXT

page 1

page 4

page 7

page 8

page 11

page 12

page 13

page 14

research
03/28/2022

Expanding Low-Density Latent Regions for Open-Set Object Detection

Modern object detectors have achieved impressive progress under the clos...
research
08/15/2021

Learning Open-World Object Proposals without Learning to Classify

Object proposals have become an integral preprocessing steps of many vis...
research
09/12/2019

Detecting Robotic Affordances on Novel Objects with Regional Attention and Attributes

This paper presents a framework for predicting affordances of object par...
research
08/15/2023

Improved Region Proposal Network for Enhanced Few-Shot Object Detection

Despite significant success of deep learning in object detection tasks, ...
research
07/20/2022

More Practical Scenario of Open-set Object Detection: Open at Category Level and Closed at Super-category Level

Open-set object detection (OSOD) has recently attracted considerable att...
research
01/07/2022

Extending One-Stage Detection with Open-World Proposals

In many applications, such as autonomous driving, hand manipulation, or ...
research
02/07/2019

StampNet: unsupervised multi-class object discovery

Unsupervised object discovery in images involves uncovering recurring pa...

Please sign up or login with your details

Forgot password? Click here to reset