2022-11-22 更新
PartCom: Part Composition Learning for 3D Open-Set Recognition
Authors:Weng Tingyu, Xiao Jun, Jiang Haiyong
3D recognition is the foundation of 3D deep learning in many emerging fields, such as autonomous driving and robotics.Existing 3D methods mainly focus on the recognition of a fixed set of known classes and neglect possible unknown classes during testing. These unknown classes may cause serious accidents in safety-critical applications, i.e. autonomous driving. In this work, we make a first attempt to address 3D open-set recognition (OSR) so that a classifier can recognize known classes as well as be aware of unknown classes. We analyze open-set risks in the 3D domain and point out the overconfidence and under-representation problems that make existing methods perform poorly on the 3D OSR task. To resolve above problems, we propose a novel part prototype-based OSR method named PartCom. We use part prototypes to represent a 3D shape as a part composition, since a part composition can represent the overall structure of a shape and can help distinguish different known classes and unknown ones. Then we formulate two constraints on part prototypes to ensure their effectiveness. To reduce open-set risks further, we devise a PUFS module to synthesize unknown features as representatives of unknown samples by mixing up part composite features of different classes. We conduct experiments on three kinds of 3D OSR tasks based on both CAD shape dataset and scan shape dataset. Extensive experiments show that our method is powerful in classifying known classes and unknown ones and can attain much better results than SOTA baselines on all 3D OSR tasks. The project will be released.
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MetaMax: Improved Open-Set Deep Neural Networks via Weibull Calibration
Authors:Zongyao Lyu, Nolan B. Gutierrez, William J. Beksi
Open-set recognition refers to the problem in which classes that were not seen during training appear at inference time. This requires the ability to identify instances of novel classes while maintaining discriminative capability for closed-set classification. OpenMax was the first deep neural network-based approach to address open-set recognition by calibrating the predictive scores of a standard closed-set classification network. In this paper we present MetaMax, a more effective post-processing technique that improves upon contemporary methods by directly modeling class activation vectors. MetaMax removes the need for computing class mean activation vectors (MAVs) and distances between a query image and a class MAV as required in OpenMax. Experimental results show that MetaMax outperforms OpenMax and is comparable in performance to other state-of-the-art approaches.
PDF To be presented at the 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshop on Dealing with Novelty in Open Worlds (DNOW)
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Open-Set Object Detection Using Classification-free Object Proposal and Instance-level Contrastive Learning with Appendix
Authors:Zhongxiang Zhou, Yifei Yang, Yue Wang, Rong Xiong
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/
PDF Submit to IEEE Robotics and Automation Letters
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