PartCom: Part Composition Learning for 3D Open-Set Recognition
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingOpen Set LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Composing Parts for Expressive Object Generation
Image composition and generation are processes where the artists need control over various parts of the generated images. However, the current state-of-the-art generation models, like Stable Diffusion, cannot handle …
AttributeDenoisingImage GenerationObjectPartComposer: Learning and Composing Part-Level Concepts from Single-Image Examples
We present PartComposer: a framework for part-level concept learning from single-image examples that enables text-to-image diffusion models to compose novel objects from meaningful components. Existing methods either str…
DisentanglementExploring the Spectrum of Visio-Linguistic Compositionality and Recognition
Vision and language models (VLMs) such as CLIP have showcased remarkable zero-shot recognition abilities yet face challenges in visio-linguistic compositionality, particularly in linguistic comprehension and fine-grained…
Retrievalzero-shot-classificationZero-Shot LearningCompositional Clustering: Applications to Multi-Label Object Recognition and Speaker Identification
We consider a novel clustering task in which clusters can have compositional relationships, e.g., one cluster contains images of rectangles, one contains images of circles, and a third (compositional) cluster contains im…
ClusteringFew-Shot LearningObject Recognitionspeaker-diarization+2Knowledge Guided Learning: Towards Open Domain Egocentric Action Recognition with Zero Supervision
Advances in deep learning have enabled the development of models that have exhibited a remarkable tendency to recognize and even localize actions in videos. However, they tend to experience errors when faced with scenes …
Action RecognitionDomain AdaptationNovel Object Detectionobject-detection+2