Transductive Zero-Shot Learning for 3D Point Cloud Classification
By Ali Cheraghian, Shafin Rahman, Dylan Campbell, Lars Petersson. 2019
68 citations recorded by the source corpus.
Zero-shot learning, the task of learning to recognize new classes not seen during training, has received considerable attention in the case of 2D image classification. However despite the increasing ubiquity of 3D sensors, the corresponding 3D point cloud classification problem has not been meaningfully explored and introduces new challenges. This paper extends, for the first time, transductive ZeroShot Learning (ZSL) and Generalized Zero-Shot Learning (GZSL) approaches to the domain of 3D point cloud classification. To this end, a novel triplet loss is developed that takes advantage of unlabeled test data. While designed for the task of 3D point cloud classification, the method is also shown to be applicable to the more common use-case of 2D image classification. An extensive set of experiments is carried out, establishing state-of-the-art for ZSL and GZSL in the 3D point cloud domain, as well as demonstrating the applicability of the approach to the image domain.1
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Mehrtash Harandi
- Christian Simon
- Lars Petersson
- Ali Cheraghian
- Shafin Rahman
- Pengfei Fang
- Soumava Kumar Roy
- Sameera Ramasinghe
In Computer Vision and Pattern Recognition
- Mehrtash Harandi
- Christian Simon
- Lars Petersson
- Ali Cheraghian
- Shafin Rahman
- Pengfei Fang
- Soumava Kumar Roy
- Sameera Ramasinghe
In Electrical and Electronic Engineering
Citations in the corpus
- Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning (2021, background): “Being inspired by the literature on zero-shot learning [7, 6, 5, 20, 37, 24, 25], given an image as input, we estimate the semantic word vectors for the input instead of directly predicting its class label.”
- Synthesized Feature based Few-Shot Class-Incremental Learning on a Mixture of Subspaces (2021, background): “Learning without forgetting using word vectors: Word vectors have shown promising success on various computer vision tasks such as zero-shot learning, few-shot learning, image/video captioning and visual question answering [15, 29, 39, 43, 9, 46, 47, 6, 5, 7].”