# Pose Induction for Novel Object Categories

arXiv: https://arxiv.org/abs/1505.00066 · HTML: https://mlsanity.com/paper/1505.00066/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2015.

## Built on by (most cited first, 2 of 2)

- [PoseContrast: Class-Agnostic Object Viewpoint Estimation in the Wild with Pose-Aware Contrastive Learning](https://mlsanity.com/paper/2105.05643/) (2021)
- [Category-Level Pose Retrieval with Contrastive Features Learnt with Occlusion Augmentation](https://mlsanity.com/paper/2208.06195/) (2022)

## Abstract (arXiv)

We address the task of predicting pose for objects of unannotated object categories from a small seed set of annotated object classes. We present a generalized classifier that can reliably induce pose given a single instance of a novel category. In case of availability of a large collection of novel instances, our approach then jointly reasons over all instances to improve the initial estimates. We empirically validate the various components of our algorithm and quantitatively show that our method produces reliable pose estimates. We also show qualitative results on a diverse set of classes and further demonstrate the applicability of our system for learning shape models of novel object classes.