Search Results for author: Pengkai Zhu

Found 6 papers, 1 papers with code

Contrastive Neighborhood Alignment

no code implementations6 Jan 2022 Pengkai Zhu, Zhaowei Cai, Yuanjun Xiong, Zhuowen Tu, Luis Goncalves, Vijay Mahadevan, Stefano Soatto

We present Contrastive Neighborhood Alignment (CNA), a manifold learning approach to maintain the topology of learned features whereby data points that are mapped to nearby representations by the source (teacher) model are also mapped to neighbors by the target (student) model.

Don't Even Look Once: Synthesizing Features for Zero-Shot Detection

no code implementations CVPR 2020 Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama

Zero-shot detection, namely, localizing both seen and unseen objects, increasingly gains importance for large-scale applications, with large number of object classes, since, collecting sufficient annotated data with ground truth bounding boxes is simply not scalable.

Object Detection

Dont Even Look Once: Synthesizing Features for Zero-Shot Detection

no code implementations18 Nov 2019 Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama

Zero-shot detection, namely, localizing both seen and unseen objects, increasingly gains importance for large-scale applications, with large number of object classes, since, collecting sufficient annotated data with ground truth bounding boxes is simply not scalable.

Object Detection

Learning Classifiers for Domain Adaptation, Zero and Few-Shot Recognition Based on Learning Latent Semantic Parts

no code implementations25 Jan 2019 Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama

In computer vision applications, such as domain adaptation (DA), few shot learning (FSL) and zero-shot learning (ZSL), we encounter new objects and environments, for which insufficient examples exist to allow for training "models from scratch," and methods that adapt existing models, trained on the presented training environment, to the new scenario are required.

Domain Adaptation Few-Shot Learning +1

Zero-Shot Detection

1 code implementation19 Mar 2018 Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama

While we utilize semantic features during training, our method is agnostic to semantic information for unseen classes at test-time.

Object Detection

Cannot find the paper you are looking for? You can Submit a new open access paper.