105 citations · 125 across the 7 of their papers we have counts for
7 papers
Fine-grained Few-shot Recognition by Deep Object Parsing
Ruizhao Zhu, Pengkai Zhu, Samarth Mishra +1
We propose a new method for fine-grained few-shot recognition via deep object parsing. In our framework, an object is made up of K distinct parts and for each part, we learn a dict…
Learning Compositional Representations for Effective Low-Shot Generalization
Samarth Mishra, Pengkai Zhu, Venkatesh Saligrama
We propose Recognition as Part Composition (RPC), an image encoding approach inspired by human cognition. It is based on the cognitive theory that humans recognize complex objects…
Contrastive Neighborhood Alignment
Pengkai Zhu, Zhaowei Cai, Yuanjun Xiong +4
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 represe…
Dont Even Look Once: Synthesizing Features for Zero-Shot Detection
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, collec…
Learning Classifiers for Domain Adaptation, Zero and Few-Shot Recognition Based on Learning Latent Semantic Parts
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 insuffic…
Generalized Zero-Shot Recognition based on Visually Semantic Embedding
Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama
We propose a novel Generalized Zero-Shot learning (GZSL) method that is agnostic to both unseen images and unseen semantic vectors during training. Prior works in this context prop…
Zero-Shot Detection
Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama
As we move towards large-scale object detection, it is unrealistic to expect annotated training data, in the form of bounding box annotations around objects, for all object classes…