Semantic-diversity transfer network for generalized zero-shot learning via inner disagreement based OOD detector

by   Bo Liu, et al.

Zero-shot learning (ZSL) aims to recognize objects from unseen classes, where the kernel problem is to transfer knowledge from seen classes to unseen classes by establishing appropriate mappings between visual and semantic features. The knowledge transfer in many existing works is limited mainly due to the facts that 1) the widely used visual features are global ones but not totally consistent with semantic attributes; 2) only one mapping is learned in existing works, which is not able to effectively model diverse visual-semantic relations; 3) the bias problem in the generalized ZSL (GZSL) could not be effectively handled. In this paper, we propose two techniques to alleviate these limitations. Firstly, we propose a Semantic-diversity transfer Network (SetNet) addressing the first two limitations, where 1) a multiple-attention architecture and a diversity regularizer are proposed to learn multiple local visual features that are more consistent with semantic attributes and 2) a projector ensemble that geometrically takes diverse local features as inputs is proposed to model visual-semantic relations from diverse local perspectives. Secondly, we propose an inner disagreement based domain detection module (ID3M) for GZSL to alleviate the third limitation, which picks out unseen-class data before class-level classification. Due to the absence of unseen-class data in training stage, ID3M employs a novel self-contained training scheme and detects out unseen-class data based on a designed inner disagreement criterion. Experimental results on three public datasets demonstrate that the proposed SetNet with the explored ID3M achieves a significant improvement against 30 state-of-the-art methods.


Region Semantically Aligned Network for Zero-Shot Learning

Zero-shot learning (ZSL) aims to recognize unseen classes based on the k...

Semantic Disentangling Generalized Zero-Shot Learning

Generalized Zero-Shot Learning (GZSL) aims to recognize images from both...

Transductive Zero-Shot Learning with Visual Structure Constraint

Zero-shot Learning (ZSL) aims to recognize objects of the unseen classes...

Rethinking Zero-Shot Learning: A Conditional Visual Classification Perspective

Zero-shot learning (ZSL) aims to recognize instances of unseen classes s...

Hardness Sampling for Self-Training Based Transductive Zero-Shot Learning

Transductive zero-shot learning (T-ZSL) which could alleviate the domain...

Revisiting Document Representations for Large-Scale Zero-Shot Learning

Zero-shot learning aims to recognize unseen objects using their semantic...

GBE-MLZSL: A Group Bi-Enhancement Framework for Multi-Label Zero-Shot Learning

This paper investigates a challenging problem of zero-shot learning in t...

Please sign up or login with your details

Forgot password? Click here to reset