Zero-Shot Learning Based on Knowledge Sharing

• URL: http://arxiv.org/abs/2102.13326v1
• Date: Fri, 26 Feb 2021 06:43:29 GMT
• Title: Zero-Shot Learning Based on Knowledge Sharing
• Authors: Zeng Ting, Xiang Hongxin, Xie Cheng, Yang Yun, Liu Qing
• Abstract summary: Zero-Shot Learning (ZSL) is an emerging research that aims to solve the classification problems with very few training data. This paper introduces knowledge sharing (KS) to enrich the representation of semantic features. Based on KS, we apply a generative adversarial network to generate pseudo visual features from semantic features that are very close to the real visual features.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Zero-Shot Learning (ZSL) is an emerging research that aims to solve the classification problems with very few training data. The present works on ZSL mainly focus on the mapping of learning semantic space to visual space. It encounters many challenges that obstruct the progress of ZSL research. First, the representation of the semantic feature is inadequate to represent all features of the categories. Second, the domain drift problem still exists during the transfer from semantic space to visual space. In this paper, we introduce knowledge sharing (KS) to enrich the representation of semantic features. Based on KS, we apply a generative adversarial network to generate pseudo visual features from semantic features that are very close to the real visual features. Abundant experimental results from two benchmark datasets of ZSL show that the proposed approach has a consistent improvement.

Related papers

• ZeroMamba: Exploring Visual State Space Model for Zero-Shot Learning [28.52949450389388]
  Zero-shot learning (ZSL) aims to recognize unseen classes by transferring semantic knowledge from seen classes to unseen ones. We propose a parameter-efficient ZSL framework called ZeroMamba to advance ZSL. ZeroMamba comprises three key components: Semantic-aware Local Projection (SLP), Global Representation Learning (GRL), and Semantic Fusion (SeF)
  arXiv  Detail & Related papers  (2024-08-27T08:39:47Z)
• CREST: Cross-modal Resonance through Evidential Deep Learning for Enhanced Zero-Shot Learning [48.46511584490582]
  Zero-shot learning (ZSL) enables the recognition of novel classes by leveraging semantic knowledge transfer from known to unknown categories. Real-world challenges such as distribution imbalances and attribute co-occurrence hinder the discernment of local variances in images. We propose a bidirectional cross-modal ZSL approach CREST to overcome these challenges.
  arXiv  Detail & Related papers  (2024-04-15T10:19:39Z)
• Progressive Semantic-Guided Vision Transformer for Zero-Shot Learning [56.65891462413187]
  We propose a progressive semantic-guided vision transformer for zero-shot learning (dubbed ZSLViT) ZSLViT first introduces semantic-embedded token learning to improve the visual-semantic correspondences via semantic enhancement. Then, we fuse low semantic-visual correspondence visual tokens to discard the semantic-unrelated visual information for visual enhancement.
  arXiv  Detail & Related papers  (2024-04-11T12:59:38Z)
• Discriminative Region-based Multi-Label Zero-Shot Learning [145.0952336375342]
  Multi-label zero-shot learning (ZSL) is a more realistic counter-part of standard single-label ZSL. We propose an alternate approach towards region-based discriminability-preserving ZSL.
  arXiv  Detail & Related papers  (2021-08-20T17:56:47Z)
• FREE: Feature Refinement for Generalized Zero-Shot Learning [86.41074134041394]
  Generalized zero-shot learning (GZSL) has achieved significant progress, with many efforts dedicated to overcoming the problems of visual-semantic domain gap and seen-unseen bias. Most existing methods directly use feature extraction models trained on ImageNet alone, ignoring the cross-dataset bias between ImageNet and GZSL benchmarks. We propose a simple yet effective GZSL method, termed feature refinement for generalized zero-shot learning (FREE) to tackle the above problem.
  arXiv  Detail & Related papers  (2021-07-29T08:11:01Z)
• Learning Robust Visual-semantic Mapping for Zero-shot Learning [8.299945169799795]
  We focus on fully empowering the semantic feature space, which is one of the key building blocks of Zero-shot learning (ZSL) In ZSL, the common practice is to train a mapping function between the visual and semantic feature spaces with labeled seen class examples. Under such a paradigm, the ZSL models may easily suffer from the domain shift problem when constructing and reusing the mapping function.
  arXiv  Detail & Related papers  (2021-04-12T17:39:38Z)
• Goal-Oriented Gaze Estimation for Zero-Shot Learning [62.52340838817908]
  We introduce a novel goal-oriented gaze estimation module (GEM) to improve the discriminative attribute localization. We aim to predict the actual human gaze location to get the visual attention regions for recognizing a novel object guided by attribute description. This work implies the promising benefits of collecting human gaze dataset and automatic gaze estimation algorithms on high-level computer vision tasks.
  arXiv  Detail & Related papers  (2021-03-05T02:14:57Z)
• Cross Knowledge-based Generative Zero-Shot Learning Approach with Taxonomy Regularization [5.280368849852332]
  We develop a generative network-based ZSL approach equipped with the proposed Cross Knowledge Learning (CKL) scheme and Taxonomy Regularization (TR) CKL enables more relevant semantic features to be trained for semantic-to-visual feature embedding in ZSL. TR significantly improves the intersections with unseen images with more generalized visual features generated from generative network.
  arXiv  Detail & Related papers  (2021-01-25T04:38:18Z)
• Information Bottleneck Constrained Latent Bidirectional Embedding for Zero-Shot Learning [59.58381904522967]
  We propose a novel embedding based generative model with a tight visual-semantic coupling constraint. We learn a unified latent space that calibrates the embedded parametric distributions of both visual and semantic spaces. Our method can be easily extended to transductive ZSL setting by generating labels for unseen images.
  arXiv  Detail & Related papers  (2020-09-16T03:54:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.