Contrastive Embedding for Generalized Zero-Shot Learning
1.Contrastive Embedding for Generalized Zero-Shot Learning 제 1부
링크 : https://openaccess.thecvf.com/content/CVPR2021/papers/Han_Contrastive_Embedding_for_Generalized_Zero-Shot_Learning_CVPR_2021_paper.pdf코드 : h
2.Contrastive Embedding for Generalized Zero-Shot Learning 제 2부
Object recognition is a core problem in computer vision.물체 인식은 컴퓨터 비전의 핵심 문제입니다.This problem on a fixed set of categories with plenty of training samp
3.Contrastive Embedding for Generalized Zero-Shot Learning 제 3부
2. Related Work Zero-shot learning [39, 54] aims to transfer the object recognition model from seen to unseen classes via the shared semantic space,
4.Contrastive Embedding for Generalized Zero-Shot Learning 제 4부
In this section, we first define the Generalized Zero-Shot Learning (GZSL) problem, before introducing the proposed hybrid GZSL framework and the cont
5.Contrastive Embedding for Generalized Zero-Shot Learning 제4-2부
Our basic hybrid GZSL framework is based on the traditional semantic embedding model, where only the class-wise supervision is exploited.우리의 기본 하이브리드
6.Contrastive Embedding for Generalized Zero-Shot Learning 제4-3부
In our final hybrid GZSL framework, we replace the semantic embedding (SE) model in the basic hybrid framework in Eq.4 with the proposed contrastive e
7.Contrastive Embedding for Generalized Zero-Shot Learning 제5부
We evaluate our method on five benchmark datasets for ZSL: Animals with Attributes 1&2 (AWA1 39 & AWA2 69), Caltech-UCSD Birds-200-2011 (CUB) 65, Oxfo
8.Contrastive Embedding for Generalized Zero-Shot Learning 제5-2부
The effect of different embedding models (E-M) and different spaces in the hybrid GZSL framework. 하이브리드 GZSL 프레임워크에서 다양한 임베딩 모델(E-M)과 다른 공간의 효과.All th