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Zero-Shot Learning

75 papers with code · Methodology

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Greatest papers with code

Improving zero-shot learning by mitigating the hubness problem

20 Dec 2014facebookresearch/MUSE

The zero-shot paradigm exploits vector-based word representations extracted from text corpora with unsupervised methods to learn general mapping functions from other feature spaces onto word space, where the words associated to the nearest neighbours of the mapped vectors are used as their linguistic labels.

IMAGE RETRIEVAL ZERO-SHOT LEARNING

Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs

CVPR 2018 JudyYe/zero-shot-gcn

Given a learned knowledge graph (KG), our approach takes as input semantic embeddings for each node (representing visual category).

KNOWLEDGE GRAPHS ZERO-SHOT LEARNING

Synthesized Classifiers for Zero-Shot Learning

CVPR 2016 JudyYe/zero-shot-gcn

Given semantic descriptions of object classes, zero-shot learning aims to accurately recognize objects of the unseen classes, from which no examples are available at the training stage, by associating them to the seen classes, from which labeled examples are provided.

ZERO-SHOT LEARNING

Zero-Shot Learning by Convex Combination of Semantic Embeddings

19 Dec 2013JudyYe/zero-shot-gcn

In other cases the semantic embedding space is established by an independent natural language processing task, and then the image transformation into that space is learned in a second stage.

ZERO-SHOT LEARNING

Learning to Compare: Relation Network for Few-Shot Learning

CVPR 2018 floodsung/LearningToCompare_FSL

Once trained, a RN is able to classify images of new classes by computing relation scores between query images and the few examples of each new class without further updating the network.

FEW-SHOT IMAGE CLASSIFICATION FEW-SHOT LEARNING ZERO-SHOT LEARNING

Learning Deep Representations of Fine-grained Visual Descriptions

CVPR 2016 hanzhanggit/StackGAN-v2

State-of-the-art methods for zero-shot visual recognition formulate learning as a joint embedding problem of images and side information.

IMAGE RETRIEVAL ZERO-SHOT LEARNING

Prototypical Networks for Few-shot Learning

NeurIPS 2017 learnables/learn2learn

We propose prototypical networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class.

FEW-SHOT IMAGE CLASSIFICATION ONE-SHOT LEARNING ZERO-SHOT LEARNING

Sampling Matters in Deep Embedding Learning

ICCV 2017 Confusezius/Deep-Metric-Learning-Baselines

In addition, we show that a simple margin based loss is sufficient to outperform all other loss functions.

#2 best model for Metric Learning on CUB-200-2011 (using extra training data)

FACE VERIFICATION IMAGE RETRIEVAL METRIC LEARNING ZERO-SHOT LEARNING

No Fuss Distance Metric Learning using Proxies

ICCV 2017 Confusezius/Deep-Metric-Learning-Baselines

Traditionally, for this problem supervision is expressed in the form of sets of points that follow an ordinal relationship -- an anchor point $x$ is similar to a set of positive points $Y$, and dissimilar to a set of negative points $Z$, and a loss defined over these distances is minimized.

METRIC LEARNING SEMANTIC SIMILARITY SEMANTIC TEXTUAL SIMILARITY ZERO-SHOT LEARNING

Rethinking Knowledge Graph Propagation for Zero-Shot Learning

CVPR 2019 cyvius96/DGP

Graph convolutional neural networks have recently shown great potential for the task of zero-shot learning.

ZERO-SHOT LEARNING