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Generative Adversarial Text to Image Synthesis
TLDR
A novel deep architecture and GAN formulation is developed to effectively bridge advances in text and image modeling, translating visual concepts from characters to pixels. Expand
Evaluation of output embeddings for fine-grained image classification
TLDR
This project shows that compelling classification performance can be achieved on fine-grained categories even without labeled training data, and establishes a substantially improved state-of-the-art on the Animals with Attributes and Caltech-UCSD Birds datasets. Expand
Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly
TLDR
A new zero-shot learning dataset is proposed, the Animals with Attributes 2 (AWA2) dataset which is made publicly available both in terms of image features and the images themselves and compares and analyzes a significant number of the state-of-the-art methods in depth. Expand
Feature Generating Networks for Zero-Shot Learning
TLDR
A novel generative adversarial network (GAN) that synthesizes CNN features conditioned on class-level semantic information, offering a shortcut directly from a semantic descriptor of a class to a class-conditional feature distribution. Expand
Zero-Shot Learning — The Good, the Bad and the Ugly
TLDR
A new benchmark is defined by unifying both the evaluation protocols and data splits for zero-shot learning, and a significant number of the state-of-the-art methods are compared and analyzed in depth, both in the classic zero- shot setting but also in the more realistic generalized zero-shots setting. Expand
Label-Embedding for Attribute-Based Classification
TLDR
This work proposes to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors, and introduces a function which measures the compatibility between an image and a label embedding. Expand
Learning Deep Representations of Fine-Grained Visual Descriptions
TLDR
This model achieves strong performance on zero-shot text-based image retrieval and significantly outperforms the attribute-based state-of-the-art for zero- shot classification on the Caltech-UCSD Birds 200-2011 dataset. Expand
Latent Embeddings for Zero-Shot Classification
TLDR
A novel latent embedding model for learning a compatibility function between image and class embeddings, in the context of zero-shot classification, that improves the state-of-the-art for various classembeddings consistently on three challenging publicly available datasets for the zero- shot setting. Expand
Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
TLDR
This work proposes a model where a shared latent space of image features and class embeddings is learned by modality-specific aligned variational autoencoders, and align the distributions learned from images and from side-information to construct latent features that contain the essential multi-modal information associated with unseen classes. Expand
Label-Embedding for Image Classification
TLDR
This work proposes to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors, and introduces a function that measures the compatibility between an image and a label embedding. Expand
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