Learning Deep Representations of Fine-grained Visual Descriptions

TL;DR

This paper introduces a neural language model trained from scratch to generate fine-grained visual descriptions, enabling zero-shot image recognition and retrieval without relying on attributes, thus providing a natural language interface.

Contribution

The authors propose a novel end-to-end neural language model trained from scratch that aligns raw text with images for fine-grained recognition, surpassing attribute-based methods.

Findings

Outperforms attribute-based methods on zero-shot classification

Achieves strong results on zero-shot text-based image retrieval

Provides a natural language interface for image annotation and retrieval

Abstract

State-of-the-art methods for zero-shot visual recognition formulate learning as a joint embedding problem of images and side information. In these formulations the current best complement to visual features are attributes: manually encoded vectors describing shared characteristics among categories. Despite good performance, attributes have limitations: (1) finer-grained recognition requires commensurately more attributes, and (2) attributes do not provide a natural language interface. We propose to overcome these limitations by training neural language models from scratch; i.e. without pre-training and only consuming words and characters. Our proposed models train end-to-end to align with the fine-grained and category-specific content of images. Natural language provides a flexible and compact way of encoding only the salient visual aspects for distinguishing categories. By training on raw text, our model can do inference on raw text as well, providing humans a familiar mode both for annotation and retrieval. Our 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.