Learning Robust Visual-Semantic Embeddings

TL;DR

This paper introduces an end-to-end unsupervised learning framework for robust visual-semantic embeddings, leveraging auto-encoders and domain adaptation to improve image and text representation across labeled and unlabeled data.

Contribution

It presents a novel combination of auto-encoders and Maximum Mean Discrepancy loss for joint embedding learning, including an unsupervised-data adaptation inference technique.

Findings

Outperforms state-of-the-art on multiple datasets

Effective in zero and few-shot recognition tasks

Improves robustness of multi-modal representations

Abstract

Many of the existing methods for learning joint embedding of images and text use only supervised information from paired images and its textual attributes. Taking advantage of the recent success of unsupervised learning in deep neural networks, we propose an end-to-end learning framework that is able to extract more robust multi-modal representations across domains. The proposed method combines representation learning models (i.e., auto-encoders) together with cross-domain learning criteria (i.e., Maximum Mean Discrepancy loss) to learn joint embeddings for semantic and visual features. A novel technique of unsupervised-data adaptation inference is introduced to construct more comprehensive embeddings for both labeled and unlabeled data. We evaluate our method on Animals with Attributes and Caltech-UCSD Birds 200-2011 dataset with a wide range of applications, including zero and few-shot image recognition and retrieval, from inductive to transductive settings. Empirically, we show that our framework improves over the current state of the art on many of the considered tasks.