Multimodal Transfer Deep Learning with Applications in Audio-Visual Recognition

TL;DR

This paper introduces a transfer deep learning framework that enables knowledge transfer between different modalities, such as from speech to video recognition, using analogy-preserving embeddings and fine-tuning.

Contribution

The novel framework allows transfer of semantic knowledge across modalities by learning analogy-preserving embeddings and fine-tuning networks without changing their topology.

Findings

Effective transfer of knowledge demonstrated on audio-visual datasets.

Framework is flexible for various multimodal datasets and existing networks.

Potential for broad applications in multimodal recognition tasks.

Abstract

We propose a transfer deep learning (TDL) framework that can transfer the knowledge obtained from a single-modal neural network to a network with a different modality. Specifically, we show that we can leverage speech data to fine-tune the network trained for video recognition, given an initial set of audio-video parallel dataset within the same semantics. Our approach first learns the analogy-preserving embeddings between the abstract representations learned from intermediate layers of each network, allowing for semantics-level transfer between the source and target modalities. We then apply our neural network operation that fine-tunes the target network with the additional knowledge transferred from the source network, while keeping the topology of the target network unchanged. While we present an audio-visual recognition task as an application of our approach, our framework is flexible and thus can work with any multimodal dataset, or with any already-existing deep networks that share the common underlying semantics. In this work in progress report, we aim to provide comprehensive results of different configurations of the proposed approach on two widely used audio-visual datasets, and we discuss potential applications of the proposed approach.