Deep Triplet Neural Networks with Cluster-CCA for Audio-Visual Cross-Modal Retrieval

Abstract

Cross-modal retrieval aims to retrieve data in one modality by a query in another modality, which has been a very interesting research issue in the field of multimedia, information retrieval, and computer vision, and database. Most existing works focus on cross-modal retrieval between text-image, text-video, and lyrics-audio. Little research addresses cross-modal retrieval between audio and video due to limited audio-video paired datasets and semantic information. The main challenge of the audio-visual cross-modal retrieval task focuses on learning joint embeddings from a shared subspace for computing the similarity across different modalities, where generating new representations is to maximize the correlation between audio and visual modalities space. In this work, we propose TNN-C-CCA, a novel deep triplet neural network with cluster canonical correlation analysis, which is an end-to-end supervised learning architecture with an audio branch and a video branch. We not only consider the matching pairs in the common space but also compute the mismatching pairs when maximizing the correlation. In particular, two significant contributions are made. First, a better representation by constructing a deep triplet neural network with triplet loss for optimal projections can be generated to maximize correlation in the shared subspace. Second, positive examples and negative examples are used in the learning stage to improve the capability of embedding learning between audio and video. Our experiment is run over fivefold cross validation, where average performance is applied to demonstrate the performance of audio-video cross-modal retrieval. The experimental results achieved on two different audio-visual datasets show that the proposed learning architecture with two branches outperforms existing six canonical correlation analysis–based methods and four state-of-the-art-based cross-modal retrieval methods.