Donghuo Zeng

Donghuo Zeng, Yanan Wang, Jianming Wu et al.

The heterogeneity gap problem is the main challenge in cross-modal retrieval. Because cross-modal data (e.g. audiovisual) have different distributions and representations that cannot be directly compared. To bridge the gap between audiovisual modalities, we learn a common subspace for them by utilizing the intrinsic correlation in the natural synchronization of audio-visual data with the aid of annotated labels. TNN-CCCA is the best audio-visual cross-modal retrieval (AV-CMR) model so far, but the model training is sensitive to hard negative samples when learning common subspace by applying triplet loss to predict the relative distance between inputs. In this paper, to reduce the interference of hard negative samples in representation learning, we propose a new AV-CMR model to optimize semantic features by directly predicting labels and then measuring the intrinsic correlation between audio-visual data using complete cross-triple loss. In particular, our model projects audio-visual features into label space by minimizing the distance between predicted label features after feature projection and ground label representations. Moreover, we adopt complete cross-triplet loss to optimize the predicted label features by leveraging the relationship between all possible similarity and dissimilarity semantic information across modalities. The extensive experimental results on two audio-visual double-checked datasets have shown an improvement of approximately 2.1% in terms of average MAP over the current state-of-the-art method TNN-CCCA for the AV-CMR task, which indicates the effectiveness of our proposed model.

Donghuo Zeng, Yi Yu, Keizo Oyama

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 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 a novel deep triplet neural network with cluster canonical correlation analysis(TNN-C-CCA), which is an end-to-end supervised learning architecture with audio branch and 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: i) a better representation by constructing deep triplet neural network with triplet loss for optimal projections can be generated to maximize correlation in the shared subspace. ii) 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 5-fold 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 the proposed learning architecture with two branches outperforms existing six CCA-based methods and four state-of-the-art based cross-modal retrieval methods.