COBRA: Contrastive Bi-Modal Representation Algorithm

There are a wide range of applications that involve multi-modal data, such as cross-modal retrieval, visual question-answering, and image captioning. Such applications are primarily dependent on aligned distributions of the different constituent modalities. Existing approaches generate latent embedd...

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Bibliographic Details
Published in:arXiv.org 2020-05
Main Authors: Udandarao, Vishaal, Maiti, Abhishek, Srivatsav, Deepak, Suryatej Reddy Vyalla, Yin, Yifang, Shah, Rajiv Ratn
Format: Article
Language:English
Subjects:
Algorithms
Embedding
Modal data
Noise prediction
Online Access:Get full text
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Summary:There are a wide range of applications that involve multi-modal data, such as cross-modal retrieval, visual question-answering, and image captioning. Such applications are primarily dependent on aligned distributions of the different constituent modalities. Existing approaches generate latent embeddings for each modality in a joint fashion by representing them in a common manifold. However these joint embedding spaces fail to sufficiently reduce the modality gap, which affects the performance in downstream tasks. We hypothesize that these embeddings retain the intra-class relationships but are unable to preserve the inter-class dynamics. In this paper, we present a novel framework COBRA that aims to train two modalities (image and text) in a joint fashion inspired by the Contrastive Predictive Coding (CPC) and Noise Contrastive Estimation (NCE) paradigms which preserve both inter and intra-class relationships. We empirically show that this framework reduces the modality gap significantly and generates a robust and task agnostic joint-embedding space. We outperform existing work on four diverse downstream tasks spanning across seven benchmark cross-modal datasets.
ISSN:2331-8422