COBRA: Contrastive Bi-Modal Representation Algorithm

• URL: http://arxiv.org/abs/2005.03687v2
• Date: Sun, 24 May 2020 20:07:52 GMT
• Title: COBRA: Contrastive Bi-Modal Representation Algorithm
• Authors: Vishaal Udandarao, Abhishek Maiti, Deepak Srivatsav, Suryatej Reddy Vyalla, Yifang Yin, Rajiv Ratn Shah
• Abstract summary: We present a novel framework that aims to train two modalities in a joint fashion inspired by Contrastive Predictive Coding (CPC) and Noise Contrastive Estimation (NCE) paradigms. 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.
• Score: 43.33840912256077
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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