TVDIM: Enhancing Image Self-Supervised Pretraining via Noisy Text Data
- URL: http://arxiv.org/abs/2106.01797v1
- Date: Thu, 3 Jun 2021 12:36:01 GMT
- Title: TVDIM: Enhancing Image Self-Supervised Pretraining via Noisy Text Data
- Authors: Pengda Qin and Yuhong Li
- Abstract summary: We propose Text-enhanced Visual Deep InfoMax (TVDIM) to learn better visual representations.
Our core idea of self-supervised learning is to maximize the mutual information between features extracted from multiple views.
TVDIM significantly outperforms previous visual self-supervised methods when processing the same set of images.
- Score: 13.68491474904529
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Among ubiquitous multimodal data in the real world, text is the modality
generated by human, while image reflects the physical world honestly. In a
visual understanding application, machines are expected to understand images
like human. Inspired by this, we propose a novel self-supervised learning
method, named Text-enhanced Visual Deep InfoMax (TVDIM), to learn better visual
representations by fully utilizing the naturally-existing multimodal data. Our
core idea of self-supervised learning is to maximize the mutual information
between features extracted from multiple views of a shared context to a
rational degree. Different from previous methods which only consider multiple
views from a single modality, our work produces multiple views from different
modalities, and jointly optimizes the mutual information for features pairs of
intra-modality and inter-modality. Considering the information gap between
inter-modality features pairs from data noise, we adopt a \emph{ranking-based}
contrastive learning to optimize the mutual information. During evaluation, we
directly use the pre-trained visual representations to complete various image
classification tasks. Experimental results show that, TVDIM significantly
outperforms previous visual self-supervised methods when processing the same
set of images.
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