Multimodal Contrastive Training for Visual Representation Learning
| Autor: | Jianming Zhang, Baldo Faieta, Xin Yuan, Zhe Lin, Yilin Wang, Michael Maire, Jason Kuen, Ajinkya Gorakhnath Kale |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Modality (human–computer interaction) Contextual image classification Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Image segmentation computer.software_genre Semantics Object detection Visualization Similarity (psychology) Artificial intelligence business computer Feature learning Natural language processing |
| Zdroj: | CVPR |
| DOI: | 10.48550/arxiv.2104.12836 |
| Popis: | We develop an approach to learning visual representations that embraces multimodal data, driven by a combination of intra- and inter-modal similarity preservation objectives. Unlike existing visual pre-training methods, which solve a proxy prediction task in a single domain, our method exploits intrinsic data properties within each modality and semantic information from cross-modal correlation simultaneously, hence improving the quality of learned visual representations. By including multimodal training in a unified framework with different types of contrastive losses, our method can learn more powerful and generic visual features. We first train our model on COCO and evaluate the learned visual representations on various downstream tasks including image classification, object detection, and instance segmentation. For example, the visual representations pre-trained on COCO by our method achieve state-of-the-art top-1 validation accuracy of $55.3\%$ on ImageNet classification, under the common transfer protocol. We also evaluate our method on the large-scale Stock images dataset and show its effectiveness on multi-label image tagging, and cross-modal retrieval tasks. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|