Details

Autor(en) / Beteiligte

• Desai, Karan
• Johnson, Justin

Titel

VirTex: Learning Visual Representations from Textual Annotations

Ist Teil von

• 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, p.11157-11168

Ort / Verlag

IEEE

Erscheinungsjahr

2021

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• The de-facto approach to many vision tasks is to start from pretrained visual representations, typically learned via supervised training on ImageNet. Recent methods have explored unsupervised pretraining to scale to vast quantities of unlabeled images. In contrast, we aim to learn high-quality visual representations from fewer images. To this end we revisit supervised pretraining, and seek data-efficient alternatives to classification-based pretraining. We propose VirTex - a pretraining approach using semantically dense captions to learn visual representations. We train convolutional networks from scratch on COCO Captions, and transfer them to downstream recognition tasks including image classification, object detection, and instance segmentation. On all tasks, VirTex yields features that match or exceed those learned on ImageNet - supervised or unsupervised - despite using up to ten times fewer images.