Papers › VirTex: Learning Visual Representations from Textual Annotations
VirTex: Learning Visual Representations from Textual Annotations
Karan Desai, Justin Johnson
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Captioning | COCO Captions | Virtex (ResNet-101) | CIDER | 94 | #38 of 41 | Archive leaderboard | report |
| Image Captioning | COCO Captions | Virtex (ResNet-101) | SPICE | 18.5 | #38 of 41 | Archive leaderboard | report |
| Instance Segmentation | COCO test-dev | VirTex Mask R-CNN (ResNet-50-FPN) | AP50 | 58.4 | #95 of 112 | Archive leaderboard | report |
| Instance Segmentation | COCO test-dev | VirTex Mask R-CNN (ResNet-50-FPN) | AP75 | 39.7 | #95 of 112 | Archive leaderboard | report |
| Instance Segmentation | COCO test-dev | VirTex Mask R-CNN (ResNet-50-FPN) | mask AP | 36.9 | #95 of 112 | Archive leaderboard | report |
| Object Detection | COCO minival | VirTex Mask R-CNN (ResNet-50-FPN) | box AP | 40.9 | #171 of 220 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: VirTex
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