Methods › Computer Vision › Image Representations › VirTex

VirTex

6 papers tagged archive 2025-07-28

Introduced by Karan Desai et al. in VirTex: Learning Visual Representations from Textual Annotations

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

VirText, or Visual representations from Textual annotations is a pretraining approach using semantically dense captions to learn visual representations. First a ConvNet and Transformer are jointly trained from scratch to generate natural language captions for images. Then, the learned features are transferred to downstream visual recognition tasks.

PaperSource

Papers archive 2025-07-28

6 shown of 6, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

16 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
8k1
CPU1
Edge-computing1
GPU1
General Classification1
High-Level Synthesis1
Image Captioning1
Image Classification1
Image Reconstruction1
Instance Segmentation1
Object Detection1
Quantization1
Semantic Segmentation1
compressed sensing1
image-classification1
object-detection1

Usage over time archive 2025-07-28

Papers per year tagged with VirTex: 2020 to 2024, peak 2 2 0 2020: 2 papers 2020 2021: 2 papers 2021 2022: 0 papers 2022 2023: 0 papers 2023 2024: 2 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (6 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Image Representations

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