Research map / Detection and segmentation / Open-set and few-shot detection
VirTex: Learning Visual Representations From Textual Annotations
Check your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use caption-based VirTex pretraining instead of training on cropped COCO images with ImageNet-supervised hyperparameters | ResNet-50 · VOC classification | mAP: 79.1 -> 88.5 | Helped |
Closest papers by meaning
- Visual Lexicon: Rich Image Features in Language Space (CVPR 2025 · similarity 0.87)
- Rejuvenating image-GPT as Strong Visual Representation Learners (ICML 2024 · similarity 0.86)
- UVU: Improving Multimodal Understanding via Vision-Language Unified Autoregressive Paradigm (CVPR 2026 · similarity 0.86)
- Perception Encoder: The best visual embeddings are not at the output of the network (NeurIPS 2025 · similarity 0.86)
- Vision Transformers Need Registers (ICLR 2024 · similarity 0.86)
- T-MARS: Improving Visual Representations by Circumventing Text Feature Learning (ICLR 2024 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.