# Detect Every Thing with Few Examples

**Authors:** Xinyu Zhang, Yuting Wang, Abdeslam Boularias  
**arXiv:** [2309.12969](https://arxiv.org/abs/2309.12969)  
**Date:** 2023-09-22  
**Summary page:** https://www.summarizepaper.com/en/paper/2309.12969v2/

## Key points

- DE-ViT is an open-set object detector for detecting arbitrary categories beyond those seen during training.
- It uses vision-only DINOv2 backbones and learns new categories through example images instead of language.
- DE-ViT transforms multi-classification tasks into binary classification tasks, improving general detection ability.
- It introduces a novel region propagation technique for localization.
- DE-ViT's performance is evaluated on open-vocabulary, few-shot, and one-shot object detection benchmarks using COCO and LVIS datasets.
- In terms of open-vocabulary detection on COCO, DE-ViT outperforms the state-of-the-art (SoTA) by achieving a 6.9 AP50 improvement and reaching 50 AP50 in novel classes.

## Summary

DE-ViT is an open-set object detector that addresses the challenge of detecting arbitrary categories beyond those seen during training. Unlike previous approaches that utilize vision-language backbones, DE-ViT employs vision-only DINOv2 backbones and learns new categories through example images instead of language. This approach improves general detection ability by transforming multi-classification tasks into binary classification tasks and bypassing per-class inference. Additionally, DE-ViT introduces a novel region propagation technique for localization. The performance of DE-ViT is evaluated on open-vocabulary, few-shot, and one-shot object detection benchmarks using COCO and LVIS datasets. In terms of open-vocabulary detection on COCO, DE-ViT outperforms the state-of-the-art (SoTA) by achieving a 6.9 AP50 improvement and reaching 50 AP50 in novel classes.