# Target Driven Instance Detection

arXiv: https://arxiv.org/abs/1803.04610 · HTML: https://mlsanity.com/paper/1803.04610/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2018.

## Built on by (most cited first, 14 of 14)

- [Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB Images](https://mlsanity.com/paper/2204.10776/) (2022)
- [Semantically Grounded Object Matching for Robust Robotic Scene Rearrangement](https://mlsanity.com/paper/2111.07975/) (2021)
- [Adapting Pre-Trained Vision Models for Novel Instance Detection and Segmentation](https://mlsanity.com/paper/2405.17859/) (2024)
- [OSSID: Online Self-Supervised Instance Detection by (and for) Pose Estimation](https://mlsanity.com/paper/2201.07309/) (2022)
- [VoxDet: Voxel Learning for Novel Instance Detection](https://mlsanity.com/paper/2305.17220/) (2023)
- [SPVLoc: Semantic Panoramic Viewport Matching for 6D Camera Localization in Unseen Environments](https://mlsanity.com/paper/2404.10527/) (2024)
- [A High-Resolution Dataset for Instance Detection with Multi-View Instance Capture](https://mlsanity.com/paper/2310.19257/) (2023)
- [Label-Free Synthetic Pretraining of Object Detectors](https://mlsanity.com/paper/2208.04268/) (2022)
- [DoUnseen: Tuning-Free Class-Adaptive Object Detection of Unseen Objects for Robotic Grasping](https://mlsanity.com/paper/2304.02833/) (2023)
- [AccessLens: Auto-detecting Inaccessibility of Everyday Objects](https://mlsanity.com/paper/2401.15996/) (2024)
- [Gram-SLD: Automatic Self-labeling and Detection for Instance Objects](https://mlsanity.com/paper/2112.03641/) (2021)
- [One-Shot General Object Localization](https://mlsanity.com/paper/2211.13392/) (2022)
- [Few-shot target-driven instance detection based on open-vocabulary object detection models](https://mlsanity.com/paper/2410.16028/) (2024)
- [From Local Matches to Global Masks: Template-Guided Instance Detection and Segmentation in Open-World Scenes](https://mlsanity.com/paper/2603.03577/) (2026)

## Abstract (arXiv)

While state-of-the-art general object detectors are getting better and better, there are not many systems specifically designed to take advantage of the instance detection problem. For many applications, such as household robotics, a system may need to recognize a few very specific instances at a time. Speed can be critical in these applications, as can the need to recognize previously unseen instances. We introduce a Target Driven Instance Detector(TDID), which modifies existing general object detectors for the instance recognition setting. TDID not only improves performance on instances seen during training, with a fast runtime, but is also able to generalize to detect novel instances.