# Comparison Network for One-Shot Conditional Object Detection

arXiv: https://arxiv.org/abs/1904.02317 · HTML: https://mlsanity.com/paper/1904.02317/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2019.

## Built on by (most cited first, 2 of 2)

- [Single-Stage Visual Query Localization in Egocentric Videos](https://mlsanity.com/paper/2306.09324/) (2023)
- [Joint Neural Networks for One-shot Object Recognition and Detection](https://mlsanity.com/paper/2408.00701/) (2024)

## Abstract (arXiv)

The current advances in object detection depend on large-scale datasets to get good performance. However, there may not always be sufficient samples in many scenarios, which leads to the research on few-shot detection as well as its extreme variation one-shot detection. In this paper, the one-shot detection has been formulated as a conditional probability problem. With this insight, a novel one-shot conditional object detection (OSCD) framework, referred as Comparison Network (ComparisonNet), has been proposed. Specifically, query and target image features are extracted through a Siamese network as mapped metrics of marginal probabilities. A two-stage detector for OSCD is introduced to compare the extracted query and target features with the learnable metric to approach the optimized non-linear conditional probability. Once trained, ComparisonNet can detect objects of both seen and unseen classes without further training, which also has the advantages including class-agnostic, training-free for unseen classes, and without catastrophic forgetting. Experiments show that the proposed approach achieves state-of-the-art performance on the proposed datasets of Fashion-MNIST and PASCAL VOC.