# GTNet: Generative Transfer Network for Zero-Shot Object Detection

arXiv: https://arxiv.org/abs/2001.06812 · HTML: https://mlsanity.com/paper/2001.06812/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2020.

## Built on by (most cited first, 25 of 25)

- [Open-Vocabulary DETR with Conditional Matching](https://mlsanity.com/paper/2203.11876/) (2022)
- [Open-Vocabulary One-Stage Detection with Hierarchical Visual-Language Knowledge Distillation](https://mlsanity.com/paper/2203.10593/) (2022)
- [Object-Aware Distillation Pyramid for Open-Vocabulary Object Detection](https://mlsanity.com/paper/2303.05892/) (2023)
- [A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Future](https://mlsanity.com/paper/2307.09220/) (2023)
- [CoDet: Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection](https://mlsanity.com/paper/2310.16667/) (2023)
- [Zero-Shot Instance Segmentation](https://mlsanity.com/paper/2104.06601/) (2021)
- [Semantics-Guided Contrastive Network for Zero-Shot Object detection](https://mlsanity.com/paper/2109.06062/) (2021)
- [Robust Region Feature Synthesizer for Zero-Shot Object Detection](https://mlsanity.com/paper/2201.00103/) (2022)
- [LaMI-DETR: Open-Vocabulary Detection with Language Model Instruction](https://mlsanity.com/paper/2407.11335/) (2024)
- [Semantic-Promoted Debiasing and Background Disambiguation for Zero-Shot Instance Segmentation](https://mlsanity.com/paper/2305.13173/) (2023)
- [Zero-Shot Aerial Object Detection with Visual Description Regularization](https://mlsanity.com/paper/2402.18233/) (2024)
- [Zero-shot Object Detection Through Vision-Language Embedding Alignment](https://mlsanity.com/paper/2109.12066/) (2021)
- [Fine-Grained Zero-Shot Object Detection](https://mlsanity.com/paper/2507.10358/) (2025)
- [Aligning Effective Tokens with Video Anomaly in Large Language Models](https://mlsanity.com/paper/2508.06350/) (2025)
- [A Survey of Deep Learning for Low-Shot Object Detection](https://mlsanity.com/paper/2112.02814/) (2021)
- [Fewer is More: Efficient Object Detection in Large Aerial Images](https://mlsanity.com/paper/2212.13136/) (2022)
- [SeeDS: Semantic Separable Diffusion Synthesizer for Zero-shot Food Detection](https://mlsanity.com/paper/2310.04689/) (2023)
- [Balanced Classification: A Unified Framework for Long-Tailed Object Detection](https://mlsanity.com/paper/2308.02213/) (2023)
- [Synthesizing Knowledge-enhanced Features for Real-world Zero-shot Food Detection](https://mlsanity.com/paper/2402.09242/) (2024)
- [Resolving Semantic Confusions for Improved Zero-Shot Detection](https://mlsanity.com/paper/2212.06097/) (2022)
- [Meta-ZSDETR: Zero-shot DETR with Meta-learning](https://mlsanity.com/paper/2308.09540/) (2023)
- [Review of Zero-Shot and Few-Shot AI Algorithms in The Medical Domain](https://mlsanity.com/paper/2406.16143/) (2024)
- [Simba: Towards High-Fidelity and Geometrically-Consistent Point Cloud Completion via Transformation Diffusion](https://mlsanity.com/paper/2511.16161/) (2025)
- [Separate Scene Text Detector for Unseen Scripts is Not All You Need](https://mlsanity.com/paper/2307.15991/) (2023)
- [Unveiling the Unknown: Open Vocabulary Object Detection with Scene Graphs](https://mlsanity.com/paper/2606.05916/) (2026)

## Abstract (arXiv)

We propose a Generative Transfer Network (GTNet) for zero shot object detection (ZSD). GTNet consists of an Object Detection Module and a Knowledge Transfer Module. The Object Detection Module can learn large-scale seen domain knowledge. The Knowledge Transfer Module leverages a feature synthesizer to generate unseen class features, which are applied to train a new classification layer for the Object Detection Module. In order to synthesize features for each unseen class with both the intra-class variance and the IoU variance, we design an IoU-Aware Generative Adversarial Network (IoUGAN) as the feature synthesizer, which can be easily integrated into GTNet. Specifically, IoUGAN consists of three unit models: Class Feature Generating Unit (CFU), Foreground Feature Generating Unit (FFU), and Background Feature Generating Unit (BFU). CFU generates unseen features with the intra-class variance conditioned on the class semantic embeddings. FFU and BFU add the IoU variance to the results of CFU, yielding class-specific foreground and background features, respectively. We evaluate our method on three public datasets and the results demonstrate that our method performs favorably against the state-of-the-art ZSD approaches.