# Generating Features with Increased Crop-related Diversity for Few-Shot Object Detection

arXiv: https://arxiv.org/abs/2304.05096 · HTML: https://mlsanity.com/paper/2304.05096/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 60 references that are in the map.
- First posted on arXiv 11 Apr 2023.

## Builds on (most cited first, 30 of 40)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [MagFace: A Universal Representation for Face Recognition and Quality Assessment](https://mlsanity.com/paper/2103.06627/) (2021)
- [Frustratingly Simple Few-Shot Object Detection](https://mlsanity.com/paper/2003.06957/) (2020)
- [Few-shot Object Detection via Feature Reweighting](https://mlsanity.com/paper/1812.01866/) (2018)
- [Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector](https://mlsanity.com/paper/1908.01998/) (2019)
- [Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning](https://mlsanity.com/paper/1909.13032/) (2019)
- [FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding](https://mlsanity.com/paper/2103.05950/) (2021)
- [f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning](https://mlsanity.com/paper/1903.10132/) (2019)
- [Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild](https://mlsanity.com/paper/2007.12107/) (2020)
- [Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation](https://mlsanity.com/paper/2007.09886/) (2020)
- [Multi-Scale Positive Sample Refinement for Few-Shot Object Detection](https://mlsanity.com/paper/2007.09384/) (2020)
- [DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection](https://mlsanity.com/paper/2108.09017/) (2021)
- [Incremental Few-Shot Object Detection](https://mlsanity.com/paper/2003.04668/) (2020)
- [Generalized Few-Shot Object Detection without Forgetting](https://mlsanity.com/paper/2105.09491/) (2021)
- [RepMet: Representative-based metric learning for classification and one-shot object detection](https://mlsanity.com/paper/1806.04728/) (2018)
- [Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection](https://mlsanity.com/paper/2103.04612/) (2021)
- [Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection](https://mlsanity.com/paper/2103.01903/) (2021)
- [Few-Shot Object Detection with Fully Cross-Transformer](https://mlsanity.com/paper/2203.15021/) (2022)
- [Dont Even Look Once: Synthesizing Features for Zero-Shot Detection](https://mlsanity.com/paper/1911.07933/) (2019)
- [Query Adaptive Few-Shot Object Detection with Heterogeneous Graph Convolutional Networks](https://mlsanity.com/paper/2112.09791/) (2021)
- [Learning Latent Subspaces in Variational Autoencoders](https://mlsanity.com/paper/1812.06190/) (2018)
- [Hallucination Improves Few-Shot Object Detection](https://mlsanity.com/paper/2105.01294/) (2021)
- [Universal-Prototype Enhancing for Few-Shot Object Detection](https://mlsanity.com/paper/2103.01077/) (2021)
- [Label, Verify, Correct: A Simple Few Shot Object Detection Method](https://mlsanity.com/paper/2112.05749/) (2021)
- [Few-Shot Object Detection via Association and DIscrimination](https://mlsanity.com/paper/2111.11656/) (2021)

## Built on by (most cited first, 21 of 21)

- [Revisiting Few-Shot Object Detection with Vision-Language Models](https://mlsanity.com/paper/2312.14494/) (2023)
- [All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds](https://mlsanity.com/paper/2411.18810/) (2024)
- [Adapting Pre-Trained Vision Models for Novel Instance Detection and Segmentation](https://mlsanity.com/paper/2405.17859/) (2024)
- [Assessing Sample Quality via the Latent Space of Generative Models](https://mlsanity.com/paper/2407.15171/) (2024)
- [Multi-Perspective Data Augmentation for Few-shot Object Detection](https://mlsanity.com/paper/2502.18195/) (2025)
- [Generalized Semantic Contrastive Learning via Embedding Side Information for Few-Shot Object Detection](https://mlsanity.com/paper/2504.07060/) (2025)
- [Visual Textualization for Image Prompted Object Detection](https://mlsanity.com/paper/2506.23785/) (2025)
- [The Art of Camouflage: Few-Shot Learning for Animal Detection and Segmentation](https://mlsanity.com/paper/2304.07444/) (2023)
- [BIV-Priv-Seg: Locating Private Content in Images Taken by People With Visual Impairments](https://mlsanity.com/paper/2407.18243/) (2024)
- [Shadow Removal Refinement via Material-Consistent Shadow Edges](https://mlsanity.com/paper/2409.06848/) (2024)
- [Few-shot Personalized Scanpath Prediction](https://mlsanity.com/paper/2504.05499/) (2025)
- [Controlling the Fidelity and Diversity of Deep Generative Models via Pseudo Density](https://mlsanity.com/paper/2407.08659/) (2024)
- [Weighting Pseudo-Labels via High-Activation Feature Index Similarity and Object Detection for Semi-Supervised Segmentation](https://mlsanity.com/paper/2407.12630/) (2024)
- [A Closer Look at Data Augmentation Strategies for Finetuning-Based Low/Few-Shot Object Detection](https://mlsanity.com/paper/2408.10940/) (2024)
- [DetPO: In-Context Learning with Multi-Modal LLMs for Few-Shot Object Detection](https://mlsanity.com/paper/2603.23455/) (2026)
- [Decoupled DETR For Few-shot Object Detection](https://mlsanity.com/paper/2311.11570/) (2023)
- [CORA: Consistency-Guided Semi-Supervised Framework for Reasoning Segmentation](https://mlsanity.com/paper/2511.17755/) (2025)
- [Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection](https://mlsanity.com/paper/2506.22161/) (2025)
- [Few-Shot Object Detection via Spatial-Channel State Space Model](https://mlsanity.com/paper/2507.15308/) (2025)
- [Cast and Attached Shadow Detection via Iterative Light and Geometry Reasoning](https://mlsanity.com/paper/2512.06179/) (2025)
- [Boosting Text-to-Image Diffusion Models via Core Token Attention-Based Seed Selection](https://mlsanity.com/paper/2605.19532/) (2026)

## Abstract (arXiv)

Two-stage object detectors generate object proposals and classify them to detect objects in images. These proposals often do not contain the objects perfectly but overlap with them in many possible ways, exhibiting great variability in the difficulty levels of the proposals. Training a robust classifier against this crop-related variability requires abundant training data, which is not available in few-shot settings. To mitigate this issue, we propose a novel variational autoencoder (VAE) based data generation model, which is capable of generating data with increased crop-related diversity. The main idea is to transform the latent space such latent codes with different norms represent different crop-related variations. This allows us to generate features with increased crop-related diversity in difficulty levels by simply varying the latent norm. In particular, each latent code is rescaled such that its norm linearly correlates with the IoU score of the input crop w.r.t. the ground-truth box. Here the IoU score is a proxy that represents the difficulty level of the crop. We train this VAE model on base classes conditioned on the semantic code of each class and then use the trained model to generate features for novel classes. In our experiments our generated features consistently improve state-of-the-art few-shot object detection methods on the PASCAL VOC and MS COCO datasets.