mlsanityData to 14 Oct 2026

Sylph: A Hypernetwork Framework for Incremental Few-shot Object Detection

arXiv 2203.13903 ↗First posted 25 Mar 2022cs.CVcited by 23

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Abstract (arXiv)

We study the challenging incremental few-shot object detection (iFSD) setting. Recently, hypernetwork-based approaches have been studied in the context of continuous and finetune-free iFSD with limited success. We take a closer look at important design choices of such methods, leading to several key improvements and resulting in a more accurate and flexible framework, which we call Sylph. In particular, we demonstrate the effectiveness of decoupling object classification from localization by leveraging a base detector that is pretrained for class-agnostic localization on a large-scale dataset. Contrary to what previous results have suggested, we show that with a carefully designed class-conditional hypernetwork, finetune-free iFSD can be highly effective, especially when a large number of base categories with abundant data are available for meta-training, almost approaching alternatives that undergo test-time-training. This result is even more significant considering its many practical advantages: (1) incrementally learning new classes in sequence without additional training, (2) detecting both novel and seen classes in a single pass, and (3) no forgetting of previously seen classes. We benchmark our model on both COCO and LVIS, reporting as high as 17% AP on the long-tail rare classes on LVIS, indicating the promise of hypernetwork-based iFSD.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). Builds on 53 of its 72 references that are in the map. First posted on arXiv 25 Mar 2022.

Builds on

53 of 72 references.

References · 53

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38 more
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Citing papers per year: 2022: 5; 2023: 13; 2024: 3; 2025: 1; 2026: 1.

23 papers.

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  1. 2023Class-Incremental Learning: A Surveycited by 209
  2. 2023A Brief Review of Hypernetworks in Deep Learningcited by 102
  3. 2023A Survey on Few-Shot Class-Incremental Learningcited by 48
  4. 2022EgoLoc: Revisiting 3D Object Localization from Egocentric Videos with Visual Queriescited by 25
  5. 2023Few-shot Class-incremental Learning for Classification and Object Detection: A Surveycited by 25
  6. 2022Where is my Wallet? Modeling Object Proposal Sets for Egocentric Visual Query Localizationcited by 24
  7. 2022Few-shot Learning with Noisy Labelscited by 23
  8. 2023Multimodal Parameter-Efficient Few-Shot Class Incremental Learningcited by 19
  9. 2024Zero-shot Generalizable Incremental Learning for Vision-Language Object Detectioncited by 15
  10. 2023When Meta-Learning Meets Online and Continual Learning: A Surveycited by 12
  11. 2022Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparisoncited by 8
  12. 2023Identification of Novel Classes for Improving Few-Shot Object Detectioncited by 7
11 more
  1. 2023Improved Region Proposal Network for Enhanced Few-Shot Object Detectioncited by 5
  2. 2022Few-shot Object Detection with Refined Contrastive Learningcited by 4
  3. 2023Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detectioncited by 4
  4. 2023Continual HyperTransformer: A Meta-Learner for Continual Few-Shot Learningcited by 3
  5. 2023Incremental Few-Shot Object Detection via Simple Fine-Tuning Approachcited by 2
  6. 2026Few-Shot Incremental 3D Object Detection in Dynamic Indoor Environmentscited by 2
  7. 2023CMFDFormer: Transformer-based Copy-Move Forgery Detection with Continual Learningcited by 1
  8. 2024Evaluating the Energy Efficiency of Few-Shot Learning for Object Detection in Industrial Settingscited by 1
  9. 2023Learning to Generate Parameters of ConvNets for Unseen Image Datacited by 0
  10. 2024InfRS: Incremental Few-Shot Object Detection in Remote Sensing Imagescited by 0
  11. 2025ZeBROD: Zero-Retraining Based Recognition and Object Detection Frameworkcited by 0