mlsanityData to 7 Oct 2026

Continual Object Detection via Prototypical Task Correlation Guided Gating Mechanism

arXiv 2205.03055 ↗First posted 6 May 2022cs.CVcited by 20

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Abstract (arXiv)

Continual learning is a challenging real-world problem for constructing a mature AI system when data are provided in a streaming fashion. Despite recent progress in continual classification, the researches of continual object detection are impeded by the diverse sizes and numbers of objects in each image. Different from previous works that tune the whole network for all tasks, in this work, we present a simple and flexible framework for continual object detection via pRotOtypical taSk corrElaTion guided gaTing mechAnism (ROSETTA). Concretely, a unified framework is shared by all tasks while task-aware gates are introduced to automatically select sub-models for specific tasks. In this way, various knowledge can be successively memorized by storing their corresponding sub-model weights in this system. To make ROSETTA automatically determine which experience is available and useful, a prototypical task correlation guided Gating Diversity Controller(GDC) is introduced to adaptively adjust the diversity of gates for the new task based on class-specific prototypes. GDC module computes class-to-class correlation matrix to depict the cross-task correlation, and hereby activates more exclusive gates for the new task if a significant domain gap is observed. Comprehensive experiments on COCO-VOC, KITTI-Kitchen, class-incremental detection on VOC and sequential learning of four tasks show that ROSETTA yields state-of-the-art performance on both task-based and class-based continual object detection.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 41 of its 54 references that are in the map. First posted on arXiv 6 May 2022.

Builds on

41 of 54 references.

References · 41

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29 more
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  2. 2018Efficient Lifelong Learning with A-GEMcited by 1,044
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 7; 2024: 4; 2025: 6; 2026: 2.

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  1. 2023SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagerycited by 202
  2. 2023SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained Modelcited by 178
  3. 2023Continual Detection Transformer for Incremental Object Detectioncited by 59
  4. 2023Weighted Ensemble Models Are Strong Continual Learnerscited by 41
  5. 2024SLCA++: Unleash the Power of Sequential Fine-tuning for Continual Learning with Pre-trainingcited by 10
  6. 2022Learning with Style: Continual Semantic Segmentation Across Tasks and Domainscited by 9
  7. 2024Preventing Catastrophic Forgetting through Memory Networks in Continuous Detectioncited by 7
  8. 2024ATLAS: Adapter-Based Multi-Modal Continual Learning with a Two-Stage Learning Strategycited by 7
  9. 2023Unsupervised Recognition of Unknown Objects for Open-World Object Detectioncited by 6
  10. 2025RICO: Two Realistic Benchmarks and an In-Depth Analysis for Incremental Learning in Object Detectioncited by 5
  11. 2025Looking Beyond the Known: Towards a Data Discovery Guided Open-World Object Detectioncited by 4
  12. 2023Out-of-distribution forgetting: vulnerability of continual learning to intra-class distribution shiftcited by 3
8 more
  1. 2024IOR: Inversed Objects Replay for Incremental Object Detectioncited by 2
  2. 2025C-LoRA: Continual Low-Rank Adaptation for Pre-trained Visual Modelscited by 2
  3. 2025Incremental Object Detection with Prompt-based Methodscited by 2
  4. 2023CMFDFormer: Transformer-based Copy-Move Forgery Detection with Continual Learningcited by 1
  5. 2025Lifelong Domain Adaptive 3D Human Pose Estimationcited by 1
  6. 2026Knowing the Unknown: Interpretable Open-World Object Detection via Concept Decomposition Modelcited by 1
  7. 2025Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapsecited by 0
  8. 2026Hierarchical Dual-Subspace Decoupling for Continual Learning in Vision-Language Modelscited by 0