mlsanityData to 7 Oct 2026

Patch-based Selection and Refinement for Early Object Detection

arXiv 2311.02274 ↗First posted 3 Nov 2023cs.CVcited by 5

Not on the map. Why?

Abstract (arXiv)

Early object detection (OD) is a crucial task for the safety of many dynamic systems. Current OD algorithms have limited success for small objects at a long distance. To improve the accuracy and efficiency of such a task, we propose a novel set of algorithms that divide the image into patches, select patches with objects at various scales, elaborate the details of a small object, and detect it as early as possible. Our approach is built upon a transformer-based network and integrates the diffusion model to improve the detection accuracy. As demonstrated on BDD100K, our algorithms enhance the mAP for small objects from 1.03 to 8.93, and reduce the data volume in computation by more than 77%. The source code is available at hrefhttps://github.com/destiny301/dprhttps://github.com/destiny301/dpr

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 35 of its 37 references that are in the map. First posted on arXiv 3 Nov 2023.

Builds on

35 of 37 references.

References · 35

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23 more
  1. 2022Generative Adversarial Networkscited by 5,603
  2. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  3. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  4. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
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  15. 2021Swin Transformer V2: Scaling Up Capacity and Resolutioncited by 1,215
  16. 2021Cascaded Diffusion Models for High Fidelity Image Generationcited by 1,155
  17. 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
  18. 2022Fast Sampling of Diffusion Models with Exponential Integratorcited by 526
  19. 2023Efficient Diffusion Training via Min-SNR Weighting Strategycited by 242
  20. 2022Fast Sampling of Diffusion Models via Operator Learningcited by 173
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 0; 2026: 2.

5 papers.

  1. 2023Transformer-based Selective Super-Resolution for Efficient Image Refinementcited by 7
  2. 2024Enhancing Exchange Rate Forecasting with Explainable Deep Learning Modelscited by 2
  3. 2024Context-Aware Token Selection and Packing for Enhanced Vision Transformercited by 1
  4. 2026Enhancing Open-Vocabulary Object Detection through Multi-Level Fine-Grained Visual-Language Alignmentcited by 0
  5. 2026DenseScout: Algorithm-System Co-design for Budgeted Tiny Object Selection on Edge Platformscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022DiffusionDet: Diffusion Model for Object Detection
  2. 2024DiffuBox: Refining 3D Object Detection with Point Diffusion
  3. 2025Bridging the Scale Gap: Balanced Tiny and General Object Detection in Remote Sensing Imagery
  4. 20233DiffTection: 3D Object Detection with Geometry-Aware Diffusion Features
  5. 2023Diffusion-based 3D Object Detection with Random Boxes
  6. 2025DisPatch: Disarming Adversarial Patches in Object Detection with Diffusion Models
  7. 2024ODGEN: Domain-specific Object Detection Data Generation with Diffusion Models
  8. 2024DiffYOLO: Object Detection for Anti-Noise via YOLO and Diffusion Models