mlsanityData to 14 Oct 2026

SaccadeDet: A Novel Dual-Stage Architecture for Rapid and Accurate Detection in Gigapixel Images

arXiv 2407.17956 ↗First posted 25 Jul 2024cs.CVcited by 5

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Abstract (arXiv)

The advancement of deep learning in object detection has predominantly focused on megapixel images, leaving a critical gap in the efficient processing of gigapixel images. These super high-resolution images present unique challenges due to their immense size and computational demands. To address this, we introduce 'SaccadeDet', an innovative architecture for gigapixel-level object detection, inspired by the human eye saccadic movement. The cornerstone of SaccadeDet is its ability to strategically select and process image regions, dramatically reducing computational load. This is achieved through a two-stage process: the 'saccade' stage, which identifies regions of probable interest, and the 'gaze' stage, which refines detection in these targeted areas. Our approach, evaluated on the PANDA dataset, not only achieves an 8x speed increase over the state-of-the-art methods but also demonstrates significant potential in gigapixel-level pathology analysis through its application to Whole Slide Imaging.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 34 of its 52 references that are in the map. First posted on arXiv 25 Jul 2024.

Builds on

34 of 52 references.

References · 34

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22 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025SparseFormer: Detecting Objects in HRW Shots via Sparse Vision Transformercited by 7
  2. 2025DyCrowd: Towards Dynamic Crowd Reconstruction from a Large-scene Videocited by 3
  3. 2026Adaptive Image Zoom-in with Bounding Box Transformation for UAV Object Detectioncited by 2
  4. 2026UHR-DETR: Efficient End-to-End Small Object Detection for Ultra-High-Resolution Remote Sensing Imagerycited by 0
  5. 2026Increasing the Efficiency of DETR for Maritime High-Resolution Imagescited by 0