PerSense: Training-Free Personalized Instance Segmentation in Dense Images


Muhammad Ibraheem Siddiqui (Mohamed bin Zayed University of Artificial Intelligence), Muhammad Umer Sheikh (Mohamed bin Zayed University of Artificial Intelligence), Hassan Abid (Mohamed bin Zayed University of Artificial Intelligence), Muhammad Haris Khan (Mohamed Bin Zayed University of Artificial Intelligence)
The 35th British Machine Vision Conference
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Abstract

The emergence of foundational models has significantly advanced segmentation approaches. However, challenges still remain in dense scenarios, where occlusions, scale variations, and clutter impede precise instance delineation. To address this, we propose PerSense, an end-to-end, training-free, and model-agnostic one-shot framework for personalized instance segmentation in dense images. We start with developing a new baseline capable of automatically generating instance-level point prompts via proposing a novel Instance Detection Module (IDM) that leverages density maps (DMs), encapsulating spatial distribution of objects in an image. To reduce false positives, we design the Point Prompt Selection Module (PPSM), which refines the output of IDM based on an adaptive threshold and spatial gating. Both IDM and PPSM seamlessly integrate into our model-agnostic framework. Furthermore, we introduce a feedback mechanism that enables PerSense to improve the accuracy of DMs by automating the exemplar selection process for DM generation. Finally, to advance research in this relatively underexplored area, we introduce PerSense-D, an evaluation benchmark for instance segmentation in dense images. Our extensive experiments establish PerSense's superiority over SOTA in dense settings.

Citation

@inproceedings{Siddiqui_2025_BMVC,
author    = {Muhammad Ibraheem Siddiqui and Muhammad Umer Sheikh and Hassan Abid and Muhammad Haris Khan},
title     = {PerSense: Training-Free Personalized Instance Segmentation in Dense Images},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_614/paper.pdf}
}


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