Papers › EXTD: Extremely Tiny Face Detector via Iterative Filter Reuse
EXTD: Extremely Tiny Face Detector via Iterative Filter Reuse
YoungJoon Yoo, Dongyoon Han, Sangdoo Yun
In this paper, we propose a new multi-scale face detector having an extremely tiny number of parameters (EXTD),less than 0.1 million, as well as achieving comparable performance to deep heavy detectors. While existing multi-scale face detectors extract feature maps with different scales from a single backbone network, our method generates the feature maps by iteratively reusing a shared lightweight and shallow backbone network. This iterative sharing of the backbone network significantly reduces the number of parameters, and also provides the abstract image semantics captured from the higher stage of the network layers to the lower-level feature map. The proposed idea is employed by various model architectures and evaluated by extensive experiments. From the experiments from WIDER FACE dataset, we show that the proposed face detector can handle faces with various scale and conditions, and achieved comparable performance to the more massive face detectors that few hundreds and tens times heavier in model size and floating point operations.
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Code
Syntology Ran 4 of 13 code samples harvested from 2 repositories linked to this paper; 9 have no recorded run. Of those that ran: 2 ran · our draft was wrong; 2 ran · fixture could not drive it.
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Face Detection | WIDER Face (Hard) | EXTD | AP | 0.850 | #22 of 40 | Archive leaderboard | report |
| Face Detection | WIDER Face (Medium) | EXTD | AP | 0.903 | #25 of 37 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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