Methods › General › Binary Neural Networks › BiDet
BiDet
Introduced by Ziwei Wang et al. in BiDet: An Efficient Binarized Object Detector
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
BiDet is a binarized neural network learning method for efficient object detection. Conventional network binarization methods directly quantize the weights and activations in one-stage or two-stage detectors with constrained representational capacity, so that the information redundancy in the networks causes numerous false positives and degrades the performance significantly. On the contrary, BiDet fully utilizes the representational capacity of the binary neural networks for object detection by redundancy removal, through which the detection precision is enhanced with alleviated false positives. Specifically, the information bottleneck (IB) principle is generalized to object detection, where the amount of information in the high-level feature maps is constrained and the mutual information between the feature maps and object detection is maximized.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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BiDet: An Efficient Binarized Object Detector 9 Mar 2020 · 2 repositories · arXiv:2003.03961
Tasks archive 2025-07-28
4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Binarization | 1 |
| Object | 1 |
| Object Detection | 1 |
| object-detection | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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