{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/feature-selective-anchor-free-module-for","title":"Feature Selective Anchor-Free Module for Single-Shot Object Detection","arxiv_id":"1903.00621","date":"2019-03-02","proceeding":"CVPR 2019 6","authors":["Chenchen Zhu","Yihui He","Marios Savvides"],"abstract":"We motivate and present feature selective anchor-free (FSAF) module, a simple\nand effective building block for single-shot object detectors. It can be\nplugged into single-shot detectors with feature pyramid structure. The FSAF\nmodule addresses two limitations brought up by the conventional anchor-based\ndetection: 1) heuristic-guided feature selection; 2) overlap-based anchor\nsampling. The general concept of the FSAF module is online feature selection\napplied to the training of multi-level anchor-free branches. Specifically, an\nanchor-free branch is attached to each level of the feature pyramid, allowing\nbox encoding and decoding in the anchor-free manner at an arbitrary level.\nDuring training, we dynamically assign each instance to the most suitable\nfeature level. At the time of inference, the FSAF module can work jointly with\nanchor-based branches by outputting predictions in parallel. We instantiate\nthis concept with simple implementations of anchor-free branches and online\nfeature selection strategy. Experimental results on the COCO detection track\nshow that our FSAF module performs better than anchor-based counterparts while\nbeing faster. When working jointly with anchor-based branches, the FSAF module\nrobustly improves the baseline RetinaNet by a large margin under various\nsettings, while introducing nearly free inference overhead. And the resulting\nbest model can achieve a state-of-the-art 44.6% mAP, outperforming all existing\nsingle-shot detectors on COCO.","url_abs":"http://arxiv.org/abs/1903.00621v1","url_pdf":"http://arxiv.org/pdf/1903.00621v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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Detection"},{"task_slug":"feature-selection","task_name":"feature selection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"fpn","method_name":"FPN"},{"method_slug":"fsaf","method_name":"FSAF"},{"method_slug":"focal-loss","method_name":"Focal Loss"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"grouped-convolution","method_name":"Grouped Convolution"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"non-maximum-suppression","method_name":"Non 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