{"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/finding-tiny-faces","title":"Finding Tiny Faces","arxiv_id":"1612.04402","date":"2016-12-13","proceeding":"CVPR 2017 7","authors":["Peiyun Hu","Deva Ramanan"],"abstract":"Though tremendous strides have been made in object recognition, one of the\nremaining open challenges is detecting small objects. We explore three aspects\nof the problem in the context of finding small faces: the role of scale\ninvariance, image resolution, and contextual reasoning. While most recognition\napproaches aim to be scale-invariant, the cues for recognizing a 3px tall face\nare fundamentally different than those for recognizing a 300px tall face. We\ntake a different approach and train separate detectors for different scales. To\nmaintain efficiency, detectors are trained in a multi-task fashion: they make\nuse of features extracted from multiple layers of single (deep) feature\nhierarchy. While training detectors for large objects is straightforward, the\ncrucial challenge remains training detectors for small objects. We show that\ncontext is crucial, and define templates that make use of massively-large\nreceptive fields (where 99% of the template extends beyond the object of\ninterest). Finally, we explore the role of scale in pre-trained deep networks,\nproviding ways to extrapolate networks tuned for limited scales to rather\nextreme ranges. We demonstrate state-of-the-art results on\nmassively-benchmarked face datasets (FDDB and WIDER FACE). In particular, when\ncompared to prior art on WIDER FACE, our results reduce error by a factor of 2\n(our models produce an AP of 82% while prior art ranges from 29-64%).","url_abs":"http://arxiv.org/abs/1612.04402v2","url_pdf":"http://arxiv.org/pdf/1612.04402v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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