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Register a new userQuality Estimation for Image Captions Based on Large-scale Human Evaluations
تقدير الجودة لمعاملات الصور بناء على تقييمات بشرية واسعة النطاق
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تحسنت تقسيم الصور التلقائي بشكل كبير خلال السنوات القليلة الماضية، لكن المشكلة بعيدة عن حلها، حيث لا تزال حالة من النماذج الفنية غالبا ما تنتج توضيحات منخفضة الجودة عند استخدامها في البرية. في هذه الورقة، نركز على مهمة تقدير الجودة (QE) للحصول على تعليق الصور، والتي تحاول طراز جودة التسمية التوضيحية من منظور إنساني و * بدون * الوصول إلى مراجع الحقيقة الأرضية، بحيث يمكن تطبيقها في وقت التنبؤ للكشف عن التسميات التوضيحية منخفضة الجودة المنتجة على * الصور غير المرئية سابقا *. بالنسبة لهذه المهمة، نقوم بتطوير عملية تقييم بشرية تقوم بجمع التعليقات التوضيحية الشعوية من المستخدمين من مستخدمي الجماعة الجماعية، والتي يتم استخدامها بعد ذلك لجمع مجموعة بيانات كبيرة الحجم تمتد أكثر من 600 كيلو بايت تصنيفات جودة التسمية التوضيحية. ثم صرفها بعناية جودة التصنيفات التي تم جمعها وإنشاء نماذج أساسية لهذه المهمة الجديدة في QE. أخيرا، سنقوم بزيادة جمع التعليقات التوضيحية ذات جودة التسمية التوضيحية الجميلة من الدراسات المدربة، واستخدامها لإظهار أن نماذج QE التي تم تدريبها على التصنيفات الخشنة يمكن أن تكتشف بشكل فعال وتصفية التسميات التوضيحية ذات الجودة المنخفضة الجودة، وبالتالي تحسين تجربة المستخدم من أنظمة التسمية التوضيحية.
Automatic image captioning has improved significantly over the last few years, but the problem is far from being solved, with state of the art models still often producing low quality captions when used in the wild. In this paper, we focus on the task of Quality Estimation (QE) for image captions, which attempts to model the caption quality from a human perspective and *without* access to ground-truth references, so that it can be applied at prediction time to detect low-quality captions produced on *previously unseen images*. For this task, we develop a human evaluation process that collects coarse-grained caption annotations from crowdsourced users, which is then used to collect a large scale dataset spanning more than 600k caption quality ratings. We then carefully validate the quality of the collected ratings and establish baseline models for this new QE task. Finally, we further collect fine-grained caption quality annotations from trained raters, and use them to demonstrate that QE models trained over the coarse ratings can effectively detect and filter out low-quality image captions, thereby improving the user experience from captioning systems.
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