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Register a new userEaSe: A Diagnostic Tool for VQA based on Answer Diversity
سهولة: أداة تشخيصية ل VQA بناء على تنوع الإجابة
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نقترح سهولة، أداة تشخيصية بسيطة للإجابة على السؤال المرئي (VQA) الذي يحدد صعوبة الصورة، عينة السؤال.يعتمد سهولة على نمط الإجابات التي قدمها المعلقون المتعددين على سؤال معين.على وجه الخصوص، تعتبر جوانبين من الإجابات: (1) انتروبيا؛(2) المحتوى الدلالي.أولا، نثبت صحة تشخيصنا لتحديد عينات سهلة / من الصعب لنماذج VQA الحديثة.ثانيا، نعرض أن هذه السهولة يمكن استخدامها بنجاح لتحديد العينات الأكثر إعلانية للتدريب / ضبط الدقيقة.بشكل حاسم، يتم استخدام المعلومات فقط المتوفرة بسهولة في أي مجموعة بيانات VQA لحساب درجاتها.
We propose EASE, a simple diagnostic tool for Visual Question Answering (VQA) which quantifies the difficulty of an image, question sample. EASE is based on the pattern of answers provided by multiple annotators to a given question. In particular, it considers two aspects of the answers: (i) their Entropy; (ii) their Semantic content. First, we prove the validity of our diagnostic to identify samples that are easy/hard for state-of-art VQA models. Second, we show that EASE can be successfully used to select the most-informative samples for training/fine-tuning. Crucially, only information that is readily available in any VQA dataset is used to compute its scores.
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