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Register a new userCrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization
Crossvqa: توليد المعايير بشكل متقن لاختبار نظام VQA بشكل منهجي
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تحدي واحد في تقييم نماذج الإجابة عن السؤال المرئي (VQA) في إعداد التكيف عبر DataSet هو أن التحولات التوزيع متعددة الوسائط متعددة، مما يجعل من الصعب تحديد ما إذا كانت التحولات في ميزات مرئية أو لغة تلعب دورا رئيسيا. في هذه الورقة، نقترح إطارا شبه أوتوماتيكي لإنشاء تحولات محددة من خلال إدخال وحدة نمطية لجيل الإجابات مرئية يمكن السيطرة عليها (VQAG) قادرة على توليد أزواج للإجابة على الأسئلة ذات الصلة والتنوع مع نمط البيانات المطلوب. نستخدمها لإنشاء crossvqa، وهي مجموعة من تقسيم الاختبار لتقييم مجموعات بيانات VQA2، VizWiz، وفتح الصور المفتوحة. نحن نقدم تحليلا لمجموعات البيانات التي تم إنشاؤها وإظهار فائدتها باستخدامها لتقييم العديد من أنظمة VQA الحديثة. اكتشاف واحد مهم هو أن التحولات المرئية في VQA عبر DataSet يهم أكثر من التحولات اللغوية. على نطاق أوسع، نقدم إطارا قابل للتطوير لتقييم الجهاز بشكل منهجي مع التدخل البشري القليل.
One challenge in evaluating visual question answering (VQA) models in the cross-dataset adaptation setting is that the distribution shifts are multi-modal, making it difficult to identify if it is the shifts in visual or language features that play a key role. In this paper, we propose a semi-automatic framework for generating disentangled shifts by introducing a controllable visual question-answer generation (VQAG) module that is capable of generating highly-relevant and diverse question-answer pairs with the desired dataset style. We use it to create CrossVQA, a collection of test splits for assessing VQA generalization based on the VQA2, VizWiz, and Open Images datasets. We provide an analysis of our generated datasets and demonstrate its utility by using them to evaluate several state-of-the-art VQA systems. One important finding is that the visual shifts in cross-dataset VQA matter more than the language shifts. More broadly, we present a scalable framework for systematically evaluating the machine with little human intervention.
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