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Register a new userBroaden the Vision: Geo-Diverse Visual Commonsense Reasoning
توسيع الرؤية: منطق العمولة البصرية المتنوعة
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يتم تعريف المنزول على أنه المعرفة التي يوافق عليها الجميع. ومع ذلك، فإن أنواع معينة من المعرفة المنطقية مرتبطة بالثقافة والمواقع الجغرافية ويتم تقاسمها فقط محليا. على سبيل المثال، تختلف مشاهد مراسم الزفاف عبر المناطق الناجمة عن الجمارك المختلفة التي تتأثر بالعوامل التاريخية والدينية. ومع ذلك، حذفت هذه الخصائص الإقليمية عموما في العمل السابق. في هذه الورقة، نقوم بإنشاء مجموعة بيانات منطق مرئية للبصرية (GD-VCR) لاختبار قدرة النماذج في الرؤية واللغة على فهم المنطقية الثقافية والرائعة. على وجه الخصوص، نقوم بدراسة نماذج للرؤية واللغدية التي من بين الفنون، و Visualbert و Vilbert تدربت على VCR، وهو معيار قياسي مع الصور في المقام الأول من المناطق الغربية. بعد ذلك تقييم مدى جودة أن تعميم النماذج المدربة للإجابة على الأسئلة في GD-VCR. نجد أن أداء كلا النماذج للمناطق غير الغربية بما في ذلك شرق آسيا وجنوب آسيا وأفريقيا أقل بكثير من تلك الخاصة بالمنطقة الغربية. نقوم بتحليل الأسباب الكامنة وراء تباين الأداء وتجد أن فجوة الأداء أكبر على أزواج ضمنيا: 1) تشعر بالقلق من السيناريوهات المتعلقة بالثقافة، على سبيل المثال، حفلات الزفاف، الأنشطة الدينية، والمهرجانات؛ 2) تتطلب منطق المنطقي الجغرافي الرفيع المستوى بدلا من التصور والاعتراف بالترتيب المنخفض. يتم إصدار DataSet و Code في https://github.com/wadeyin9712/gd-vcr.
Commonsense is defined as the knowledge on which everyone agrees. However, certain types of commonsense knowledge are correlated with culture and geographic locations and they are only shared locally. For example, the scenes of wedding ceremonies vary across regions due to different customs influenced by historical and religious factors. Such regional characteristics, however, are generally omitted in prior work. In this paper, we construct a Geo-Diverse Visual Commonsense Reasoning dataset (GD-VCR) to test vision-and-language models' ability to understand cultural and geo-location-specific commonsense. In particular, we study two state-of-the-art Vision-and-Language models, VisualBERT and ViLBERT trained on VCR, a standard benchmark with images primarily from Western regions. We then evaluate how well the trained models can generalize to answering the questions in GD-VCR. We find that the performance of both models for non-Western regions including East Asia, South Asia, and Africa is significantly lower than that for Western region. We analyze the reasons behind the performance disparity and find that the performance gap is larger on QA pairs that: 1) are concerned with culture-related scenarios, e.g., weddings, religious activities, and festivals; 2) require high-level geo-diverse commonsense reasoning rather than low-order perception and recognition. Dataset and code are released at https://github.com/WadeYin9712/GD-VCR.
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