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Register a new userProbing Cross-Modal Representations in Multi-Step Relational Reasoning
التحقيق في تمثيلات متعددة الوسائط في التفكير العلائقي متعدد الخطوات
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نحن نبحث في التمثيلات التي تعلمناها عن طريق الرؤية ونماذج اللغة في المهام التي تتطلب التفكير العلائقي.مع التركيز على مشكلة تقييم الحجم النسبي للكائنات في السياقات البصرية مجردة، نحلل منطق واحد وخطوتين.بالنسبة لهذا الأخير، نبني مجموعة بيانات جديدة من مشاهد ثلاثية وتحدد مهمة تتطلب منطق على مستوى الصور الفردية وعبر الصور في مشهد.نحن نبذل تمثيلات النموذج المستفادة باستخدام مصنفات التشخيص.تظهر تجاربنا أن الهندسة المعاد المسبدة مسبقا القائمة على المحولات يمكن أن تؤدي من التفكير العلائقي المستوى الأعلى، وهي قادرة على تعلم تمثيلات المهام والبيانات الجديدة التي تختلف عن ما شوهد في الاحتجاج.
We investigate the representations learned by vision and language models in tasks that require relational reasoning. Focusing on the problem of assessing the relative size of objects in abstract visual contexts, we analyse both one-step and two-step reasoning. For the latter, we construct a new dataset of three-image scenes and define a task that requires reasoning at the level of the individual images and across images in a scene. We probe the learned model representations using diagnostic classifiers. Our experiments show that pretrained multimodal transformer-based architectures can perform higher-level relational reasoning, and are able to learn representations for novel tasks and data that are very different from what was seen in pretraining.
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