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تعلم تحديد العلاقات ذات الصلة بالسؤال للحصول على سؤال مرئي
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تستخدم أنظمة الرد على السؤال المرئي الحالي (VQA) بشكل شائع الشبكات العصبية الرسم البيانية (GNNS) لاستخراج العلاقات البصرية مثل العلاقات الدلالية أو العلاقات المكانية. ومع ذلك، فإن الدراسات التي تستخدم GNNS تتجاهل عادة أهمية كل علاقة وتسلسل ببساطة النواتج من ترميز العلاقات المتعددة. في هذه الورقة، نقترح هندسة طبقة جديدة تضرب علاقات مرئية متعددة من خلال آلية الاهتمام لمعالجة هذه المسألة. على وجه التحديد، نقوم بتطوير نموذج يستخدم تضمين السؤال ومضمون مشترك للمشفرين للحصول على أوزان الاهتمام الديناميكي فيما يتعلق بنوع الأسئلة. باستخدام الأوزان الاهتمام بالترفيه، يمكن للنموذج المقترح استخدام ميزات العلاقة المرئية اللازمة لسؤال معين. النتائج التجريبية على DataSet VQA 2.0 توضح أن النموذج المقترح تفوق الفنيات القائمة على الرسم البياني القائمة على شبكة الإنترنت. بالإضافة إلى ذلك، نقوم بتصور وزن الاهتمام وإظهار أن النموذج المقترح يعين وزن أعلى للعلاقات الأكثر صلة بالمسألة.
Previous existing visual question answering (VQA) systems commonly use graph neural networks(GNNs) to extract visual relationships such as semantic relations or spatial relations. However, studies that use GNNs typically ignore the importance of each relation and simply concatenate outputs from multiple relation encoders. In this paper, we propose a novel layer architecture that fuses multiple visual relations through an attention mechanism to address this issue. Specifically, we develop a model that uses question embedding and joint embedding of the encoders to obtain dynamic attention weights with regard to the type of questions. Using the learnable attention weights, the proposed model can efficiently use the necessary visual relation features for a given question. Experimental results on the VQA 2.0 dataset demonstrate that the proposed model outperforms existing graph attention network-based architectures. Additionally, we visualize the attention weight and show that the proposed model assigns a higher weight to relations that are more relevant to the question.
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