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Register a new userMultimodal Weighted Fusion of Transformers for Movie Genre Classification
التوتر متعدد الوسائط من محولات المحولات لتصنيف النوع الفيلم
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أظهر المحول متعدد الوسائط نموذجا تنافسي للمهام متعددة الوسائط التي تنطوي على إشارات نصية ومرئية وصوتية.ومع ذلك، نظرا لأن المزيد من الطرائق متورطة، يبدأ الاندماج المتأخر عن طريق التسلسل في الحصول على تأثير سلبي على أداء النموذج.علاوة على ذلك، تصبح تنبؤات نموذج الترجمة الشفوية صعبة، لأن المرء يجب أن ينظر إلى مصفوفات تنشيط الاهتمام المختلفة.من أجل التغلب على أوجه القصور هذه، نقترح أداء الانصهار المتأخر عن طريق إضافة وحدة نمطية GMU، والتي تتيح بشكل فعال النموذج من طرائق الوزن على مستوى مثيل، مما يحسن أدائه مع توفير آلية تفسيرية أفضل.في التجارب، نقوم بمقارنة نموذجنا المقترح (Mult-Gmu) مقابل التنفيذ الأصلي (Mult-Concat) ونموذج SOTA تم اختباره في مجموعة بيانات تصنيف أنواع الأفلام.نهجنا، Mult-Gmu، تتفوق على حد سواء، Mult-Concat ونموذج Sota السابق.
The Multimodal Transformer showed to be a competitive model for multimodal tasks involving textual, visual and audio signals. However, as more modalities are involved, its late fusion by concatenation starts to have a negative impact on the model's performance. Besides, interpreting model's predictions becomes difficult, as one would have to look at the different attention activation matrices. In order to overcome these shortcomings, we propose to perform late fusion by adding a GMU module, which effectively allows the model to weight modalities at instance level, improving its performance while providing a better interpretabilty mechanism. In the experiments, we compare our proposed model (MulT-GMU) against the original implementation (MulT-Concat) and a SOTA model tested in a movie genre classification dataset. Our approach, MulT-GMU, outperforms both, MulT-Concat and previous SOTA model.
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