Resnet Tabanlı Derin Geçitli Tekrarlayan Birim ile Akıllı Telefonda Görüntü Altyazılama

Öz

Görüntü altyazılama, görsel içerikler için dilbilgisel ve anlamsal olarak uygun doğal dil cümleleri oluşturmayı amaçlamaktadır. Geçitli tekrarlayan birim (GRU) tabanlı yaklaşımlar, son zamanlarda altyazı oluşturmadaki performanslarından dolayı büyük ilgi görmektedir. Kaybolan gradyan problemi ve derin ağlardaki ilgili bilgi akışının modülasyonunu sağlanması GRU'daki başlıca zorluklardır. Bu çalışmada, ilgili bilgilerin çoklu GRU katmanları kullanılarak aktarılmasını sağlamak, ve kaybolan gradyan sorununun üstesinden gelmek için resnet tabanlı bir derin GRU yaklaşımı önerilmektedir. Derin GRU'nun ardışık katmanları arasında artık bağlantılar kullanılmasıyla alt katmanlardan üst katmanlara doğru gradyan akışının iyileştirilmesi sağlanmıştır. Yaygın olarak kullanılan MSCOCO veri seti üzerindeki deneysel araştırmalar, önerilen yaklaşımın son yaklaşımlarla karşılaştırılabilir performans sağladığını göstermiştir. Ayrıca bu yaklaşım, internet bağlantısı olmaksızın altyazı oluşturma olanağı sunan ve sesle kontrol edilebilen bir arayüzü olan kendi tasarladığımız Android uygulamamıza CaptionEye gömülmüştür.

Anahtar Kelimeler

• Kapılı Tekrarlayan Birim
• Artık Bağlantı
• Görüntü Altyazılama
• Android Uygulama.

Resnet based Deep Gated Recurrent Unit for Image Captioning on Smartphone

Abstract

Image captioning aims at generating grammatically and semantically acceptable natural language sentences for visual contents. Gated recurrent units (GRU) based approaches have recently attracted much attention due to their performance in caption generation. Challenges with GRU are to deal with vanishing gradient problems and modulation of the most relevant information flow in deep networks. In this paper, we propose a resnet-based deep GRU approach to overcome the vanishing gradient problem with residual connections while the most relevant information is ensured to flow using multiple layers of GRU. Residual connections are employed between consecutive layers of deep GRU, which improves the gradient flow from lower to upper layers. Experimental investigations on the publicly available MSCOCO dataset prove that the proposed approach achieves comparable performance with some state-of-the-art approaches. Moreover, the approach is embedded into our custom-designed Android application, CaptionEye, which offers the ability to generate captions without an internet connection under a voice user interface.

Keywords

• Gated Recurrent Unit
• Residual Connection
• Image Captioning
• Android Application.

Kaynakça

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