Video Altyazılama için Önceden Eğitilmiş 3B-CNN'lerden Yararlanma

Öz

Video altyazılama, hem dilbilgisel hem de anlamsal olarak doğru açıklamalar oluşturmayı amaçlayan bir görsel anlama görevidir. Video altyazılamadaki ana zorluklardan biri, videolardaki karmaşık dinamikleri yakalamaktır. Bu çalışma bu zorluğu aşmak için önceden eğitilmiş 3B Evrişimli Sinir Ağlarını (3D-CNNs) kullanmaktadır. Bu ağlar bu tür dinamikleri modellemede özellikle etkilidir, böylece videoların bağlamsal anlayışını artırır. Önerilen yaklaşım, video altyazılama için yaygın olarak tanınan bir ölçüt olan Microsoft Araştırma Video Açıklama (MSVD) veri seti üzerinde değerlendirildi. Performansı değerlendirmek için BLEU-1’den BLEU-4’e, CIDEr, ROUGE-L, METEOR ve SPICE de dahil olmak üzere standart metrikler kullandık. Sonuçlar, tüm bu metriklerde önemli iyileşmeler göstererek, önceden eğitilmiş 3D-CNN’lerin video altyazılama doğruluğunu artırdığını vurgulamaktadır.

Anahtar Kelimeler

• Video Altyazılama
• Video-Dil Multimodal Öğrenme
• Hareket Nitelikleri.

Leveraging Pre-trained 3D-CNNs for Video Captioning

Abstract

Video captioning is a visual understanding task that aims to generate grammatically and semantically accurate descriptions. One of the main challenges in video captioning is capturing the complex dynamics present in videos. This study addresses this challenge by leveraging pre-trained 3D Convolutional Neural Networks (3D-CNNs). These networks are particularly effective at modeling such dynamics, enhancing video contextual understanding. We evaluated the approach on the Microsoft Research Video Description (MSVD) dataset, with commonly utilized performance metrics in video captioning including CIDEr, BLEU-1 through BLEU-4, ROUGE-L, METEOR, and SPICE. The results show significant improvements across all these metrics, proving the advantage of pre-trained 3D-CNNs in enhancing video captioning accuracy

Keywords

• Video Captioning
• Video-Language Multimodal Learning
• Motion Features.

Kaynakça

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