Bengali Image Captioning with Visual Attention

Ami, Amit Saha; Humaira, Mayeesha; Jim, Abidur Rahman Khan; Paul, Shimul; Shah, Faisal Muhammad

doi:10.1109/iccit51783.2020.9392709

Cited by 8 publications

(8 citation statements)

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“…Since image captioning is a mixture of two fields computer vision and NLP equal importance must be given to both fields to get better results. In Table 3, we illustrated the corpus BLEU scores which were not done by [7].…”

Section: Results Analysismentioning

confidence: 99%

“…On the other hand, Deb et al Fig. 2 Illustration of how our model learns words from input image to generate caption using visual attention-based approach [7]. First of all, image features were extracted using CNN.…”

Section: Image Captioning In Bengalimentioning

confidence: 99%

“…They also utilized beam search and greedy search to compute the BLEU scores. Additionally, Ami et al [7] employed visual attention with the encoder-decoder approach to caption images in Bengali. They added attention weights to image features and passed them to the GRU with word vectors to generate captions.…”

Section: Image Captioning In Bengalimentioning

confidence: 99%

“…We utilized the transformer model and the attention-based model proposed by [7] to caption images in Bengali. The transformer model does not process sequence in order but the attention-based model processes sequence in order.…”

Section: Model Architecturementioning

confidence: 99%

“…1. Furthermore, we compare the performance with the visual attention-based approach to caption images in Bengali that was proposed by Ami et al [7]. This visual attention-based approach is shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

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Bornon: Bengali Image Captioning with Transformer-Based Deep Learning Approach

et al. 2021

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Image captioning using encoder-decoder-based approach where CNN is used as the Encoder and sequence generator like RNN as Decoder has proven to be very effective. However, this method has a drawback, that is, sequence needs to be processed in order. To overcome this drawback, some researchers have utilized the transformer model to generate captions from images using English datasets. However, none of them generated captions in Bengali using the transformer model. As a result, we utilized three different Bengali datasets to generate Bengali captions from images using the transformer model. Additionally, we compared the performance of the transformer-based model with a visual attention-based encoder-decoder approach. Finally, we compared the result of the transformer-based model with other models that employed different Bengali image captioning datasets.

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Section: Results Analysismentioning

confidence: 99%

Section: Image Captioning In Bengalimentioning

confidence: 99%

Section: Image Captioning In Bengalimentioning

confidence: 99%

Section: Model Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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