Image captioning with audio has emerged as a challenging yet promising task in the field of deep learning. This paper proposes a novel approach to address this task by integrating convolutional neural networks (CNNs) for image feature extraction and recurrent neural networks (RNNs) for sequential audio analysis. Specifically, we leverage pre-trained CNNs such as VGG to extract visual features from images and employ spectrogram representations coupled with RNNs such as LSTM or GRU to process audio inputs. Our proposed model based not only on their visual content but also on accompanying audio cues. We evaluate the performance of our model on benchmark datasets and demonstrate its effectiveness in generating coherent and contextually relevant captions for images with corresponding audio inputs. Additionally, we conduct tablation studies to analyze the contribution of each modality to the overall captioning performance, our results show that the fusion of visual and auditory modalities significantly improves captioning quality compared to using either modality in isolation.
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kannaiyan KanchanaMs K.Kanchana is working as an Assistant Professor in Computer Science and Engineering Department at Kathir College of Engineering. She is interested in the area of Machine Learning and Deep Learning.Image capti. N° de réf. du vendeur 1702233380
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Image captioning with audio has emerged as a challenging yet promising task in the field of deep learning. This paper proposes a novel approach to address this task by integrating convolutional neural networks (CNNs) for image feature extraction and recurrent neural networks (RNNs) for sequential audio analysis. Specifically, we leverage pre-trained CNNs such as VGG to extract visual features from images and employ spectrogram representations coupled with RNNs such as LSTM or GRU to process audio inputs. Our proposed model based not only on their visual content but also on accompanying audio cues. We evaluate the performance of our model on benchmark datasets and demonstrate its effectiveness in generating coherent and contextually relevant captions for images with corresponding audio inputs. Additionally, we conduct tablation studies to analyze the contribution of each modality to the overall captioning performance, our results show that the fusion of visual and auditory modalities significantly improves captioning quality compared to using either modality in isolation. N° de réf. du vendeur 9786207647606
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Image captioning with audio has emerged as a challenging yet promising task in the field of deep learning. This paper proposes a novel approach to address this task by integrating convolutional neural networks (CNNs) for image feature extraction and recurrent neural networks (RNNs) for sequential audio analysis. Specifically, we leverage pre-trained CNNs such as VGG to extract visual features from images and employ spectrogram representations coupled with RNNs such as LSTM or GRU to process audio inputs. Our proposed model based not only on their visual content but also on accompanying audio cues. We evaluate the performance of our model on benchmark datasets and demonstrate its effectiveness in generating coherent and contextually relevant captions for images with corresponding audio inputs. Additionally, we conduct tablation studies to analyze the contribution of each modality to the overall captioning performance, our results show that the fusion of visual and auditory modalities significantly improves captioning quality compared to using either modality in isolation.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch. N° de réf. du vendeur 9786207647606
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Taschenbuch. Etat : Neu. Image Caption | Image Caption using Deep learning | Kanchana Kannaiyan (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2024 | LAP LAMBERT Academic Publishing | EAN 9786207647606 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. N° de réf. du vendeur 129381058
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