On the limitations of visual-semantic embedding networks for image-to-text information retrieval
Authors: Gong, Y., Cosma, G. and Fang, H.
Journal: Journal of Imaging
Volume: 7
Issue: 8
eISSN: 2313-433X
DOI: 10.3390/jimaging7080125
Abstract:Visual-semantic embedding (VSE) networks create joint image–text representations to map images and texts in a shared embedding space to enable various information retrieval-related tasks, such as image–text retrieval, image captioning, and visual question answering. The most recent state-of-the-art VSE-based networks are: VSE++, SCAN, VSRN, and UNITER. This study evaluates the performance of those VSE networks for the task of image-to-text retrieval and identifies and analyses their strengths and limitations to guide future research on the topic. The experimental results on Flickr30K revealed that the pre-trained network, UNITER, achieved 61.5% on average Recall@5 for the task of retrieving all relevant descriptions. The traditional networks, VSRN, SCAN, and VSE++, achieved 50.3%, 47.1%, and 29.4% on average Recall@5, respectively, for the same task. An additional analysis was performed on image–text pairs from the top 25 worst-performing classes using a subset of the Flickr30K-based dataset to identify the limitations of the performance of the best-performing models, VSRN and UNITER. These limitations are discussed from the perspective of image scenes, image objects, image semantics, and basic functions of neural networks. This paper discusses the strengths and limitations of VSE networks to guide further research into the topic of using VSE networks for cross-modal information retrieval tasks.
Source: Scopus
On the Limitations of Visual-Semantic Embedding Networks for Image-to-Text Information Retrieval.
Authors: Gong, Y., Cosma, G. and Fang, H.
Journal: J Imaging
Volume: 7
Issue: 8
eISSN: 2313-433X
DOI: 10.3390/jimaging7080125
Abstract:Visual-semantic embedding (VSE) networks create joint image-text representations to map images and texts in a shared embedding space to enable various information retrieval-related tasks, such as image-text retrieval, image captioning, and visual question answering. The most recent state-of-the-art VSE-based networks are: VSE++, SCAN, VSRN, and UNITER. This study evaluates the performance of those VSE networks for the task of image-to-text retrieval and identifies and analyses their strengths and limitations to guide future research on the topic. The experimental results on Flickr30K revealed that the pre-trained network, UNITER, achieved 61.5% on average Recall@5 for the task of retrieving all relevant descriptions. The traditional networks, VSRN, SCAN, and VSE++, achieved 50.3%, 47.1%, and 29.4% on average Recall@5, respectively, for the same task. An additional analysis was performed on image-text pairs from the top 25 worst-performing classes using a subset of the Flickr30K-based dataset to identify the limitations of the performance of the best-performing models, VSRN and UNITER. These limitations are discussed from the perspective of image scenes, image objects, image semantics, and basic functions of neural networks. This paper discusses the strengths and limitations of VSE networks to guide further research into the topic of using VSE networks for cross-modal information retrieval tasks.
Source: PubMed
On the Limitations of Visual-Semantic Embedding Networks for Image-to-Text Information Retrieval
Authors: Gong, Y., Cosma, G. and Fang, H.
Journal: JOURNAL OF IMAGING
Volume: 7
Issue: 8
eISSN: 2313-433X
DOI: 10.3390/jimaging7080125
Source: Web of Science (Lite)
On the limitations of visual-semantic embedding networks for image-to-text information retrieval
Authors: Gong, Y., Cosma, G. and Fang, H.
Journal: Journal of Imaging
Source: Manual
On the Limitations of Visual-Semantic Embedding Networks for Image-to-Text Information Retrieval.
Authors: Gong, Y., Cosma, G. and Fang, H.
Journal: Journal of imaging
Volume: 7
Issue: 8
Pages: 125
eISSN: 2313-433X
ISSN: 2313-433X
DOI: 10.3390/jimaging7080125
Abstract:Visual-semantic embedding (VSE) networks create joint image-text representations to map images and texts in a shared embedding space to enable various information retrieval-related tasks, such as image-text retrieval, image captioning, and visual question answering. The most recent state-of-the-art VSE-based networks are: VSE++, SCAN, VSRN, and UNITER. This study evaluates the performance of those VSE networks for the task of image-to-text retrieval and identifies and analyses their strengths and limitations to guide future research on the topic. The experimental results on Flickr30K revealed that the pre-trained network, UNITER, achieved 61.5% on average Recall@5 for the task of retrieving all relevant descriptions. The traditional networks, VSRN, SCAN, and VSE++, achieved 50.3%, 47.1%, and 29.4% on average Recall@5, respectively, for the same task. An additional analysis was performed on image-text pairs from the top 25 worst-performing classes using a subset of the Flickr30K-based dataset to identify the limitations of the performance of the best-performing models, VSRN and UNITER. These limitations are discussed from the perspective of image scenes, image objects, image semantics, and basic functions of neural networks. This paper discusses the strengths and limitations of VSE networks to guide further research into the topic of using VSE networks for cross-modal information retrieval tasks.
Source: Europe PubMed Central