Image Caption Generation Using Contextual Information Fusion With Bi-LSTM-s

Zhang, Huawei; Ma, Chengbo; Jiang, Zhanjun; Lian, Jing

doi:10.1109/access.2022.3232508

Cited by 20 publications

(5 citation statements)

References 44 publications

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“…Recognizing aerial images is an indispensable application in deep neural networks [37,38,39,40,41,. We proposed a novel LR aerial photo categorization pipeline, wherein deep perceptual features are extracted and refined by propagating the prior knowledge of HR aerial photos into LR ones.…”

Section: Discussionmentioning

confidence: 99%

LR Aerial Photo Categorization by Semi-Supervised Perceptual Feature Selection

Li,

Sheng

2023

IEEE Access

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Recognizing the semantic categories of low-resolution (LR) aerial photos is an indispensable technique in geoscience and remote sensing. However, it is also a challenging task in practice. In this work, a semi-supervised perceptual feature selection (SPFS) pipeline is proposed for LR aerial photo categorization, focusing on selecting high quality perception-guided visual features. Specifically, by mimicking human vision system, a novel low-rank model is designed to decompose each LR aerial photo into multiple visually or semantically salient foreground regions coupled with the background non-salient regions. This model can 1) produce the a gaze shifting path (GSP) simulating human gaze behavior, and 2) generate hierarchical deep representation for a GSP. Afterward, a semi-supervised feature selection (FS) is leveraged toward a succinct set of discriminative deep GSP features, wherein only labels of LR aerial photos are required. Based on the selected features, a classifier is trained for visual categorization. Comprehensive experimental results have validated our method's advantage.

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Section: Discussionmentioning

confidence: 99%

LR Aerial Photo Categorization by Semi-Supervised Perceptual Feature Selection

Li,

Sheng

2023

IEEE Access

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“…Recognizing aerial images is an indispensable application in remote sensing [21][22][23][24][25]. We proposed a novel crossresolution-enhanced high-resolution aerial photo categorization pipeline, wherein deep perceptual features are extracted and refined by propagating the prior knowledge of low-resolution aerial photos into high-resolution ones.…”

Section: Discussionmentioning

confidence: 99%

High-Resolution Aerial Photo Categorization Model by Cross-Resolution Perceptual Experiences Transfer

Li,

Liu

2023

IEEE Access

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There are thousands of observation satellites orbiting the earth, each of which captures massive-scale photographs covering millions of square kilometers everyday. In practice, these aerial photos are with high-resolution and usually contain tens to hundreds of ground objects (e.g., vehicles and rooftops). Understanding the categories of a rich variety of high-resolution aerial photos is an indispensable technique for many applications, such as intelligent transportation, natural disaster prediction, and smart agriculture. In this work, we propose a cross-resolution perceptual experiences transfer framework for categorizing highresolution aerial photos, focusing on leveraging the perceptual features from low-resolution aerial photos to enhance the feature selection of high-resolution ones. More specifically, we first construct gaze shifting path to mimic human visual perception to both low-resolution and high-resolution aerial photos, wherein the corresponding deep gaze shifting path features are engineered. Afterward, a kernel-induced feature selection algorithm is formulated to obtain a succinct set of deep gaze shifting path features discriminative across lowand high-resolution aerial photos. Based on the selected features, low-and high-resolution aerial photos' labels are collaboratively utilized to train a linear classifier for categorizing high-resolution ones. Extensive comparative studies have validated the superiority of our method.

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“…Traditional LSTM models, however, are only capable of capturing sequential information in one direction. In music, the selection of each musical note depends not only on preceding musical segments but also exhibits significant associations with subsequent musical segments [10] . Consequently, relying solely on unidirectional LSTM proves challenging in generating high-quality music compositions.…”

Section: Bi-lstmmentioning

confidence: 99%

A music generation model based on Bi-LSTM

bai

2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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The unidirectional LSTM based music generation model does not take into account the influence of future information when generating music. It solely focuses on learning the dependencies of the current moment on past information, resulting in music with poor stability and subpar quality. To address this issue, we have developed a music generation model based on bidirectional LSTM. During the training phase, this model effectively captures musical information from both past and future time steps, resulting in a probability distribution of musical elements that closely approximates real-world music. This, in turn, leads to enhanced structural stability and improved music quality in the generated compositions. Finally, we conducted validation experiments on our proposed approach, and the results unequivocally demonstrate its effectiveness.

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Image Caption Generation Using Contextual Information Fusion With Bi-LSTM-s

Cited by 20 publications

References 44 publications

LR Aerial Photo Categorization by Semi-Supervised Perceptual Feature Selection

LR Aerial Photo Categorization by Semi-Supervised Perceptual Feature Selection

High-Resolution Aerial Photo Categorization Model by Cross-Resolution Perceptual Experiences Transfer

A music generation model based on Bi-LSTM

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