The Social Hashtag Recommendation for Image and Video Using Deep Learning Approach

Panchal, Priyanka; Prajapati, Dinesh J.

doi:10.1007/978-981-19-5443-6_19

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…Although visual concepts generally refer to a collection of objects, they can also refer to more abstract concepts like emotions or attributes like colors. AIA has a wide range of practical applications, including image retrieval [1], medical image recognition [2], and social tag recommendation [3].…”

Section: Introductionmentioning

confidence: 99%

Enhancing high-vocabulary image annotation with a novel attention-based pooling

Salar,

Ahmadi

2023

Preprint

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Given an image, we aim to assign a set of semantic labels to its visual content automatically. This is generally known as automatic image annotation (AIA). Images contain objects that can vary in size and position, with some only taking up a small region of the entire picture. The rise in the number of object classes also heightens this variety. Despite the achievement of promising results, the majority of current methods have limited efficacy in the detection of small-scale objects. To make more effective use of spatial data compared to the global pooling method, we propose a modified transformer decoder layer that improves computational complexity without sacrificing model performance. The study has conducted multiple experiments on four datasets, including three high-vocabulary small-scale datasets (Corel 5k, IAPR TC-12, and Esp Game) and one large-scale dataset (Visual Genome) with a vocabulary list of 500 words. In comparison to existing state-of-the-art models, our approach achieves comparable results in F1-score, \({\text{N}}^{+}\), and mean average precision (mAP) on small- and large-scale datasets.

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Section: Introductionmentioning

confidence: 99%

Enhancing high-vocabulary image annotation with a novel attention-based pooling

Salar,

Ahmadi

2023

Preprint

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“…Furthermore, they can perform customer segmentation to provide tailored services to different groups. In image recommendation [25,26], tag information is associated with images, allowing users to identify their preferred articles, pictures, videos, and other content using tags and facilitating the quick discovery of content of interest. Nowadays, tag recommendation has become increasingly important in various application scenarios.…”

mentioning

confidence: 99%

Deep feature fusion‐based stacked denoising autoencoder for tag recommendation systems

Fei,

Wang,

Liu

et al. 2023

IET Cyber-Syst and Robotics

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With the rapid development of artificial intelligence technology, commercial robots have gradually entered our daily lives. In order to promote product dissemination, shopping guide robots are a new service options of commerce platforms that use tag recommendation systems to identify users' intentions. A large number of applications combine user historical tagging information with the multi‐round dialogue ability of shopping guide robots to help users efficiently search for and retrieve products of interest. Recently, tensor decomposition methods have become a common approach for modelling entity interaction relationships in tag recommendation systems. However, due to the sparsity of data, these methods only consider low‐order information of entities, making it difficult to capture the higher‐order collaborative signals among entities. Recommendation methods by autoencoders can effectively extract abstract feature representations while they only focus on the two‐dimensional relationship between users and items, ignoring the interaction relationship among users, items and tags in real complex recommendation scenarios. The authors focus on modelling the similarity relationship among entities and propose a method called deep feature fusion tag (DFFT) based on the deep feature fusion of stacked denoising autoencoders. This method can extract high‐order information with different embedding dimensions and fuse them in a unified framework. To extract robust feature representations, the authors inject random noise (mask‐out/drop‐out noise) into the tag information corresponding to users and items to generate corrupted input data, and then utilise autoencoders to encode the interaction relationship among entities. To further obtain the interaction relationship with different dimensions, different encoding layers are stacked and combined to produce a better expanded model which can reinforce each other. Finally, a decoding component is used to reconstruct the original input data. According to the experimental results on two common datasets, the proposed DFFT method outperforms other baselines in terms of the F1@N, NDCG@N and Recall@N evaluation metrics.

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Enhancing high-vocabulary image annotation with a novel attention-based pooling

Salar,

Ahmadi

2024

Vis Comput

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The Social Hashtag Recommendation for Image and Video Using Deep Learning Approach

Cited by 4 publications

References 28 publications

Enhancing high-vocabulary image annotation with a novel attention-based pooling

Enhancing high-vocabulary image annotation with a novel attention-based pooling

Deep feature fusion‐based stacked denoising autoencoder for tag recommendation systems

Enhancing high-vocabulary image annotation with a novel attention-based pooling

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