Deep Reinforced Attention Regression for Partial Sketch Based Image Retrieval

Wang, Dingrong; Sapkota, Hitesh; Liu, Xumin; Yu, Qi

doi:10.1109/icdm51629.2021.00078

Cited by 4 publications

(5 citation statements)

References 19 publications

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“…[10], [12], [18], [26], [27], [28], [48], [51], [66], [67], [68], [69], [70], [71], [72], [73] RNN+CNN [30], [31], [32], [74], [75] [41] used sketches and natural images to co-train CNNs, prior to which a specific image scaling method and a multi-angle voting scheme were designed for image data to be used together for SBIR. Bui, et al [18] proposed a triplet ranked CNN for SBIR to learn embeddings between sketches and images with significantly improved performance.…”

Section: Ann [65] Cnnmentioning

confidence: 99%

“…Through deep binary hash retrieval, the system effectively navigates extensive databases and promptly provides target guidance sketches for stroke guidance stages in real-time. Wang, et al [74] designed a framework that employs deep reinforcement attentional regression to support on-the-fly image-based retrieval. A hybrid loss function was developed which will be used to train RL agents for dynamic ranking rewards and for supervised learning for updating RNNs.…”

Section: ) Approaches Used In Fine-grained Sbirmentioning

confidence: 99%

“…Although scientific research methods and reasonable retrieval strategies are used whenever possible, it is still possible to miss some valuable research. [18], [40], [42], [44], [47], [48], [49], [59], [62], [63], [64], [67], [72], [93], [102], [ [62], [63], [64] Sketchy [26] 75471sketches, 12500 photos 125 Public [26], [29], [38], [43], [44], [45], [47], [48], [49], [50], [51], [52], [55], [67], [71], [73], [74], [80], [86], [87], [92], [94], [99], [101], [104], [107], [108], [109], [110],…”

Section: ) Approaches Used In Zero-shot Sbirmentioning

confidence: 99%

“…[87] QMUL-ShoeV2 [80] 2,000 image-sketch pairs 1 Public [30], [71], [74], [78], [82], [87], [90], [91], [94] QMUL Chair [10] 297 sketches, 297 photos 1 Public [10], [71], [79], [81], [83], [87] QMUL-ChairV2 [10] 400 image-sketch pairs 1 Public [30], [74], [78], [82], [87], [90], [91] QMUL Handbag [83] 568 sketches, 568 photos 1 Public [73], [80], [81], [83…”

Section: ) Approaches Used In Zero-shot Sbirmentioning

confidence: 99%

“…[9], [12], [17], [18], [28], [29], [30], [31], [32], [38], [40], [41], [42], [44], [47], [48], [49], [50], [51], [55], [56], [60], [63], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [79], [81], [82], [83], [84], [87], [88], [89], [90], [93], [94], [95], [96], [97], [99], [100], [101], <...…”

Section: Rq3: What Are the Various Metrics For Evaluating The Perform...unclassified

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A Systematic Literature Review of Deep Learning Approaches for Sketch-Based Image Retrieval: Datasets, Metrics, and Future Directions

Yang,

Ismail,

Pang

et al. 2024

IEEE Access

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Sketch-based image retrieval (SBIR) utilizes sketches to search for images containing similar objects or scenes. Due to the proliferation of touch-screen devices, sketching has become more accessible and therefore has received increasing attention. Deep learning has emerged as a potential tool for SBIR, allowing models to automatically extract image features and learn from large amounts of data. To the best of our knowledge, there is currently no systematic literature review (SLR) of SBIR with deep learning. Therefore, the aim of this review is to incorporate related works into a systematic study, highlighting the main contributions of individual researchers over the years, with a focus on past, present and future trends. To achieve the purpose of this study, 90 studies from 2016 to June 2023 in 4 databases were collected and analyzed using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) framework. The specific models, datasets, evaluation metrics, and applications of deep learning in SBIR are discussed in detail. This study found that Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN) are the most widely used deep learning methods for SBIR. A commonly used dataset is Sketchy, especially in the latest Zero-shot sketch-based image retrieval (ZS-SBIR) task. The results show that Mean Average Precision (mAP) is the most commonly used metric for quantitative evaluation of SBIR. Finally, we provide some future directions and guidance for researchers based on the results of this review.INDEX TERMS Sketch-based image retrieval, SBIR, SLR, PRISMA, deep learning.

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