Indicative Vision Transformer for end-to-end zero-shot sketch-based image retrieval

Zhang, Haoxiang; Cheng, Deqiang; Kou, Qiqi; Asad, Mujtaba; Jiang, He

doi:10.1016/j.aei.2024.102398

Advanced Engineering Informatics

2024

DOI: 10.1016/j.aei.2024.102398

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Indicative Vision Transformer for end-to-end zero-shot sketch-based image retrieval

Haoxiang Zhang,

Deqiang Cheng,

Qiqi Kou

et al.

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Cited by 4 publications

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Automatic identification of bottlenecks for ambulance passage on urban streets: A deep learning-based approach

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Yan

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Advanced Engineering Informatics

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Automatic identification of bottlenecks for ambulance passage on urban streets: A deep learning-based approach

Pan,

Liu,

Yan

et al. 2024

Advanced Engineering Informatics

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Domain disentanglement and fusion based on hyperbolic neural networks for zero-shot sketch-based image retrieval

Zhang,

et al. 2025

Information Processing & Management

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Single Image Super-Resolution via Wide-Activation Feature Distillation Network

Su,

Wang,

et al. 2024

Sensors

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Feature extraction plays a pivotal role in the context of single image super-resolution. Nonetheless, relying on a single feature extraction method often undermines the full potential of feature representation, hampering the model’s overall performance. To tackle this issue, this study introduces the wide-activation feature distillation network (WFDN), which realizes single image super-resolution through dual-path learning. Initially, a dual-path parallel network structure is employed, utilizing a residual network as the backbone and incorporating global residual connections to enhance feature exploitation and expedite network convergence. Subsequently, a feature distillation block is adopted, characterized by fast training speed and a low parameter count. Simultaneously, a wide-activation mechanism is integrated to further enhance the representational capacity of high-frequency features. Lastly, a gated fusion mechanism is introduced to weight the fusion of feature information extracted from the dual branches. This mechanism enhances reconstruction performance while mitigating information redundancy. Extensive experiments demonstrate that the proposed algorithm achieves stable and superior results compared to the state-of-the-art methods, as evidenced by quantitative evaluation metrics tests conducted on four benchmark datasets. Furthermore, our WFDN excels in reconstructing images with richer detailed textures, more realistic lines, and clearer structures, affirming its exceptional superiority and robustness.

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Indicative Vision Transformer for end-to-end zero-shot sketch-based image retrieval

Cited by 4 publications

References 47 publications

Automatic identification of bottlenecks for ambulance passage on urban streets: A deep learning-based approach

Automatic identification of bottlenecks for ambulance passage on urban streets: A deep learning-based approach

Domain disentanglement and fusion based on hyperbolic neural networks for zero-shot sketch-based image retrieval

Single Image Super-Resolution via Wide-Activation Feature Distillation Network

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