Attention-Net: An Ensemble Sketch Recognition Approach Using Vector Images

Jain, Gaurav; Chopra, Shivang; Chopra, Suransh; Parihar, Anil Singh

doi:10.1109/tcds.2020.3023055

Cited by 13 publications

(3 citation statements)

References 36 publications

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“…constructed a sketch recognition network based on the transformer structure that encodes sketches into feature vectors and uses the stroke sequence of sketches as input to the network, enhancing the network’s ability to learn the stroke sequence of complex sketches. Jain et al 12 . further designed the TransSketchNet in accordance with the transformer structure, which improves the network’s ability to extract more valuable features by entirely using the stroke sequences and the attention mechanism.…”

Section: Related Workmentioning

confidence: 99%

“…On the basis of existing approaches, 6 , 7 , 19 , 23 – 47 we introduce the dual-attention mechanism followed by the CNN backbone, so that the proposed model can fully use the sketch features in the learning processes. Rather than using the transformer structure 10 – 12 or embedding the attention mechanism into certain modules, we adopt the attention mechanism itself directly to keep the lightweight nature of the model.…”

Section: Related Workmentioning

confidence: 99%

“…However, the aforementioned methods rarely fully utilize the extracted sketch features. To address this issue, researchers have incorporated attention mechanisms 9 into networks, such as multi-graph transformer (MGT), 10 SketchFormer, 11 and TransSketchNet 12 . Although these methods focus on the features through attention mechanisms, they all require a complex computational process.…”

Section: Introductionmentioning

confidence: 99%

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Light-SRNet: a lightweight dual-attention feature fusion network for hand-drawn sketch recognition

Hou

Rong

2023

J. Electron. Imag.

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.Free-hand sketches play a vital role in graphically portraying ideas and concepts in image recognition systems. Most recently proposed learning-based sketch recognition methods have achieved marked progress in recognition accuracy, but they rarely optimize the use of the sparsity features of sketch images. Although several attention-based sketch recognition models have been presented, they endure complex computations and large model sizes. To address these challenges, we present a lightweight convolutional neural network called Light-SRNet based on a dual-attention mechanism to improve the accuracy of sketch recognition while retaining its lightweight nature. In the proposed model, we introduced both the spatial and channel attention mechanisms into the feature extraction network to highlight more discriminative feature representations to enhance its powerful sketch recognition ability. We compared the proposed Light-SRNet with its competitors on the TU-Berlin dataset, Sketchy dataset, and QuickDrawExtended dataset. Extensive experimental results show that Light-SRNet achieves a recognition accuracy of 73.14%, which is comparable to other similar sketch recognition techniques, while requiring only about a quarter of the model parameters.

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Section: Related Workmentioning

confidence: 99%