Strengthening Dynamic Convolution With Attention and Residual Connection in Kernel Space
In this paper, we propose Dynamic Residual Convolution (DRConv), an efficient method for computing input-specific local features while addressing the limitations of dynamic convolution. DRConv utilizes global salient features calculated using efficient token attention, strengthening representation p...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10409154/ |