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Efficient Multi-Purpose Cross-Attention Based Image Alignment Block for Edge Devices | | Bahri Batuhan Bilecen
; Alparslan Fisne
; Mustafa Ayazoglu
; | | Date: |
1 Jun 2022 | | Abstract: | Image alignment, also known as image registration, is a critical block used
in many computer vision problems. One of the key factors in alignment is
efficiency, as inefficient aligners can cause significant overhead to the
overall problem. In the literature, there are some blocks that appear to do the
alignment operation, although most do not focus on efficiency. Therefore, an
image alignment block which can both work in time and/or space and can work on
edge devices would be beneficial for almost all networks dealing with multiple
images. Given its wide usage and importance, we propose an efficient,
cross-attention-based, multi-purpose image alignment block (XABA) suitable to
work within edge devices. Using cross-attention, we exploit the relationships
between features extracted from images. To make cross-attention feasible for
real-time image alignment problems and handle large motions, we provide a
pyramidal block based cross-attention scheme. This also captures local
relationships besides reducing memory requirements and number of operations.
Efficient XABA models achieve real-time requirements of running above 20 FPS
performance on NVIDIA Jetson Xavier with 30W power consumption compared to
other powerful computers. Used as a sub-block in a larger network, XABA also
improves multi-image super-resolution network performance in comparison to
other alignment methods. | | Source: | arXiv, 2206.00291 | | Services: | Forum | Review | PDF | Favorites | |
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