Summary of Lda-aqu: Adaptive Query-guided Upsampling Via Local Deformable Attention, by Zewen Du et al.
LDA-AQU: Adaptive Query-guided Upsampling via Local Deformable Attention
by Zewen Du, Zhenjiang Hu, Guiyu Zhao, Ying Jin, Hongbin Ma
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty Summary: Feature upsampling is a crucial operation in deep convolutional neural networks. Existing upsamplers either lack specific feature guidance or require high-resolution feature maps, hindering performance and flexibility. This paper introduces local self-attention into the upsampling task, demonstrating that most existing upsamplers can be viewed as special cases of local self-attention-based upsamplers. The proposed LDA-AQU is a novel dynamic kernel-based upsampler utilizing query features to guide the model in adaptively adjusting neighboring points’ positions and aggregation weights. LDA-AQU is lightweight and easily integrates into various architectures, outperforming previous state-of-the-art upsamplers in four dense prediction tasks: object detection, instance segmentation, panoptic segmentation, and semantic segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This paper is about making images clearer by combining small pieces of information. Current methods don’t work well because they either lack important details or require too much information. The researchers came up with a new way to combine these small pieces using something called local self-attention. They tested this method and found that it works better than previous methods in four different tasks: detecting objects, identifying instances, segmenting images, and understanding what’s happening in an image. |
Keywords
» Artificial intelligence » Instance segmentation » Object detection » Self attention » Semantic segmentation
