Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast Convergence of DETR with Spatially Modulated Co-Attention   The recently proposed Detection Transformer (DETR) model successfully applies
Transformer to objects detection and achieves comparable performance with
two-stage object detection frameworks, such as Faster-RCNN. However, DETR
suffers from its slow convergence. Training DETR from scratch needs 500 epochs
to achieve a high accuracy. To accelerate its convergence, we propose a simple
yet effective scheme for improving the DETR framework, namely Spatially
Modulated Co-Attention (SMCA) mechanism. The core idea of SMCA is to conduct
location-aware co-attention in DETR by constraining co-attention responses to
be high near initially estimated bounding box locations. Our proposed SMCA
increases DETR's convergence speed by replacing the original co-attention
mechanism in the decoder while keeping other operations in DETR unchanged.
Furthermore, by integrating multi-head and scale-selection attention designs
into SMCA, our fully-fledged SMCA can achieve better performance compared to
DETR with a dilated convolution-based backbone (45.6 mAP at 108 epochs vs. 43.3
mAP at 500 epochs). We perform extensive ablation studies on COCO dataset to
validate SMCA. Code is released at https://github.com/gaopengcuhk/SMCA-DETR .

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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-th
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The categories of this paper are: adap-org
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The categories of this paper are: bayes-an
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The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: eess
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The categories of this paper are: dg-ga
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The categories of this paper are: math
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-alg
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The categories of this paper are: chem-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: physics
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The categories of this paper are: alg-geom