LINEA用新注意力机制实现高速高精度直线检测。
LINEA: Fast and Accurate Line Detection Using Scalable Transformers
- 引入可变形直线注意力机制，无需在大数据集预训练
- 推理速度显著提升，跨分布测试中准确率更高
- 适合实时视频分析等低延迟场景使用
直线检测是数字图像处理中的基础操作，常用于高层视觉任务。近年来基于Transformer的方法在直线检测上优于传统CNN方法，但推理速度大幅下降，难以满足低延迟视频分析需求。现有Transformer模型还需在大规模数据集（如COCO或Object360）上预训练注意力机制。本文提出新型Transformer方法LINEA，采用可变形直线注意力（DLA）机制，避免了对大型数据集的注意力预训练。大量实验表明，LINEA在保持高精度的同时，显著提升推理速度，在分布外数据测试中，sAP指标优于先前模型。
原文摘要 · Abstract (English)
Line detection is a basic digital image processing operation used by higher-level processing methods. Recently, transformer-based methods for line detection have proven to be more accurate than methods based on CNNs, at the expense of significantly lower inference speeds. As a result, video analysis methods that require low latencies cannot benefit from current transformer-based methods for line detection. In addition, current transformer-based models require pretraining attention mechanisms on large datasets (e.g., COCO or Object360). This paper develops a new transformer-based method that is significantly faster without requiring pretraining the attention mechanism on large datasets. We eliminate the need to pre-train the attention mechanism using a new mechanism, Deformable Line Attention (DLA). We use the term LINEA to refer to our new transformer-based method based on DLA. Extensive experiments show that LINEA is significantly faster and outperforms previous models on sAP in out-of-distribution dataset testing.
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