At present, with the rapid development of Transformer in object detection tasks, the object detection performance has been significantly improved. However, Transformer-based object detectors generally suffer fro
然而,与大多数以前的工作相反,我们远离自回归模型,使用具有并行解码的Transformer,这将在下面描述。 2.2、Transformers和并行解码 Vaswani等人引入了Transformer,作为一种新的基于注意力的机器翻译构建模块。注意机制是从整个输入序列中聚集信息的神经网络层。Transformer引入了自我关注层,类似于非局部神经网络[49],扫描序列的...
, year={2024}, volume={62}, number={}, pages={1-16}, keywords={Encoding;Object detection;Proposals;Detectors;Remote sensing;Current transformers;Position measurement;End-to-end detectors;oriented object detection;positional encoding (PE);remote sensing;transformer}, doi={10.1109/TGRS.2024.3456240}}...
我们提出了一种新的基于transformer和用于直接集合预测的二部匹配损失的目标检测系统DETR(We presented DETR, a new design for object detection systems based on transformers and bipartite matching loss for direct set prediction. )。该方法在具有挑战性的COCO数据集上取得了与优化后的Faster R-CNN基线相当的结果...
由于其set-based的损失,DETR在设计上不需要NMS。为了验证这一点,我们为每个解码器后的输出运行默认参数[50]的标准NMS程序。NMS提高了来自第一个decoder的预测的性能。这可以解释为transformer的单个解码层无法计算输出元素之间的任何互相关关系,因此容易对同一对象进行多个预测。在第二层和后续层中,激活过程中的自注意...
Set-based loss Recurrent detectors 之前都有工作在其他backbone上实现过,但是效果不够好,仍然较为复杂,还用了人工干预,所以归根结底还是Transformer的成功 模型详解 主体方法 下图是DETR的整个工作流程 (1)先CNN抽特征,拉直后送入transformer (2)Encoder学全局特征,大概区分物体块,使其与输出预测框一对一,而不是一...
called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via bipartite matching, and a transformer encoder-decoder architecture. Given a fixed small set of learned object queries, DETR reasons about the relations of the objects and the global image context to...
The evaluation of text detection results on the standard datasets (see Table 4, Table 4), shows that the proposed system performs well in terms of both Recall and F-Measure compared to the state-of the-art, including the transformer-based methods [[33], [34], [35]]. It is interesting...
These approaches use CNN-based detectors that rely on anchor proposals and post-processing stages such as NMS. To tackle these limitations, this paper presents a novel end-to-end semi-supervised table detection method that employs the deformable transformer for detecting table objects. We evaluate ...
王利民:[ICCV 2023] MeMOTR:长时记忆力增强的Transformer 多目标跟踪器 论文: arxiv.org/pdf/2307.1570 代码: github.com/MCG-NJU/MeMO CO-MOT 论文解读: 颜丙峰:CO-MOT:end-to-end tracking也能SOTA。简单易用,性能拔群 论文: arxiv.org/pdf/2305.1272 代码: github.com/BingfengYan/ QTrack 论文: ar...