然后,transformer解码器将一小部分固定数量的学习到的位置嵌入(我们称之为对象查询)作为输入,并额外参加到编码器输出的处理中(A transformer decoder then takes as input a small fixed number of learned positional embeddings, which we call object queries, and additionally attends to the encoder output. )。
2.3 目标检测之前的相关工作 DETR主要的两个特点 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...
它包含三个主要组件,我们将在下面描述:用于提取紧凑特征表示的CNN backbone、 encoder-decoder transformer和用于进行最终检测预测的简单前馈网络(FFN,feed forward network )。 与许多现代检测器不同,DETR可以在任何提供了常见的CNN backbone和Transformer架构的深度学习框架中实现,且只需几百行。在Pytorch[32]中,DETR的...
Transformer解码器: 解码器遵循Transformer的标准架构,使用多头自定义和编码器-解码器注意机制转换尺寸为d的N个嵌入。 与原始Transformer的不同之处在于,我们的模型在每个解码器层并行解码N个目标,而Vaswani等人[47]使用自回归模型,每次预测一个元素的输出序列。 我们请不熟悉这些概念的读者参阅补充材料。 由于译码器也是...
Where applicable, we also propose remedies that mitigate some of the issues faced when adopting such Transformer-based detection. The proposed end-to-end architecture avoids much of the post-processing steps demanded by most current detectors, and outperforms the state-of-the-art methods on two ...
将两种embedding conca之后作为Transformer的输入。 Lpretraining=LMLM+LMVM+LITM ,正如前面所说,优化目标就是这3个任务。 Experiment 一些结果的罗列:可以看到,效果相比region-based的一些方法并不差。 本文提出的VD的Ablation Study, 其实不要VD也能做的不错了?只不过这个VD似乎可以有一些可解释性。 VD的可视化:...
Although these detectors have been widely used, they are often complained for complex pre- and post-designs, e.g., anchor sizes, standards for positive and negative samples, and non-maximum suppression (NMS) on results. DEtec- tion TRansformer (DETR) simplifies the fra...
The recently proposed end-to-end transformer detectors, such as DETR and Deformable DETR, have a cascade structure of stacking 6 decoder layers to update object queries iteratively, without which their performance degrades seriously. In this paper, we investigate that the random initialization of obj...
1) To our knowledge, we propose the first cascaded transformer-based framework for end-to-end per- son search. The progressive design effectively balances per- son detection and ReID and the transformers help attend to scale and pose/viewpoint changes. 2) We improve perfor- mance with a...
Structure of the proposed model. The structure combines the merits of both transformer and convolutional networks As such, it can exploit global dependencies and locality informations. Full size image Data augmentation Data augmentation is an umbrella of techniques that can be used to generate additiona...