具体来说,我们首先将一个图像编码为一个patch序列,并构建一个基于 transformer 的 strong baseline,并有一些关键的改进,用基于 CNN 的方法在几个 ReID 基准测试上取得了具有竞争力的结果。为了进一步改进 transformers 中上下文的鲁棒特征学习,我们精心设计了两个新的模块。(i)提出 the jigsaw patch module(JPM),通...
1. Transformer-based strong baseline 基本架构如上图,输入的图片分patches,然后经过线性映射后,在加入一个[cls] token(图中的*),添加position embedding后送入Transformer。Transformer的好处在于不用做下采样操作,每一个Transformer Layer都有全局的感受野,因此可以提取细节信息。 Overlapping Patches——切Patch的时候用...
Abaltion Study of Transformer-based Strong Baseline Requirements Installation pip install -r requirements.txt (we use /torch 1.6.0 /torchvision 0.7.0 /timm 0.3.2 /cuda 10.1 / 16G or 32G V100fortraining and evaluation. Note that we use torch.cuda.amp to accelerate speed of training which re...
In this paper, we explore the Vision Transformer (ViT), a pure transformer-based model, for the object re-identification (ReID) task. With several adaptations, a strong baseline ViT-BoT is constructed with ViT as backbone, which achieves comparable results to convolution neural networks-(CNN-)...
2021.3 We release the code of TransReID. Pipeline Abaltion Study of Transformer-based Strong Baseline Requirements pip install -r requirements.txt (we use /torch 1.6.0 /torchvision 0.7.0 /timm 0.3.2 /cuda 10.1 / 16G or 32G V100fortraining and evaluation. Note that we use torch.cuda.amp ...
baseline 简要概括以下 ReID 领域的一些工作: 一般ReID 的 pipeline 是用 CNN 提取特征,使用 cross-entropy (ID loss) 和 triplet loss 来进行训练。当然也有 circle loss(CVPR 2020) 将前面两者很好的结合了起来。 细粒度特征:水平分片、语义分割、姿态估计。
Finally, to serve as a baseline for Transformer-based language models, we used GloVe vectors37, which capture the “static” semantic content of a word across contexts. Conceptually, GloVe vectors are similar to the vector representations of text input to BERT prior to any contextualization applied...
Chen J al (2021) Transunet: Transformers make strong encoders for medical image segmentation. CoRR. abs/2102.04306, pp 1–13 Cao H al (2022) Swin-unet: Unet-like pure transformer for medical image segmentation. In: European conference on computer vision (ECCV), pp 205–218 Wang L, Li R...
Datasets Market-1501DukeMTMC-reIDMSMT17VehicleIDVeRi-776Occluded REIDMarket-1501-C Results from the Paper Edit Ranked #1 onPerson Re-Identification on Market-1501-C Get a GitHub badge TaskDatasetModelMetric NameMetric ValueGlobal RankResultBenchmark ...
regression heads are added to predict the bounding box on the output center feature representation. Our design reduces the convergence difficulty and computational complexity of the transformer structure. The results show significant improvements over the strong baseline of anchor-free object detection netwo...