one-stage需要把大量负样本抑制掉, 同时, 要对正样本分好类. 而区域提名只是把大部分的没有意义的负样本抑制掉, 使得第二步的训练数据中, 正负样本都是较为有意义的样本, 从而使得训练更为简单. 虽然在区域提名时并不能很好得完成定位与分类任务, 但只要区域提名提供足够多的候选区域(保证Recall), 并且保证候...
最近公开发布了各种车辆数据集,如DLR3K、VEDAI、UCAS-AOD、DOTA、ITCCVD和EAGLE,并将基于这些数据集的检测模型,如fasterRCNN、YOLO V3和FCOS,引入到车辆检测中。不同的数据集呈现不同的特征,并且它们在图像场景、图像质量、图像空间分辨率和车辆类别方面有所不同。这些开放的车辆数据集中包含了一些清晰、生动、高空间...
Kingma, J. (1987). The new two-stage model of learning: A tool for analyzing the loci of memory differences in intellectually impaired children. In J. Bisanz, C. J. Brainerd, & R. Kail (Eds.), Formal methods in developmental psychology (pp. 38-85). New York: Springer-Verlag....
TDNN的几个变体的效果比起baseline都要好; RankSVM在不同prompt上表现稳定, 而其它的baseline表现方差大, 说明它们学到了许多prompt-dependent的信息, 证明了RankSVM更适合用于第一个stage对prompt-independent信息的提取; 对比TDNN的几个变体, 说明了semantic, POS, syntactic这三个特征都很重要, 但同时使用三个特征...
The experimental results showed a reduction in potential mismatches and an overall high precision and recall. It is concluded that the two-stage framework is capable of performing more precise matching compared to those of other single-stage matching frameworks. Moreover, the two-stage framework ...
一类是two-stage,two-stage检测算法将检测问题划分为两个阶段,首先产生候选区域(region proposals),然后对候选区域分类(一般还需要对位置精修),这一类的典型代表是R-CNN, Fast R-CNN, Faster R-CNN,Mask R-CNN家族。他们识别错误率低,漏识别率也较低,但速度较慢,不能满足实时检测场景。
(1) to evaluate the performance of three state-of-the-art semantic segmentation models using the SegNet, UNet, and DeepLabv3+ network architectures for corn leaf segmentation and multi-class lesion (GLS, NLB, and NLS) segmentation; and (2) build a two-stage model for severity estimation of...
model.fit(train_gen, validation_data=valid_gen, **params) Finally, we can predict unseen data; we just need to change the batch size to 1 to score all observations. # to score all observations params['batch_size'] = 1 test_gen = H5DataLoade...
We consider the problem of minimizing total completion time in a two-stage hybrid flow shop scheduling problem with dedicated machines at stage 2. There exist one machine at stage 1 and two machines at stage 2. Each job must be processed on the single machine at stage 1 and depending upon...
在base model中stage 1 ,2被固定,batch normalization也被固定。 Horizontal image augmentation也被做了。 Ablation Baselines 首先跑R-FCN记为B1, achieves 32.1% mmAP在COCO mini-validation. 大神们也调了一个更强的baseline,记为B2. 通过以下方法: We resize the shorter edge of image to 800 pixels, an...