Kim, S., Choi, J., Kim, T., & Kim, C. (2019). Self-training and adversarial background regularization for unsupervised domain adaptive one-stage object detection. In Proceedings of the IEEE International Conference on Computer Vision (pp. 6092-6101).
Introduction
The paper introduce a weak self-training (WST) method and adversarial background score regularization (BSR) for domain adaptive one-stage object detection.
Approach
Problem Setting:
We assume that source data is drawn from the source domain , and target data is drawn from the target domain . Here, x is an image and  is a corresponding label, where  is the coordinates of the bounding box and  is the class to which the object belongs. We denote the distribution of domain  as , and . <br />False Positive(假正，**FP**)：将负类预测为正类数<br /> False Negative(假负，**FN**)：将正类预测为负类数
Weak Self-Training(WST)：
Reducing False Negatives: 
论文中采用SSD作为基本检测器，如上图原loss分为三部分，
和
分别指的是目标多分类和背景的二分类loss，
指的是localization loss，在SSD中即是offset
.
Negative examples in the set have a large potential of being foregrounds,Thus, the proposed method choose 
examples that have the lowest confidence loss value among negative examples in . the method do not update the network for bounding box regression since pseudo-labels usually have inaccurate bound- ing box information. The modified loss function:
Reducing False Positives: supporting RoIs denote examples having IoU value larger than some threshold 
with the final detection 
. SRRS (Supporting Region-based Reliable Score):
首先提出的论文采用SSD，输出是
,
is the 
detection and 
is the total number of detections (e.g., n = 8732 for SSD300), 在NMS之后的输出为
, 论文中选用这个作为pseduo的候选框。在这里，简单来说就是提出了一个SRRS来评分每一个产生的positive pseduo label,而不是简单使用a single confidence score, 作者认为all the boxes 都趋向于最终的检测结果
, 对于每一个计算每个和这个框的
,再取平均值，如上述公式，最终得到一个score（SRRS）,然后与一个阈值比较得到最后的pseduo label。
Adversarial Background Score Regularization（对抗背景评分机制）:
backgrounds of the source domain and target domain share less common features compared to those of foregrounds. Motivated by [3], the paper propose background score regularization (BSR) in an adversarial way. BSR extracts discriminative features for target backgrounds.
上面是一个二分类交叉熵，当我们minimize the loss的时候，the value of 
趋向于t。反之当我们maximize the loss的时候,
趋向于0或者1.
具体的BSR背景对抗规则如下：
对于source输入，都进行minimize强监督优化学习。对于target inputs，优化C（classifier）的输出接近t，通过minimize 
loss。相反，优化 F（Feature extraction）的输出趋向于0或者1. 从而最终造成F的error增大，去欺骗C进行unkown class的判别。进而学习得到discriminative feature。
最终的一个背景对抗评分loss如下图所示：
在这里
. 具体实施在这里，the paper enable the adversarial training using gradient reversal layer (GRL) right after relu4 3 of SSD300. Figure 4 depicts the process of the adversarial learning.
**
若有收获，就点个赞吧
0 人点赞
