Direct entropy minimization for object detection (YOLOv3)

[saurabh-2905]

Hello @tuanhungvu ,

I am a student at TU Chemnitz, Germany. I am currently working on my master thesis project titled, 'Unsupervised Domain Adaptation for object detection.' I am working on the direct entropy minimization method mentioned in your paper. I am using YOLOv3 as my base architecture for object detection. I just wanted to confirm if I am implementing the method correctly for YOLOv3, as in the paper it is defined for SSD and I could not find any source code of the same for reference.

I am a little confused about the term 'soft-detection map' which is to be used to calculate the entropy for object detection. I read the paper and found some similarities between SSD and YOLOv3 but I am not absolutely sure if I am using the correct feature map during implementation. It would be great if you could help me with this.

1. Could you specify from which exact layer is the 'soft detection' map taken for SSD? By any chance would you know what will be its equivalent in YOLOv3?

2. In the equation,
   
   is it correct that C represents the class probabilities for each anchor box or does it represent all the offsets obtained for each anchor box after applying the kernel?

3. In YOLOv3, feature maps are obtained at 3 different scales. So should the feature map be considered as the output from the previous convolutional layer that would be used for detection or just the class probabilities obtained after processing the feature map to apply softmax and calculate the entropy map?

I hope I am able to express my doubt in a clear way. In case, you need some additional information, please let me know.
Thanks in advance.