Hi,
Thank you for your review of ressources in transfer learning.
I recommend to add the paper "LSTD: A Low-Shot Transfer Detector for Object Detection" (https://arxiv.org/abs/1803.01529).
You can find the implementation in Caffe here.
It uses a mix of SSD & Faster R-CNN but the interest is more in the regularization techniques the authors have created.
Also there is an implementation in Pytorch but it's buggy and seems dead.
To be complete, there is an application of the LSTD for "document layout analysis"/"document segmentation" using text mining for the classifier.
Hi, Thank you for your review of ressources in transfer learning. I recommend to add the paper "LSTD: A Low-Shot Transfer Detector for Object Detection" (https://arxiv.org/abs/1803.01529). You can find the implementation in Caffe here. It uses a mix of SSD & Faster R-CNN but the interest is more in the regularization techniques the authors have created. Also there is an implementation in Pytorch but it's buggy and seems dead. To be complete, there is an application of the LSTD for "document layout analysis"/"document segmentation" using text mining for the classifier.
Finally, there is a model called "Zero-Shot Object Detection" that could be interesting but I didn't read yet.