Code release for "Cut and Learn for Unsupervised Object Detection and Instance Segmentation" and "VideoCutLER: Surprisingly Simple Unsupervised Video Instance Segmentation"
Hi, fantastic work on CutLER! From my understanding, CutLER is an approach to train a supervised detector, such as a Cascade Mask R-CNN, in an unsupervised manner using ViT, MaskCut, and DropLoss techniques. This implies that, theoretically, we should be able to replace the R-CNN detector with a single-stage detector like YOLO, provided we can modify the loss function accordingly.
I am planning to implement a few-shot learning approach by training a self-supervised model using the CutLER methodology on YOLOv8/YOLOv10, followed by fine-tuning on a specific dataset. Could you provide any suggestions on how to modify the YOLOv10 loss function to be compatible with the CutLER approach? Thank you!
Hi, fantastic work on CutLER! From my understanding, CutLER is an approach to train a supervised detector, such as a Cascade Mask R-CNN, in an unsupervised manner using ViT, MaskCut, and DropLoss techniques. This implies that, theoretically, we should be able to replace the R-CNN detector with a single-stage detector like YOLO, provided we can modify the loss function accordingly. I am planning to implement a few-shot learning approach by training a self-supervised model using the CutLER methodology on YOLOv8/YOLOv10, followed by fine-tuning on a specific dataset. Could you provide any suggestions on how to modify the YOLOv10 loss function to be compatible with the CutLER approach? Thank you!