পদ্ধতির তুলনা করুন

	দুর্বলভাবে তত্ত্বাবধানে থাকা ইনস্ট্যান্স সেগমেন্টেশন	Semi-supervised Instance Segmentation
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2015–2019	2018–2021
প্রবর্তক	Multiple contributors (e.g., Hsu et al., Khoreva et al.)	Multiple independent research groups (2018–2021)
ধরন	Weakly supervised deep learning for pixel-wise instance delineation	Semi-supervised deep learning for dense prediction
মৌলিক উৎস	Hsu, C.-C., Hsu, K.-J., Tsai, C.-C., Lin, Y.-Y., & Chuang, Y.-Y. (2019). Weakly supervised instance segmentation using the bounding box tightness prior. Advances in Neural Information Processing Systems (NeurIPS), 32.	Hu, H., Wei, P., Zheng, H., Bai, X., Wei, Y., & Chen, Y. (2021). Semi-supervised Semantic Segmentation via Adaptive Equalization Learning. Advances in Neural Information Processing Systems (NeurIPS), 34, 22106–22118.
অপর নাম	WSIS, weakly-supervised mask prediction, weak-label instance segmentation, box-supervised instance segmentation	Semi-supervised Mask R-CNN, pseudo-label instance segmentation, label-efficient instance segmentation, SSIS
সম্পর্কিত	6	6
সারসংক্ষেপ	Weakly supervised instance segmentation trains deep networks to delineate individual object instances at pixel level using only cheap, incomplete annotations — such as bounding boxes, image-level labels, or point clicks — rather than costly full pixel-wise masks. It dramatically reduces annotation effort while still producing instance-level masks for each object in an image.	Semi-supervised instance segmentation trains a model to detect and delineate every object instance in an image using a small labeled set and a large unlabeled image corpus. By generating pseudo-labels from confident predictions on unlabeled images and enforcing consistency under augmentation, the approach achieves competitive mask accuracy at a fraction of the full annotation cost.