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Found 3 papers, 0 papers with code
Boosting Few-Shot Semantic Segmentation Via Segment Anything Model
To avoid predicting wrong masks with SAM, we propose a prediction result selection (PRS) algorithm.
Towards Precise Weakly Supervised Object Detection via Interactive Contrastive Learning of Context Information
In spite of intensive research on deep learning (DL) approaches over the past few years, there is still a significant performance gap between WSOD and fully supervised object detection.
Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) with Diverse Inter-Correlations and its application to medical image classification
Existing MIML methods are useful in many applications but most of which suffer from relatively low accuracy and training efficiency due to several issues: i) the inter-label correlations(i. e., the probabilistic correlations between the multiple labels corresponding to an object) are neglected; ii) the inter-instance correlations (i. e., the probabilistic correlations of different instances in predicting the object label) cannot be learned directly (or jointly) with other types of correlations due to the missing instance labels; iii) diverse inter-correlations (e. g., inter-label correlations, inter-instance correlations) can only be learned in multiple stages.
