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Found 7 papers, 3 papers with code
SemFormer: Semantic Guided Activation Transformer for Weakly Supervised Semantic Segmentation
Recent mainstream weakly supervised semantic segmentation (WSSS) approaches are mainly based on Class Activation Map (CAM) generated by a CNN (Convolutional Neural Network) based image classifier.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
SLAMs: Semantic Learning based Activation Map for Weakly Supervised Semantic Segmentation
Recent mainstream weakly-supervised semantic segmentation (WSSS) approaches mainly relies on image-level classification learning, which has limited representation capacity.
Sample hardness based gradient loss for long-tailed cervical cell detection
Due to the difficulty of cancer samples collection and annotation, cervical cancer datasets usually exhibit a long-tailed data distribution.
Delving into the Scale Variance Problem in Object Detection
For most of the single-stage object detectors, replacing the traditional convolutions with MSConvs in the detection head can bring more than 2. 5\% improvement in AP (on COCO 2017 dataset), with only 3\% increase of FLOPs.
Selective Multi-Scale Learning for Object Detection
When integrated with SMSL, two-stage detectors can get around 1. 0\% improvement in AP.
Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation
While class activation map (CAM) generated by image classification network has been widely used for weakly supervised object localization (WSOL) and semantic segmentation (WSSS), such classifiers usually focus on discriminative object regions.
C2AM: Contrastive Learning of Class-Agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation
While class activation map (CAM) generated by image classification network has been widely used for weakly supervised object localization (WSOL) and semantic segmentation (WSSS), such classifiers usually focus on discriminative object regions.
