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Found 5 papers, 2 papers with code
Weakly Supervised Semantic Segmentation via Adversarial Learning of Classifier and Reconstructor
To bring this idea into WSSS, we simultaneously train two models: a classifier generating CAMs that decompose an image into segments and a reconstructor that measures the inferability between the segments.
Ranked #13 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Learning Point Cloud Completion without Complete Point Clouds: A Pose-Aware Approach
Our main idea is to generate multiple incomplete point clouds of various poses and integrate them into a complete point cloud.
Pixel-wise Deep Image Stitching
In this paper, instead of relying on the homography-based warp, we propose a novel deep image stitching framework exploiting the pixel-wise warp field to handle the large-parallax problem.
Exploring Pixel-level Self-supervision for Weakly Supervised Semantic Segmentation
In our framework, with the help of the proposed Regional Contrastive Module (RCM) and Multi-scale Attentive Module (MAM), MainNet is trained by self-supervision from the SupportNet.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Unlocking the Potential of Ordinary Classifier: Class-Specific Adversarial Erasing Framework for Weakly Supervised Semantic Segmentation
In this paper, we review the potential of the pre-trained classifier which is trained on the raw images.
Ranked #30 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
