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Found 3 papers, 3 papers with code
SeiT++: Masked Token Modeling Improves Storage-efficient Training
A recent breakthrough by SeiT proposed the use of Vector-Quantized (VQ) feature vectors (i. e., tokens) as network inputs for vision classification.
Threshold Matters in WSSS: Manipulating the Activation for the Robust and Accurate Segmentation Model Against Thresholds
Existing WSSS methods commonly argue that the sparse coverage of CAM incurs the performance bottleneck of WSSS.
Ranked #17 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Weakly supervised segmentation
Weakly supervised Semantic Segmentation
+1
Railroad is not a Train: Saliency as Pseudo-pixel Supervision for Weakly Supervised Semantic Segmentation
Existing studies in weakly-supervised semantic segmentation (WSSS) using image-level weak supervision have several limitations: sparse object coverage, inaccurate object boundaries, and co-occurring pixels from non-target objects.
Ranked #22 on
Weakly-Supervised Semantic Segmentation
on PASCAL VOC 2012 test
(using extra training data)
