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Found 5 papers, 5 papers with code
DHR: Dual Features-Driven Hierarchical Rebalancing in Inter- and Intra-Class Regions for Weakly-Supervised Semantic Segmentation
Weakly-supervised semantic segmentation (WSS) ensures high-quality segmentation with limited data and excels when employed as input seed masks for large-scale vision models such as Segment Anything.
TTD: Text-Tag Self-Distillation Enhancing Image-Text Alignment in CLIP to Alleviate Single Tag Bias
We identify a critical bias in contemporary CLIP-based models, which we denote as \textit{single tag bias}.
Ranked #1 on
Open Vocabulary Semantic Segmentation
on COCO-Stuff-171
(mIoU metric)
Multi-Label Text Classification
Open Vocabulary Semantic Segmentation
+3
MARS: Model-agnostic Biased Object Removal without Additional Supervision for Weakly-Supervised Semantic Segmentation
Weakly-supervised semantic segmentation aims to reduce labeling costs by training semantic segmentation models using weak supervision, such as image-level class labels.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
RecurSeed and EdgePredictMix: Pseudo-Label Refinement Learning for Weakly Supervised Semantic Segmentation across Single- and Multi-Stage Frameworks
Although weakly supervised semantic segmentation using only image-level labels (WSSS-IL) is potentially useful, its low performance and implementation complexity still limit its application.
Ranked #10 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Puzzle-CAM: Improved localization via matching partial and full features
Weakly-supervised semantic segmentation (WSSS) is introduced to narrow the gap for semantic segmentation performance from pixel-level supervision to image-level supervision.
