Weakly supervised segmentation using extracted semantic features from CLIP with WeCLIP
Weakly supervised segmentation using extracted semantic features from CLIP with WeCLIP
Frozen CLIP: A Strong Backbone for Weakly Supervised Semantic Segmentation
arXiv paper abstract https://arxiv.org/abs/2406.11189
arXiv PDF paper https://arxiv.org/pdf/2406.11189
... propose WeCLIP, a CLIP-based single-stage pipeline, for weakly supervised semantic segmentation.
... the frozen CLIP model is applied as the backbone for semantic feature extraction, and a new decoder is designed to interpret extracted semantic features for final prediction.
... utilize the above frozen backbone to generate pseudo labels for training the decoder. Such labels cannot be optimized during training.
... propose a refinement module (RFM) to rectify them dynamically ... architecture enforces the proposed decoder and RFM to benefit from each other to boost the final performance.
... approach significantly outperforms other approaches with less training cost.
Additionally, ... WeCLIP also obtains promising results for fully supervised settings ...
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