SAM 3 can be applied training-free to remote sensing open-vocabulary segmentation and change detection by fusing its semantic and instance heads and filtering with presence scores.
Clearclip: Decomposing clip repre- sentations for dense vision-language inference
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
ReCLIP++ rectifies class and space biases in CLIP via separate reference and positional features, logit subtraction, and a mask decoder with contrastive loss to improve unsupervised semantic segmentation on PASCAL VOC, ADE20K and other benchmarks.
GCLIP improves training-free CLIP semantic segmentation by fusing intermediate attention maps and suppressing an abnormal FFN channel.
citing papers explorer
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SegEarth-OV3: Exploring SAM 3 for Open-Vocabulary Semantic Segmentation in Remote Sensing Images
SAM 3 can be applied training-free to remote sensing open-vocabulary segmentation and change detection by fusing its semantic and instance heads and filtering with presence scores.
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ReCLIP++: Learn to Rectify the Bias of CLIP for Unsupervised Semantic Segmentation
ReCLIP++ rectifies class and space biases in CLIP via separate reference and positional features, logit subtraction, and a mask decoder with contrastive loss to improve unsupervised semantic segmentation on PASCAL VOC, ADE20K and other benchmarks.
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Rethinking the Global Knowledge of CLIP in Training-Free Open-Vocabulary Semantic Segmentation
GCLIP improves training-free CLIP semantic segmentation by fusing intermediate attention maps and suppressing an abnormal FFN channel.