ActiveSAM: Image-Conditional Class Pruning for Fast and Accurate Open-Vocabulary Segmentation
arXiv:2606.16996v1 Announce Type: cross Abstract: Segment Anything Model 3 (SAM 3) provides a strong frozen backbone for concept-prompted segmentation, but applying it directly to open-vocabulary semantic segmentation (OVSS) is inefficient: full-resolution decoding is typically run over the entire dataset vocabulary, whereas each image contains only a small active subset of classes. We introduce ActiveSAM, a training-free, zero-shot inference framework that turns SAM 3 into an active-vocabulary segmenter. ActiveSAM first canonicalizes and expands class prompts, then estimates an image-conditio
The continuous evolution of large foundation models like SAM 3 necessitates ongoing innovation in inference efficiency and targeted application to real-world problems.
Improving the efficiency and accuracy of open-vocabulary segmentation makes advanced AI vision more deployable and practical across various industries without extensive retraining.
Machine vision systems can now perform open-vocabulary semantic segmentation more efficiently, reducing computational overhead and enabling broader real-time applications.
- · AI developers
- · Robotics companies
- · Autonomous systems
- · Computer vision applications
More efficient open-vocabulary segmentation allows for quicker development and deployment of vision-guided AI systems.
This efficiency could accelerate the integration of complex visual understanding into industrial automation and consumer devices.
Reduced compute requirements for advanced vision tasks might democratize access to sophisticated AI capabilities for smaller developers and specialized applications.
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Read at arXiv cs.AI