RADSeg adapts the RADIO model with targeted enhancements to deliver 6-30% higher mIoU in zero-shot OVSS while using 2.5x fewer parameters and running 3.95x faster than prior large-model combinations.
Sigmoid loss for language image pre-training
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Inverse attention embeddings combined with standard visual features improve recall in video semantic search for crowded scenes without additional training.
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RADSeg: Unleashing Parameter and Compute Efficient Zero-Shot Open-Vocabulary Segmentation Using Agglomerative Models
RADSeg adapts the RADIO model with targeted enhancements to deliver 6-30% higher mIoU in zero-shot OVSS while using 2.5x fewer parameters and running 3.95x faster than prior large-model combinations.
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Look Beyond Saliency: Low-Attention Guided Dual Encoding for Video Semantic Search
Inverse attention embeddings combined with standard visual features improve recall in video semantic search for crowded scenes without additional training.