Making Training-Free Diffusion Segmentors Scale with the Generative Power

Researchers have identified limitations in using powerful diffusion models for training-free semantic segmentation tasks, where cross-attention maps fail to effectively capture global semantic relationships and accurately correlate text tokens with image pixels. To address these issues, they propose auto aggregation and per-pixel rescaling techniques that enhance the performance of training-free segmentors on standard benchmarks, making them more effective at leveraging generative capabilities for improved segmentation accuracy.

Read the full article at arXiv cs.CV (Vision)

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