Key Takeaways
- arXiv cs.CV introduces NUMINA , a training-free identify-then-guide framework for improved numerical alignment.
- NUMINA identifies prompt-layout inconsistencies by selecting discriminative self- and cross-attention heads to derive a countable latent layout.
- It then refines this layout conservatively and modulates cross-attention to guide regeneration.
What It Means
Context
arXiv cs.CV introduces NUMINA , a training-free identify-then-guide framework for improved numerical alignment. NUMINA identifies prompt-layout inconsistencies by selecting discriminative self- and cross-attention heads to derive a countable latent layout. It then refines this layout conservatively and modulates cross-attention to guide regeneration. On the introduced CountBench, NUMINA improves counting accuracy by up to 7.4% on Wan2.1-1.3B, and by 4.9% and 5.5% on 5B and 14B models, respectively. Furthermore, CLIP alignment is improved while maintaining temporal consistency. These results demonstrate that structural guidance complements seed search and prompt enhancement, offering a practical path toward count-accurate text-to-video diffusion. The code is available at https://github.com/H-EmbodVis/NUMINA.
For builders
arXiv cs.CV introduces NUMINA , a training-free identify-then-guide framework for improved numerical alignment.
For Builders
arXiv cs.CV introduces NUMINA , a training-free identify-then-guide framework for improved numerical alignment.