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AI Research
arXiv CS.AI·May 27·6 min read
AnchorDiff: Training-Free Concept Grounding for MM-DiTs via Anchor-Based Graph Propagation
TL;DR
Multi-Modal Diffusion Transformers (MM-DiTs) struggle with concept leakage—where attention activations for target objects spill onto visually similar non-target objects. AnchorDiff, a training-free method, decouples semantic localization from structural refinement using anchor-based graph propagation.
✦ Why It Matters
Engineers can apply AnchorDiff to improve concept precision in generative AI systems without retraining expensive models.
Key Takeaways
- Utilize AnchorDiff for training-free concept grounding.
- Reduce concept leakage in visually similar scenarios.
- Leverage the Multi-Concept Confusion Dataset for evaluation.
- Improve semantic localization in your models.
How It Works
AnchorDiff operates by selecting a high-confidence anchor from the attention map of the model, which represents the relationship between concepts and images. This anchor is then propagated through a hybrid graph that combines self-attention mechanisms, allowing for dense connections within the same object while minimizing connections to other objects.
This decoupling of semantic localization from structural refinement helps to clarify the model's focus on the intended target.