arXiv:2605.26460v1 Announce Type: cross Abstract: Multi-Modal Diffusion Transformers (MM-DiTs) encode rich representations for training-free concept grounding, but existing attention-based methods often produce overlapping activations on visually confusable concepts, a failure mode we call concept leakage, where target responses spill over to non-target objects. To address this issue, we propose AnchorDiff, a training-free grounding method that decouples semantic localization from structural refinement. AnchorDiff selects a high-confidence anchor from concept-to-image attention map and propaga
The continuous development in multi-modal AI systems like MM-DiTs creates an ongoing need for improved grounding methods to enhance reliability and interpretability.
Improved concept grounding in multi-modal AI systems enhances their reliability and interpretability, crucial for deploying them in sensitive applications and for more robust AI agent development.
The ability of MM-DiTs to accurately and reliably associate concepts with visual elements will be significantly improved, reducing 'concept leakage' and leading to more precise AI outputs.
- · AI developers
- · Multi-modal AI research
- · Generative AI applications
- · Existing attention-based grounding methods
More precise and reliable multi-modal AI models for tasks like image captioning and content generation.
Accelerated adoption of advanced multi-modal AI in new sectors due to increased trustworthiness and reduced errors.
Enhanced development of AI agents capable of more nuanced understanding and interaction with complex visual information.
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Read at arXiv cs.AI