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arXiv Computer Vision By Haoqian Zhang, Ziyuan Yang, Zerui Shao, Yi Zhang

Attributing HOW, Not Just WHICH: Counterfactual Response Trajectories for Diffusion Models

Read the original on arXiv Computer Vision →

The paper introduces CADT, a Concept Attribution method through Dynamic Trajectories, which attributes diffusion model outputs by tracking factor-induced internal response trajectories rather than scalar responses. CADT constructs matched counterfactual pairs at identical noisy states to isolate factor-specific representation displacements and models their directional and magnitude evolution across denoising as dynamic attribution signatures. Experiments on multiple public datasets demonstrate consistent improvements over existing diffusion attribution baselines for hierarchical, compositional, and style attribution tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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