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Images Talk Back: Guiding MMDiTs by Suppressing Image-to-Text Feedback

Paper recorded by Signals 4 on 2026-10-09 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-09 on arXiv · recorded by Signals 4 on 2026-10-12

Category: cs.CV · 计算机视觉 · first seen 2026-10-12

Abstract

Multimodal diffusion transformers (MMDiTs) allow the evolving image to update internal text representations through joint attention. These representations then guide subsequent image updates, raising a question: does this feedback help correct deviations from the prompt, or preserve them? We find that image-to-text (I2T) feedback can preserve the objects and attributes already represented in the e

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#293 most recent of 756 cs.CV papers we have recorded · ↑ newer: InstantFusion: A Shared Latent Interface for Heterogeneous Image Gener · ↓ older: Chronos Policy: Learning Robot Policies on Physical Time
Cite this page: Images Talk Back: Guiding MMDiTs by Suppressing Image-to-Text Feedback: the #293 most recent of 756 cs.CV papers we have recorded (as of 2026-10-09). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/images-talk-back-guiding-mmdits-by-suppressing-image-to-text-feedback.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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