Placeholder caption — one-line overview of what SeGroS does.
Unified Multimodal Models (UMMs) have emerged as a promising paradigm that integrates multimodal understanding and generation within a unified modeling framework. However, current generative training paradigms suffer from inherent limitations. We present Semantically-Grounded Supervision (SeGroS), a fine-tuning framework designed to resolve the granularity mismatch and supervisory redundancy in UMMs. At its core, we propose a novel visual grounding map to construct two complementary supervision signals. First, we formulate semantic Visual Hints to compensate for the sparsity of text prompts. Second, we generate a semantically-grounded Corrupted Input to explicitly enhance the supervision of masking-based UMMs by restricting the reconstruction loss to core text-aligned regions. Extensive evaluations on GenEval, DPGBench, and CompBench demonstrate that SeGroS significantly improves generation fidelity and cross-modal alignment across various UMM architectures.
Placeholder — describe the two problems you address (granularity mismatch and supervisory redundancy) and the intuition behind grounding supervision on text-aligned regions.
Placeholder — walk through the pipeline: the visual grounding map, the semantic Visual Hints, and the semantically-grounded Corrupted Input. Inline equations render via MathJax, e.g. $\mathcal{L} = \mathcal{L}_{\text{gen}} + \lambda\,\mathcal{L}_{\text{ground}}$.
Placeholder galleries — replace boxes with your qualitative results.
Baseline
SeGroS (Ours)
| Method | GenEval ↑ | DPGBench ↑ | CompBench ↑ |
|---|---|---|---|
| Baseline UMM | — | — | — |
| + SeGroS | — | — | — |
Placeholder — fill in with your numbers.
@article{kim2026segros,
title = {Enhancing Alignment for Unified Multimodal Models via Semantically-Grounded Supervision},
author = {Kim, Jiyeong and So, Yerim and Choi, Hyesong and Hwang, Uiwon and Min, Dongbo},
journal = {arXiv preprint arXiv:2603.19807},
year = {2026}
}