Unified multimodal models combine image understanding and generation within one system, creating an opportunity to improve both capabilities through paired image--text supervision. We examine the outputs of a pretrained unified model in both directions and observe a loss of specificity: generated images can contain the...
Hao-Yue Bai, Yin-Yi Luo, Yi-Cheng He et al.· 0 citations
UniSD integrates complementary mechanisms that address supervision reliability, representation alignment, and training stability, including multi-teacher agreement, EMA teacher stabilization, token-level contrastive learning, feature matching, and divergence clipping, and reveals when self-distillation improves over st...
Yiqiao Jin, Yi-Yang Wang, Lu-Cheng Fu et al.· arXiv.org· 7 citations· ⚡1
Pretrained unimodal models offer a flexible route to multimodal prediction through collaboration, but this collaboration can become less accurate than a surviving unimodal model when modalities are missing. We study this setting using fixed surviving-model anchors and find that collaborative gains can weaken or reverse...
Hao-Yue Bai, Yin-Yi Luo, He-Qing Huang et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
