Unifying Contrastive and Generative Objectives for Visual Understanding and Text-to-Image Generation

Chao Li; Tianhong Li; Sai Vidyaranya Nuthalapati; Hong-You Chen; Satya Narayan Shukla; Jianpeng Cheng; Yonghuan Yang; Jun Xiao; Xiangjun Fan; Aashu Singh; Dina Katabi; Shlok Kumar Mishra

arXiv · 2603.02667

Unifying Contrastive and Generative Objectives for Visual Understanding and Text-to-Image Generation

Chao Li·Tianhong Li·Sai Vidyaranya Nuthalapati·Hong-You Chen·Satya Narayan Shukla·Jianpeng Cheng·Yonghuan Yang·Jun Xiao·Xiangjun Fan·Aashu Singh·Dina Katabi·Shlok Kumar Mishra

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Abstract

Unifying text-image contrastive learning and text-to-image (T2I) generation in a single end-to-end model is challenging because the two objectives demand opposing masking regimes: contrastive alignment needs near-complete visible tokens, while masked generative modeling needs heavy corruption. We introduce DREAM, a unified framework that resolves this conflict through Masking Warmup, a schedule that shifts the center of the masking distribution over training, so low and high masking ratios coexist at every step. This co-exposure lets a single jointly-trained encoder serve both objectives. The resulting stable optimization unlocks Semantically Aligned Decoding at inference: the text encoder, trained against visual embeddings at all masking ratios, can score partially generated images and select the best trajectory with as little as 12.5% of the image decoded, improving both FID and throughput. DREAM outperforms its single-objective baselines, CLIP and FLUID: on ImageNet linear-probing (+1.1%), 5-shot transfer (+4.1%), ADE20K segmentation (+1.9%), and NYU depth estimation (+6.25%) over CLIP, and on CC12M FID (+6.2%) over FLUID while maintaining CLIP Score. Together, these gains show that text-image contrastive and generative objectives, when properly unified, are synergistic rather than competing.

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Chao Li, Tianhong Li, Sai Vidyaranya Nuthalapati, Hong-You Chen, Satya Narayan Shukla, Jianpeng Cheng, Yonghuan Yang, Jun Xiao, Xiangjun Fan, Aashu Singh, Dina Katabi, Shlok Kumar Mishra. 2026-03-03. Unifying Contrastive and Generative Objectives for Visual Understanding and Text-to-Image Generation. https://arxiv.org/abs/2603.02667

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