Harmonizing Visual Representations for Unified Multimodal Understanding and Generation
This addresses the problem of heterogeneous visual tasks for AI researchers and practitioners, offering a unified approach that is incremental by building on masked autoregressive methods.
The paper tackles the challenge of unifying visual understanding and generation in a single multimodal framework by introducing Harmon, which uses a shared masked autoregressive encoder to achieve state-of-the-art image generation results on benchmarks like GenEval, MJHQ30K, and WISE while matching dedicated semantic encoders on understanding tasks.
Unifying visual understanding and generation within a single multimodal framework remains a significant challenge, as the two inherently heterogeneous tasks require representations at different levels of granularity. Current approaches that utilize vector quantization (VQ) or variational autoencoders (VAE) for unified visual representation prioritize intrinsic imagery features over semantics, compromising understanding performance. In this work, we take inspiration from masked image modelling (MIM) that learns rich semantics via a mask-and-reconstruct pre-training and its successful extension to masked autoregressive (MAR) image generation. A preliminary study on the MAR encoder's representation reveals exceptional linear probing accuracy and precise feature response to visual concepts, which indicates MAR's potential for visual understanding tasks beyond its original generation role. Based on these insights, we present \emph{Harmon}, a unified autoregressive framework that harmonizes understanding and generation tasks with a shared MAR encoder. Through a three-stage training procedure that progressively optimizes understanding and generation capabilities, Harmon achieves state-of-the-art image generation results on the GenEval, MJHQ30K and WISE benchmarks while matching the performance of methods with dedicated semantic encoders (e.g., Janus) on image understanding benchmarks. Our code and models will be available at https://github.com/wusize/Harmon.