Authors: Chengyue Wu, Xiaokang Chen, Zhiyu Wu, Yiyang Ma, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Yu, Chong Ruan, Ping Luo
Abstract: In this paper, we introduce Janus, an autoregressive framework that unifies
multimodal understanding and generation. Prior research often relies on a
single visual encoder for both tasks, such as Chameleon. However, due to the
differing levels of information granularity required by multimodal
understanding and generation, this approach can lead to suboptimal performance,
particularly in multimodal understanding. To address this issue, we decouple
visual encoding into separate pathways, while still leveraging a single,
unified transformer architecture for processing. The decoupling not only
alleviates the conflict between the visual encoder’s roles in understanding and
generation, but also enhances the framework’s flexibility. For instance, both
the multimodal understanding and generation components can independently select
their most suitable encoding methods. Experiments show that Janus surpasses
previous unified model and matches or exceeds the performance of task-specific
models. The simplicity, high flexibility, and effectiveness of Janus make it a
strong candidate for next-generation unified multimodal models.
Source: http://arxiv.org/abs/2410.13848v1
