Towards More Unified In-context Visual Understanding

Sheng Dianmo, Chen Dongdong, Tan Zhentao, Liu Qiankun, Chu Qi, Bao Jianmin, Gong Tao, Liu Bin, Xu Shengwei, Yu Nenghai. Arxiv 2023

[Paper]    
In Context Learning Model Architecture Multimodal Models Pretraining Methods Prompting Reinforcement Learning Tools Transformer

The rapid advancement of large language models (LLMs) has accelerated the emergence of in-context learning (ICL) as a cutting-edge approach in the natural language processing domain. Recently, ICL has been employed in visual understanding tasks, such as semantic segmentation and image captioning, yielding promising results. However, existing visual ICL framework can not enable producing content across multiple modalities, which limits their potential usage scenarios. To address this issue, we present a new ICL framework for visual understanding with multi-modal output enabled. First, we quantize and embed both text and visual prompt into a unified representational space, structured as interleaved in-context sequences. Then a decoder-only sparse transformer architecture is employed to perform generative modeling on them, facilitating in-context learning. Thanks to this design, the model is capable of handling in-context vision understanding tasks with multimodal output in a unified pipeline.Experimental results demonstrate that our model achieves competitive performance compared with specialized models and previous ICL baselines. Overall, our research takes a further step toward unified multimodal in-context learning.

Similar Work