GiT: Towards Generalist Vision Transformer through Universal Language
  Interface
Haiyang Wang Hao Tang Li Jiang Shaoshuai Shi Muhammad Ferjad Naeem Hongsheng Li Bernt Schiele Liwei Wang
This paper proposes a simple, yet effective framework, called GiT,
simultaneously applicable for various vision tasks only with a vanilla ViT.
Motivated by the universality of the Multi-layer Transformer architecture (e.g,
GPT) widely used in large language models (LLMs), we seek to broaden its scope
to serve as a powerful vision foundation model (VFM). However, unlike language
modeling, visual tasks typically require specific modules, such as bounding box
heads for detection and pixel decoders for segmentation, greatly hindering the
application of powerful multi-layer transformers in the vision domain. To solve
this, we design a universal language interface that empowers the successful
auto-regressive decoding to adeptly unify various visual tasks, from
image-level understanding (e.g., captioning), over sparse perception (e.g.,
detection), to dense prediction (e.g., segmentation). Based on the above
designs, the entire model is composed solely of a ViT, without any specific
additions, offering a remarkable architectural simplification. GiT is a
multi-task visual model, jointly trained across five representative benchmarks
without task-specific fine-tuning. Interestingly, our GiT builds a new
benchmark in generalist performance, and fosters mutual enhancement across
tasks, leading to significant improvements compared to isolated training. This
reflects a similar impact observed in LLMs. Further enriching training with 27
datasets, GiT achieves strong zero-shot results over various tasks. Due to its
simple design, this paradigm holds promise for narrowing the architectural gap
between vision and language. Code and models will be available at
\url{https://github.com/Haiyang-W/GiT}.