An Efficient General-Purpose Modular Vision Model via Multi-Task
  Heterogeneous Training
Zitian Chen Mingyu Ding Yikang Shen Wei Zhan Masayoshi Tomizuka Erik Learned-Miller Chuang Gan
We present a model that can perform multiple vision tasks and can be adapted
to other downstream tasks efficiently. Despite considerable progress in
multi-task learning, most efforts focus on learning from multi-label data: a
single image set with multiple task labels. Such multi-label data sets are
rare, small, and expensive. We say heterogeneous to refer to image sets with
different task labels, or to combinations of single-task datasets. Few have
explored training on such heterogeneous datasets. General-purpose vision models
are still dominated by single-task pretraining, and it remains unclear how to
scale up multi-task models by leveraging mainstream vision datasets designed
for different purposes. The challenges lie in managing large intrinsic
differences among vision tasks, including data distribution, architectures,
task-specific modules, dataset scales, and sampling strategies. To address
these challenges, we propose to modify and scale up mixture-of-experts (MoE)
vision transformers, so that they can simultaneously learn classification,
detection, and segmentation on diverse mainstream vision datasets including
ImageNet, COCO, and ADE20K. Our approach achieves comparable results to
single-task state-of-the-art models and demonstrates strong generalization on
downstream tasks. Due to its emergent modularity, this general-purpose model
decomposes into high-performing components, efficiently adapting to downstream
tasks. We can fine-tune it with fewer training parameters, fewer model
parameters, and less computation. Additionally, its modularity allows for easy
expansion in continual-learning-without-forgetting scenarios. Finally, these
functions can be controlled and combined to meet various demands of downstream
tasks.