InstructSeq: Unifying Vision Tasks with Instruction-conditioned
  Multi-modal Sequence Generation
Rongyao Fang Shilin Yan Zhaoyang Huang Jingqiu Zhou Hao Tian Jifeng Dai Hongsheng Li
Empowering models to dynamically accomplish tasks specified through natural
language instructions represents a promising path toward more capable and
general artificial intelligence. In this work, we introduce InstructSeq, an
instruction-conditioned multi-modal modeling framework that unifies diverse
vision tasks through flexible natural language control and handling of both
visual and textual data. InstructSeq employs a multimodal transformer
architecture encompassing visual, language, and sequential modeling. We utilize
a visual encoder to extract image features and a text encoder to encode
instructions. An autoregressive transformer fuses the representations and
generates sequential task outputs. By training with LLM-generated natural
language instructions, InstructSeq acquires a strong comprehension of free-form
instructions for specifying visual tasks. This provides an intuitive interface
for directing capabilities using flexible natural instructions. Without any
task-specific tuning, InstructSeq achieves compelling performance on semantic
segmentation, referring expression segmentation/comprehension, and image
captioning. The flexible control and multi-task unification empower the model
with more human-like versatility and generalizability for computer vision. The
code will be released soon at https://github.com/rongyaofang/InstructSeq.