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Empowering models to dynamically accomplish tasks specified through naturallanguage instructions represents a promising path toward more capable andgeneral artificial intelligence. In this work, we introduce InstructSeq, aninstruction-conditioned multi-modal modeling framework that unifies diversevision tasks through flexible natural language control and handling of bothvisual and textual data. InstructSeq employs a multimodal transformerarchitecture encompassing visual, language, and sequential modeling. We utilizea visual encoder to extract image features and a text encoder to encodeinstructions. An autoregressive transformer fuses the representations andgenerates sequential task outputs. By training with LLM-generated naturallanguage instructions, InstructSeq acquires a strong comprehension of free-forminstructions for specifying visual tasks. This provides an intuitive interfacefor directing capabilities using flexible natural instructions. Without anytask-specific tuning, InstructSeq achieves compelling performance on semanticsegmentation, referring expression segmentation/comprehension, and imagecaptioning. The flexible control and multi-task unification empower the modelwith more human-like versatility and generalizability for computer vision. Thecode will be released soon at https://github.com/rongyaofang/InstructSeq.

InstructSeq: Unifying Vision Tasks with Instruction-conditioned Multi-modal Sequence Generation