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

TL;DR

InstructSeq is a multimodal transformer framework that unifies diverse vision tasks through natural language instructions, enabling flexible, instruction-driven visual task execution without task-specific tuning.

Contribution

It introduces a unified, instruction-conditioned multimodal model that handles multiple vision tasks using natural language control and a transformer architecture.

Findings

Achieves strong performance on semantic segmentation, referring expression tasks, and image captioning.

Operates effectively without task-specific fine-tuning.

Provides an intuitive natural language interface for diverse vision tasks.

Abstract

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.