InstructSeg: Unifying Instructed Visual Segmentation with Multi-modal Large Language Models (2412.14006v1)

Abstract: Boosted by Multi-modal LLMs (MLLMs), text-guided universal segmentation models for the image and video domains have made rapid progress recently. However, these methods are often developed separately for specific domains, overlooking the similarities in task settings and solutions across these two areas. In this paper, we define the union of referring segmentation and reasoning segmentation at both the image and video levels as Instructed Visual Segmentation (IVS). Correspondingly, we propose InstructSeg, an end-to-end segmentation pipeline equipped with MLLMs for IVS. Specifically, we employ an object-aware video perceiver to extract temporal and object information from reference frames, facilitating comprehensive video understanding. Additionally, we introduce vision-guided multi-granularity text fusion to better integrate global and detailed text information with fine-grained visual guidance. By leveraging multi-task and end-to-end training, InstructSeg demonstrates superior performance across diverse image and video segmentation tasks, surpassing both segmentation specialists and MLLM-based methods with a single model. Our code is available at https://github.com/congvvc/InstructSeg.

• The paper unifies four text-guided segmentation tasks under one end-to-end framework, streamlining previously separate approaches.
• The paper introduces innovative modules like the Object-aware Video Perceiver and Vision-guided Multi-granularity Text Fusion to boost segmentation accuracy.
• The model achieves superior performance on benchmarks, notably improving IoU metrics on datasets such as RefCOCO and ReVOS for both image and video tasks.

An Expert Overview of "InstructSeg: Unifying Instructed Visual Segmentation"

The paper "InstructSeg: Unifying Instructed Visual Segmentation" presents a methodological advance in the field of computer vision, focusing on combining various text-guided segmentation tasks under a unified framework called Instructed Visual Segmentation (IVS). This paper explores the intersection of referring and reasoning segmentation across both image and video domains and introduces a model, InstructSeg, that effectively addresses these tasks using Multi-modal LLMs (MLLMs).

Core Contributions

1. Unified Framework for Segmentation Tasks: InstructSeg merges four specific text-guided segmentation tasks: referring expression segmentation (RES), reasoning segmentation (ReasonSeg), referring video object segmentation (R-VOS), and reasoning video object segmentation (ReasonVOS). This unified approach streamlines the processing and solution space for these tasks, which have traditionally been treated separately.
2. Innovative Model Components:
   • Object-aware Video Perceiver (OVP): This module is designed to adeptly extract temporal and object-centric information from videos, which is critical for understanding dynamic scenes in video segmentation.
   • Vision-guided Multi-granularity Text Fusion (VMTF): This module enhances text and visual interaction by incorporating global and detailed levels of textual instruction with visual data, improving comprehension and segmentation accuracy.
3. Superior Performance Across Benchmarks: InstructSeg demonstrates significant performance gains in a variety of benchmarks. For instance, it outperforms previous state-of-the-art models in both image-level and video-level tasks, including RefCOCO datasets for referring segmentation and ReVOS for reasoning video segmentation. The superiority is highlighted by impressive improvements in metrics such as Intersection-over-Union (IoU) for segmentation accuracy.
4. End-to-End Model Training: The end-to-end training pipeline of InstructSeg facilitates performance that is not only superior but also efficient. It enables the model to handle diverse segmentation tasks with a single architecture, reducing the complexity and potential errors introduced by task-specific models.

Research Implications and Future Directions

The introduction of InstructSeg has several implications for the field of computer vision. By unifying segmentation tasks under a common framework, it simplifies the application of MLLMs to complex visual understanding problems. This unification could pave the way for more scalable solutions that leverage the power of MLLMs in multi-task environments without the need for extensive retraining for domain-specific adjustments.

In future developments, researchers can explore the following avenues:

• Enhanced Multi-modal Fusion Techniques: Building on the concept of VMTF, future research could further refine how textual and visual data interact, possibly through more sophisticated attention mechanisms or novel training paradigms.
• Scalability and Efficiency: Further optimizations to reduce computational overhead will be important for making such solutions viable in real-time applications or on constrained hardware.
• Robustness and Generalization: Testing and improving the robustness of such systems under varied real-world conditions will ensure broader applicability.

In summary, the paper presents a well-structured and technically sound advancement in visual segmentation, offering significant contributions both to theoretical understanding and practical implementations in AI-driven image and video analysis.