[Paper Review] LISA++: An Improved Baseline for Reasoning Segmentation with Large Language Model

Senqiao Yang, Tianyuan Qu+5 more|arXiv (Cornell University)|Dec 28, 2023

Multimodal Machine Learning Applications4 citations

TL;DR

LISA++ enhances the LISA model by introducing instance segmentation and natural, segmented dialogue capabilities without architectural changes, using curated data from COCO and ADE20K to improve reasoning segmentation and conversational flexibility. It achieves superior performance in multi-turn dialogue and instance-level understanding, demonstrating the scalability of the mask-as-embedding paradigm.

ABSTRACT

While LISA effectively bridges the gap between segmentation and large language models to enable reasoning segmentation, it poses certain limitations: unable to distinguish different instances of the target region, and constrained by the pre-defined textual response formats. In this work, we introduce LISA++, an update to the existing LISA model, focusing on improving core functionalities while keeping the base architecture intact. The main enhancements in LISA++ include: extbf{1) Enhanced Segmentation}: The instance segmentation ability has been added, providing a more detailed scene analysis along with the existing multi-region semantic segmentation. extbf{2) More Natural Conversation}: Improved capability for multi-turn dialogue, with the ability to incorporate segmentation results directly into text responses, i.e., Segmentation in Dialogue (SiD). These improvements are achieved by curating the existing samples of generic segmentation datasets, aimed specifically at enhancing the segmentation and conversational skills without structural change and additional data sources. Comparative analysis with the original LISA model shows significant advancements in these areas, positioning LISA++ as a notable upgrade in visual understanding and interaction. LISA++'s adaptability and improved features highlight the versatility of the mask-as-embedding paradigm proposed by LISA, and the potential as a foundational model for diverse applications.

Motivation & Objective

• To address LISA’s limitations in distinguishing same-category instances and rigid response formats.
• To enable more natural, flexible, and context-aware multimodal conversations with segmentation outputs.
• To extend the mask-as-embedding paradigm to support instance segmentation and dialogue-integrated responses.
• To develop a comprehensive benchmark for reasoning instance segmentation and Segmentation in Dialogue (SiD).
• To maintain architectural simplicity while significantly enhancing reasoning and interaction capabilities.

Proposed method

• Reconstructing instruction-tuning data from COCO and ADE20K to include instance-level queries and responses.
• Designing prompts to generate multi-turn dialogues where segmentation results are embedded in natural language responses (SiD).
• Creating a new benchmark, ReasonSeg-Inst, for evaluating multi-target instance segmentation with reasoning.
• Formulating question-answer pairs that avoid direct reference to annotations, focusing on object properties and relationships.
• Using the same base architecture as LISA, preserving the mask-as-embedding mechanism while enhancing fine-grained reasoning.
• Generating responses that explicitly reference instance IDs and label names (e.g., 'keyboards <34494; keyboard><31264; keyboard>') to enable precise segmentation grounding.

Experimental results

Research questions

• RQ1 Can the mask-as-embedding paradigm be extended to support instance segmentation without architectural changes?
• RQ2 How can segmentation results be naturally integrated into multi-turn dialogues to improve conversational fluency and accuracy?
• RQ3 To what extent does instruction-tuning on curated segmentation data improve reasoning and instance-level understanding in LLMs?
• RQ4 Can a unified framework support both semantic and instance segmentation with consistent, flexible dialogue outputs?
• RQ5 How does LISA++ compare to LISA in terms of dialogue naturalness and segmentation precision across diverse visual reasoning tasks?

Key findings

• LISA++ successfully adds instance segmentation capability to LISA without modifying its base architecture.
• The model demonstrates improved ability to distinguish and refer to multiple instances of the same category in dialogue, such as different people or objects.
• Segmentation in Dialogue (SiD) enables more natural, context-aware, and flexible conversations by embedding segmentation results directly into responses.
• The updated ReasonSeg-Inst benchmark enables comprehensive evaluation of multi-target reasoning segmentation, extending beyond single-target evaluation.
• LISA++ achieves significant improvements in both segmentation accuracy and conversational fluency compared to the original LISA model.
• The mask-as-embedding paradigm proves scalable and effective for complex visual reasoning tasks, supporting future development of foundational multimodal models.