RSVP: Reasoning Segmentation via Visual Prompting and Multi-modal Chain-of-Thought
Authors: Yi Lu, Jiawang Cao, Yongliang Wu, Bozheng Li, Licheng Tang, Yangguang Ji, Chong Wu, Jay Wu, Wenbo Zhu
Accepted at: acl25

RSVP: Reasoning Segmentation via Visual Prompting and Multi-modal Chain-of-Thought

Yi Lu1,2*, Jiawang Cao1*, Yongliang Wu1,3*, Bozheng Li1,4, Licheng Tang1, Yangguang Ji1, Chong Wu5, Jay Wu1, Wenbo Zhu1

1Opus AI Research  2University of Toronto  3Southeast University  4Brown University  5City University of Hong Kong
tomlu@cs.toronto.edu wenbo_zhu@berkeley.edu

Abstract
Multi-modal Large Language Models (MLLMs) have demonstrated remarkable reasoning capability while lacking explicit mechanisms for visual grounding and segmentation, creating a gap between cognitive reasoning and visual perception. To bridge this gap, we introduce Reasoning Segmentation via Visual Prompting (RSVP), a novel framework that unifies multi-step multimodal reasoning with grounded visual understanding. RSVP is a two-stage structuralized framework that integrates reasoning-driven localization with segmentation refinement. In the reasoning stage, RSVP employs multimodal chain-of-thought visual prompts to help MLLMs understand queries and infer targets, generating interpretable region proposals that enhance visual grounding. In the segmentation stage, RSVP refines these proposals with a Vision-Language Segmentation Module (VLSM), which seamlessly integrates textual and visual cues to produce precise segmentation masks. By explicitly modeling the interaction between multimodal reasoning and segmentation, RSVP introduces a new paradigm for interpretable reasoning segmentation. It exploits MLLMs’ inherent localization capabilities, enabling the models to not only reason about objects but also generate structured visual representations. Our extensive experiments demonstrate that RSVP achieves state-of-the-art performance, surpasses state-of-the-art methods by up to +6.5 glOu and +9.2 cloU on ReasonSeg, and achieves 49.7 mAP on SegInW under zero-shot settings. These results validate RSVP as an effective and scalable framework for integrating cognitive reasoning with structured visual understanding.

1 Introduction
Recent advances in multi-modal learning have enhanced MLLMs’ ability to reason about visual content (Cao et al., 2024; Peng et al., 2025). However,

(a) Highlight of the comparison among the result of RSVP-GPT, the result of LISA-13B, and the ground truth mask.

(b) In reasoning segmentation tasks (cloU result on ReasonSeg), while the segmentation model remains unchanged, enhancing reasoning results in significant improvements.

Figure 1: (a) and (b) depict different aspects of our segmentation pipeline performance. More demo results are available in Appendix C.

a key challenge remains unresolved: bridging the gap between cognitive reasoning and visual segmentation.

Reasoning Segmentation has emerged as a crucial task in multi-modal grounding, requiring models to produce segmentation masks from complex, implicit textual queries (Lai et al., 2024). Unlike traditional referring segmentation, which relies on explicit descriptions, reasoning segmentation demands models to infer whether a target object ex-
ists and where it is located using common sense knowledge and multi-step reasoning based on both textual and visual information. This makes it a significant step toward more interpretable and intelligent vision-language systems.

While Large Language Models (LLMs) excel at logical reasoning and contextual inference, they lack visual processing capabilities. MLLMs combine textual and visual modalities but remain incapable of generating precise segmentation masks (Das et al., 2024; Hu et al., 2024; Yang et al., 2023c). Conversely, referring segmentation models can identify object boundaries but struggle with high-level inference and reasoning, preventing them from effectively tackling reasoning segmentation. Existing solutions attempt to close this gap through fine-tuning large language-segmentation models on large-scale datasets, which is costly and impractical, or through heavy supervised training, which lacks scalability. While parameter-efficient tuning methods like LoRA (Hu et al., 2021) reduce computational costs, they still require substantial effort. Moreover, these models lack modularity—new architectures require retraining on large-scale data to benefit from performance improvements.

To address these challenges, we propose RSVP: Reasoning Segmentation via Visual Prompting, a reasoning-driven multi-stage framework that unifies multi-modal chain-of-thought prompting with visual segmentation. Unlike prior methods that treat reasoning and segmentation as separate components, RSVP explicitly models their interaction, enabling MLLMs to generate interpretable, step-wise region proposals that bridge reasoning and segmentation within a modular framework following a two-stage pipeline:
Reasoning Stage. By introducing Multi-modal Chain-of-Thought Visual Prompting, MLLMs are guided to understand queries, infer object properties, reason about existence, and generate region proposals, enabling explicit visual grounding.
Segmentation Stage. These coarse proposals are refined using a Vision-Language Segmentation Module (VLSM), which integrates textual and visual cues to produce precise segmentation masks.

By integrating structured reasoning with segmentation, RSVP enables MLLMs to reason about objects while producing explainable visual representations. Experiments on ReasonSeg (Lai et al., 2024) and SegInW (Zou et al., 2023) show state-of-the-art zero-shot performance, surpassing zero-shot baselines by +6.5 gIoU, +9.2 cIoU, and achieving 49.7 mAP. Notably, RSVP consistently outperforms baselines on both open-source and closed-source LLM foundations, demonstrating strong generalization.

Our contributions are summarized as follows:

• We propose a reasoning-driven multi-stage framework that leverages MLLMs’ reasoning capabilities for explicit region proposal generation, significantly reducing training costs while improving interpretability.

• We develop a multi-modal chain-of-thought prompting paradigm that bridges the gap between reasoning and segmentation by producing object properties, explainable rationales, and structured region proposals.

• A joint text-image segmentation model is developed following our design, that achieves state-of-the-art results on ReasonSeg (gIOU=60.3, cIOU=60.0) and SegInW (mAP=49.7) under zero-shot settings, demonstrating its effectiveness in both reasoning and open-world segmentation.

2 Related Work

2.1 Reasoning Segmentation

First introduced by LISA (Lai et al., 2024), Reasoning Segmentation extends referring segmentation by requiring models to reason over implicit queries. Unlike traditional referring segmentation which directly identifies objects based on simple descriptions (e.g., “The brown dog at the front”), reasoning segmentation involves abstract contextual inference (e.g., “What area in the picture could lead to other parts of the garden?”). This demands models to integrate visual understanding, object properties, common sense and world knowledge.

2.2 Multi-modal Large Language Models

MLLMs combine vision encoders with language models to process multi-modal inputs, bridging textual and visual tasks (Zhang et al., 2024a; Wu et al., 2024c; Zhang et al., 2025). Open-source models such as LLaVA (Liu et al., 2023b) and Mini-GPT4 (Zhu et al., 2023) demonstrate remarkable generalization on downstream tasks, while proprietary systems like GPT-4o (OpenAI et al., 2024) and Gemini (Team et al., 2024) push the limits of multi-modal cognition (Li et al., 2025).
Figure 2: Overview of the proposed model. An input image is divided into horizontal and vertical regions to assist localization. In the reasoning stage, an MLLM receives a query about the object’s protective features and identifies “the shell” as the protective object, generating region proposal using region IDs (\( id_v \) and \( id_a \)). Red boxes indicate the regions of interest determined by the MLLM, yellow box denotes the padding \( p \) for complete visual content. The CoT process enhances reasoning accuracy. In the segmentation stage, a multi-modal encoder integrates textual and visual information, resizing the image for detailed feature extraction. Finally, SAM refines the segmentation by highlighting the shell that acts as a protective covering for the snail.

MLLMs have been applied in vision-language reasoning tasks, including LISA (Lai et al., 2024), Kosmos-2 (Peng et al., 2024), and Groundhog (Zhang et al., 2024b), where models generate region proposals or segmentation masks. However, these methods either require extensive training or rely on complex architectures with large parameter sizes. Our approach differs by introducing a lightweight reasoning-driven framework that exploits MLLMs’ innate reasoning and localization capabilities without additional training.

2.3 Visual Prompting

Inspired by prompt engineering in Natural Language Processing (NLP), visual prompting (Wu et al., 2024a; Yang et al., 2023d) modifies the input space using human-perceivable markers such as bounding boxes (Lin et al., 2024), numbers (Yang et al., 2023a), or shapes (Shtedritski et al., 2023). These cues help MLLMs focus on key image regions without altering model parameters, mitigating issues like visual hallucination (Bai et al., 2024) and language bias (Qu et al., 2024).

Visual prompting has shown effectiveness in fine-grained visual attention for tasks such as referring expressions (Shtedritski et al., 2023), visual question-answering (VQA) (Zhou et al., 2024) and video localization (Wu et al., 2024b). Det-ToolChain (Wu et al., 2024a) integrates CoT reasoning (Wei et al., 2022) with visual prompts to enhance object detection using MLLMs like GPT-4V and Gemini.

To our knowledge, no previous work has explored visual prompting for reasoning segmentation. Unlike prior approaches that focus on basic spatial attention, we integrate multi-modal chain-of-thought visual prompting to provide structured reasoning for segmentation, allowing models to generate interpretable region proposals.

2.4 Multi-modal Chain of Thought

Motivated by recent advances in LLMs, Multi-modal Chain-of-Thought (CoT) has become a prominent approach for enhancing MLLMs’ reasoning capabilities. A line of work (Zhang et al., 2023) extends CoT reasoning to Vision-Language tasks by introducing a two-stage framework that separates reasoning chain generation and answer inference. M3-CoT, introduced by Chen et al. (2024) as a comprehensive benchmark dataset, provides rich multi-step, multi-modal samples of mathematical and scientific problems. Additionally, Shao et al. (2024) proposes VisCoT, a multi-round interactive image understanding pipeline that mimics human localized focus to extract key information, improving performance on VQA and document comprehension tasks. Literature Chen et al. (2025) Comprehensively surveys long CoT applications of MLLMs in areas such as mathematics, science and commonsense puzzles. However, application of Multi-modal CoTs of reasoning segmentation tasks remain underexplored.

2.5 Text-Prompted Segmentation

Text-prompted segmentation (referring segmentation) involves extracting object segmentation masks based on natural language queries. Transformer-based models like SAM (Kirillov et al., 2023) leverage CLIP (Radford et al., 2021) embeddings for segmentation, while SAM-CLIP (Wang et al., 2023) fuses the visual backbones of SAM and CLIP. Grounded-SAM (Ren
et al., 2024) employs Grounding-DINO (Liu et al., 2023c) to detect objects before refining segmentation with SAM.

However, these models lack a structured knowledge summarization and reasoning process between text embedding and segmentation. Moreover, they are trained on short, explicit queries, limiting their ability to handle abstract and implicit reasoning. In contrast, we incorporate multi-modal reasoning within segmentation models, enabling superior performance on complex, implicit queries. As shown in Figure 1, by integrating MLLMs for reasoning and rough localization region proposals while retaining similar segmentation models, we achieve superior performance compared to non-reasoning text-prompted segmentation.

3 Method

3.1 Overview
We aim to develop an efficient, modular reasoning segmentation framework that integrates MLLM’s inherent cognitive reasoning capabilities with structured visual segmentation while minimizing the need for fine-tuning. To achieve this goal, we introduce RSVP, a two-stage framework consisting:
1) Multi-modal Chain-of-Thought (CoT) Visual Prompting, which enables an MLLM to reason about object attributes and generate interpretable region proposals.
2) Vision-Language Segmentation Module (VLSM), which refines these proposals into precise segmentation masks.

Unlike existing approaches that rely on extensive fine-tuning (Lai et al., 2024), RSVP exploits MLLMs’ intrinsic reasoning and localization capabilities through structured CoT visual prompting. This enables zero-shot segmentation while improving interpretability.

3.2 Multi-modal Chain-of-Thought Visual Prompting

Region Proposal Initialization. To generate structured region proposals, we simplify reasoning segmentation into a text-guided localization task, where an MLLM identifies the object of interest and its approximate location. Unlike prior works that require model fine-tuning, our method directly leverages MLLMs’ reasoning and rough localization abilities via visual prompting.

Inspired by Set-of-Mark (SoM) prompting (Yang et al., 2023a), we introduce a region-aware visual prompt to explicitly structure spatial queries. As illustrated in Figure 3, we preprocess the input image \( I \) by dividing it into M horizontal and N vertical sections, assigning unique region IDs. The reasoning process explains why “drum” is the object that matches the query: “boosts team morale”. Region IDs for localization are determined as (\( id_{s_v} \): [4, 5, 6], \( id_{s_h} \): [5, 6, 7, 8]). The rough region proposal highlights the drum’s location in the image as a crucial target. The determined region and inferred object name are passed to the second-stage segmentation model to produce the precise mask, detailed prompt inputs are shown at Appendix D. The structured prompt consists of:
(1) A reasoning-based textual query guiding the MLLM to infer object attributes.
(2) A region-aware localization task, where the MLLM assigns region IDs to detected objects.
(3) A CoT-based reasoning step, which provides explicit rationale behind the object’s presence and location.

The MLLM outputs structured proposals containing: (1) Object name and attributes: A textual description (e.g., “a red drum”). (2) Region localization: A set of horizontal and vertical region IDs. (3) Reasoning rationale: Justification for why the object belongs to the detected region.

Region Proposal Formulation. Given the MLLM-predicted horizontal (\( id_{h} \)) and vertical (\( id_{v} \)) region indices, we construct the bounding horizontal / vertical regions, \( R_h \) and \( R_v \):

\[
R_h = [\max(\min(id_{h}), 1) - p_y, \min(\max(id_{h}), M) + p_y]
\]
\[
R_v = [\max(\min(id_{v}), 1) - p_x, \min(\max(id_{v}), N) + p_x]
\]

where \( p_x, p_y \) are padding terms ensuring the bounding box does not truncate object edges.

![Illustration of the CoT processing strategy in action for a query about a dragon boat race.](page_1012_120_393_312.png)

Figure 3: Illustration of the CoT processing strategy in action for a query about a dragon boat race.
3.3 Multi-modal Chain-of-Thought Reasoning
While MLLMs excel at multimodal reasoning, they lack structured segmentation capabilities. To address this gap, we design a CoT-based visual prompt that guides MLLMs through a step-wise progressive reasoning process, ensuring accurate object identification and localization.

CoT Reasoning Process. Given an input image \( I \) and textual query \( T_q \), the MLLM generates a structured reasoning output:

\[
T_a = \text{MLLM}(I, T_q)
\]

Unlike single-step predictions, we structure the query into explicit step-wise CoT prompts, which require the MLLM to:
(1) Infer the most probable object class (e.g., “drum”). (2) Identify key attributes (e.g., “red, cylindrical”). (3) Locate objects within image regions using visual referencing with region IDs. (4) Provide rationale explaining how and why the inference was made.

This explicit reasoning improves interpretability while ensuring robust region proposals. If the model found the object of interest is absent from the image, it returns an empty region list as well as its rationale.

3.4 Vision-Language Segmentation Module
Once the object description and approximate location are obtained, the task reduces to referring segmentation, where we generate a fine-grained mask. To achieve this, we design a Vision-Language Segmentation Module (VLSM) that integrates textual descriptions and localized image crops.

Region Cropping. We extract a cropped region \( I' \) centered around the bounding box:

\[
H' = \frac{H}{M} \times (\text{end}_v - \text{start}_v + 1) + 2p_y
\]
\[
W' = \frac{W}{N} \times (\text{end}_h - \text{start}_h + 1) + 2p_x
\]

Where \( \frac{H}{M} / \frac{W}{N} \) is the height / width of each vertical region. (\( \text{end}_v - \text{start}_v + 1 \)) and (\( \text{end}_h - \text{start}_h + 1 \)) calculate the number of units included in the cropped sections, \( p_x \) and \( p_y \) refers to horizontal and vertical padding.

Multi-modal Feature Encoding. We build upon the SAM framework (Kirillov et al., 2023) by creating a module for integrated visual-language feature extraction. Since Segment Anything Model (SAM) (Kirillov et al., 2023) lacks native text support, we incorporate BEiT-3 (Wang et al., 2022) as a joint vision-language encoder.

First, the cropped image \( I' \) is resized to \( I_r \in \mathbb{R}^{224 \times 224 \times 3} \). The target description \( T_n \), tokenized with XLMRobertaTokenizer (Rachmadi et al., 2023), is integrated into the BEiT-3 model. Image and text features are processed independently, producing \( \mathbf{F}_{\text{image}} \in \mathbb{R}^{(N+1) \times D} \) and \( \mathbf{F}_{\text{text}} \in \mathbb{R}^{L \times D} \). These embeddings are combined via cross-attention in Transformer layers, yielding a unified token for cross-modal representation. It is then processed through a projector with two linear layers and ReLU, mapping it to a 256-dimensional space, which is sent to SAM’s prompt encoder and segmentation decoder, which predicts the pixel-wise segmentation mask.

3.5 Inference Pipeline and Computational Efficiency
During inference, RSVP operates in two steps. MLLM first generates structured region proposals (CoT-based Region Proposal Generation). Identified regions are then processed by VLSM to generate the final mask, corresponding to the Vision-Language Segmentation step. We compared the inference latency on the ReasonSeg-Test split (comprising 754 images) using our pipeline (7B models) and LISA. The detailed results are as follows:

<table>
  <tr>
    <th>Model</th>
    <th>Stage</th>
    <th>Time (s)</th>
    <th>Model Size</th>
  </tr>
  <tr>
    <td rowspan="3">RSVP-LLaVA</td>
    <td>First-stage</td>
    <td>9.20</td>
    <td rowspan="3">7B</td>
  </tr>
  <tr>
    <td>Second-stage</td>
    <td>0.77</td>
  </tr>
  <tr>
    <td>Total</td>
    <td>9.97</td>
  </tr>
  <tr>
    <td rowspan="3">RSVP-Qwen</td>
    <td>First-stage</td>
    <td>9.35</td>
    <td rowspan="3">7B</td>
  </tr>
  <tr>
    <td>Second-stage</td>
    <td>0.72</td>
  </tr>
  <tr>
    <td>Total</td>
    <td>10.07</td>
  </tr>
  <tr>
    <td rowspan="3">RSVP-Qwen</td>
    <td>First-stage</td>
    <td>7.36</td>
    <td rowspan="3">2B</td>
  </tr>
  <tr>
    <td>Second-stage</td>
    <td>0.73</td>
  </tr>
  <tr>
    <td>Total</td>
    <td>8.09</td>
  </tr>
  <tr>
    <td>LISA-13B</td>
    <td>Total</td>
    <td>15.88</td>
    <td>13B</td>
  </tr>
  <tr>
    <td>LISA-7B</td>
    <td>Total</td>
    <td>9.41</td>
    <td>7B</td>
  </tr>
</table>

Table 1: Average inference time (seconds per image) on the ReasonSeg-Test split. RSVP’s latency is broken down into two stages. LISA is a single-stage model, only total time is reported.

Inference time was measured after loading each model onto a single A100 40GB GPU. RSVP-7B exhibited a worst-case latency of 10.07 seconds per image, compared to 9.41 seconds for LISA-7B and 15.88 seconds for LISA-13B. This represents only a ~7% increase over LISA-7B, a marginal overhead considering RSVP’s added capabilities.
The primary computational cost originates from the first-stage MLLM inference. In real-world applications, this overhead can be further reduced through optimization techniques such as quantization or deployment frameworks such as vLLM (Kwon et al., 2023). Importantly, unlike end-to-end fine-tuning methods, RSVP requires no additional training, making it computationally efficient for reasoning-based segmentation while maintaining inference speed comparable to existing approaches.

4 Experiments

We evaluate RSVP through quantitative and qualitative analysis to assess its performance in reasoning segmentation, open vocabulary segmentation, and referring segmentation. The section is organized as follows. Experiment Setup is described in Sec. 4.1, Details of the implementation are explained in Sec. 4.2, Sec. 4.3 presents evaluation results. In addition, the ablation study is conducted concerning various design choices in Sec. 4.4.

4.1 Evaluation Setup

We apply the following datasets and evaluation metrics for the following experiments:

Datasets. For reasoning segmentation, we conduct our experiments on the ReasonSeg validation and testing dataset, which is proposed by LISA (Lai et al., 2024). Specifically, the ReasonSeg-Val dataset consists of 200 samples, while between different splits of the ReasonSeg-Test dataset (around 770 samples), “Short Query” refers to reasoning segmentation annotations with a single short sentence, while in the “Long Query” split, each referring annotation contains multiple long sentences. To demonstrate RSVP’s generality in the zero-shot open-world segmentation task, we evaluate our model’s performance on the Segmentation in the Wild (SegInW) zero-shot benchmark (Zou et al., 2022), which comprises 25 zero-shot in-the-wild segmentation datasets. refCOCOg (Mao et al., 2015) is utilized for testing the referring expression segmentation task on our second-stage model; Visual-Language Segmentation Module (VLSM) to demonstrate its capability in this task.

Metrics. Following previous works (Lai et al., 2024; Yang et al., 2023b), we apply Generalized IoU (gIoU) and Cumulative IoU (cIoU) as the performance evaluation metric in referring segmentation, as well as the reasoning segmentation task. Generalized IoU (gIoU) is computed as the average IoU over all images in the test set:

\[
\text{gIoU} = \frac{1}{N} \sum_{i=1}^{N} \text{IoU}^{(i)} = \frac{1}{N} \sum_{i=1}^{N} \frac{I^{(i)}}{U^{(i)}}
\]

while Cumulative IoU (cIoU) is defined as the ratio of the cumulative intersection over the cumulative union across all images:

\[
\text{cIoU} = \frac{\sum_{i=1}^{N} I^{(i)}}{\sum_{i=1}^{N} U^{(i)}}
\]

Where: \( N \) is the number of images in the test set, \( B_{\text{pred}}^{(i)} \) and \( B_{\text{gt}}^{(i)} \) are the predicted and ground truth bounding boxes for the \( i \)-th image, \( I^{(i)} \) is their intersection area, and \( U^{(i)} \) is their union area.

For open-world segmentation, we applied mean average precision (mAP) as the evaluation metric following the convention of the SegInW benchmark and previous works.

4.2 Implementation Details

Our two-stage model combines a zero-shot prompted MLLM for reasoning and a referring segmentation model for mask generation.

First-stage MLLMs. For evaluating commercial MLLMs, we employ GPT-4o and Gemini-Flash\(^1\). For open-source MLLMs, we apply LLaVA-NeXT and Qwen2-V1\(^2\).

Second-stage Segmentation Model. The segmentation model is trained on refCOCO, initialized with BEiT-3 and SAM weights. The model training uses LoRA and DeepSpeed (Rasley et al., 2020), with AdamW optimizer (Loshchilov and Hutter, 2017) with a learning rate of 1e-4 alongside Dice Loss and Binary Cross-Entropy (BCE) Loss. Training involves around 16,000 epochs with a total batch size of 256.

4.3 Main Results

Reasoning Segmentation. RSVP is extensively tested on ReasonSeg’s validation and test set to demonstrate its effectiveness. Table 2 compares RSVP with conventional zero-shot methods, LISA models which are further fine-tuned on ReasonSeg’s training split, and LISA models which are not further trained on any reasoning segmentation dataset.

\(^1\)GPT-4o version: gpt-4o-2024-08-06, Gemini-Flash version: gemini-1.5-flash-002.
\(^2\)Obtained 7B version model weights from their Hugging-Face pages.
<table>
  <tr>
    <th rowspan="2">Method</th>
    <th rowspan="2">MLLM</th>
    <th colspan="4">Val</th>
    <th colspan="4">Test</th>
  </tr>
  <tr>
    <th colspan="2">Overall</th>
    <th colspan="2">Short Query</th>
    <th colspan="2">Long Query</th>
    <th colspan="2">Overall</th>
  </tr>
  <tr>
    <th></th>
    <th></th>
    <th>gIOU</th>
    <th>cIOU</th>
    <th>gIOU</th>
    <th>cIOU</th>
    <th>gIOU</th>
    <th>cIOU</th>
    <th>gIOU</th>
    <th>cIOU</th>
  </tr>
  <tr>
    <td>OVSeg(Wang et al., 2024)</td>
    <td>-</td>
    <td>28.5</td>
    <td>18.6</td>
    <td>18.0</td>
    <td>15.5</td>
    <td>28.7</td>
    <td>22.5</td>
    <td>26.1</td>
    <td>20.8</td>
  </tr>
  <tr>
    <td>SEEM(Zou et al., 2024)</td>
    <td>-</td>
    <td>25.5</td>
    <td>21.2</td>
    <td>20.1</td>
    <td>11.5</td>
    <td>25.6</td>
    <td>20.8</td>
    <td>24.3</td>
    <td>18.7</td>
  </tr>
  <tr>
    <td>Grounded-SAM(Ren et al., 2024)</td>
    <td>-</td>
    <td>26.0</td>
    <td>14.5</td>
    <td>17.8</td>
    <td>10.8</td>
    <td>22.4</td>
    <td>18.6</td>
    <td>21.3</td>
    <td>16.4</td>
  </tr>
  <tr>
    <td>LLaVA-7B + OVSeg</td>
    <td>LLaVA-7B</td>
    <td>38.2</td>
    <td>23.5</td>
    <td>24.2</td>
    <td>18.7</td>
    <td>44.6</td>
    <td>37.1</td>
    <td>39.7</td>
    <td>31.8</td>
  </tr>
  <tr>
    <td>LLaVA-7B + CoT + OVSeg</td>
    <td>LLaVA-7B</td>
    <td>42.1</td>
    <td>26.6</td>
    <td>27.5</td>
    <td>19.6</td>
    <td>44.6</td>
    <td>40.2</td>
    <td>40.4</td>
    <td>34.0</td>
  </tr>
  <tr>
    <td>LISA(Lai et al., 2024)-7B</td>
    <td>LLaVA-7B</td>
    <td>53.6</td>
    <td>52.3</td>
    <td>47.1</td>
    <td>48.5</td>
    <td>49.2</td>
    <td>48.9</td>
    <td>48.7</td>
    <td>48.8</td>
  </tr>
  <tr>
    <td>LISA(Lai et al., 2024)-13B</td>
    <td>LLaVA-13B</td>
    <td>57.7</td>
    <td>60.3</td>
    <td>50.8</td>
    <td>50.0</td>
    <td>54.7</td>
    <td>50.9</td>
    <td>53.8</td>
    <td>50.8</td>
  </tr>
  <tr>
    <td>LISA(Lai et al., 2024)-7B (ft)</td>
    <td>LLaVA-7B</td>
    <td>61.3</td>
    <td>62.9</td>
    <td>48.3</td>
    <td>46.3</td>
    <td>57.9</td>
    <td>59.7</td>
    <td>55.6</td>
    <td>56.9</td>
  </tr>
  <tr>
    <td>LISA++(Yang et al., 2023b)-7B (ft)</td>
    <td>LLaVA-7B</td>
    <td>64.2</td>
    <td>68.1</td>
    <td>49.6</td>
    <td>51.1</td>
    <td>59.3</td>
    <td>61.7</td>
    <td>57.0</td>
    <td>59.5</td>
  </tr>
  <tr>
    <td>LISA(Lai et al., 2024)-13B (ft)</td>
    <td>LLaVA-13B</td>
    <td>65.0</td>
    <td>72.9</td>
    <td>55.4</td>
    <td>50.6</td>
    <td>63.2</td>
    <td>65.3</td>
    <td>61.3</td>
    <td>62.2</td>
  </tr>
  <tr>
    <td>RSVP-LLaVA</td>
    <td>LLaVA-7B</td>
    <td>59.2</td>
    <td>56.7</td>
    <td>47.9</td>
    <td>42.0</td>
    <td>58.4</td>
    <td>53.0</td>
    <td>55.9</td>
    <td>50.7</td>
  </tr>
  <tr>
    <td>RSVP-Qwen</td>
    <td>Qwen2-VL-7B</td>
    <td>58.6</td>
    <td>48.5</td>
    <td>48.5</td>
    <td>44.3</td>
    <td>57.1</td>
    <td>53.8</td>
    <td>56.6</td>
    <td>51.6</td>
  </tr>
  <tr>
    <td>RSVP-Gemini</td>
    <td>Gemini-Flash</td>
    <td>56.9</td>
    <td>49.2</td>
    <td>47.3</td>
    <td>40.2</td>
    <td>60.2</td>
    <td>65.6</td>
    <td>57.1</td>
    <td>59.2</td>
  </tr>
  <tr>
    <td>RSVP-GPT</td>
    <td>GPT-4o</td>
    <td>64.7</td>
    <td>63.1</td>
    <td>55.4</td>
    <td>50.4</td>
    <td>61.9</td>
    <td>62.5</td>
    <td>60.3</td>
    <td>60.0</td>
  </tr>
</table>

Table 2: Reasoning segmentation results of our model and previous related works on ReasonSeg(Lai et al., 2024). “ft” denotes that the model was fine-tuned on ReasonSeg’s training split, others are tested under zero-shot. “CoT” means utilizing region proposal Chain-of-Thought strategy. Results in bold are the best metric among zero-shot models. Columns with gray backgrounds indicate training-free methods, while grayed-out columns are LISA models fine-tuned on ReasonSeg Training split.

The first 4 rows represent traditional zero-shot methods, which perform significantly worse than zero-shot or fine-tuned LISA models, and our approaches. It is worth mentioning that applying our RSVP framework to weaker models (e.g., LLaVA-7B + Multi-Modal CoT Visual Prompt + OVSeg) notably improves segmentation results.

Despite using models with comparable or larger parameter sizes, both 7B and 13B versions of LISA that are not fine-tuned on ReasonSeg-Training split underperform against RSVP, demonstrating the effectiveness of reasoning-driven segmentation. Although LISA models can be further fine-tuned on ReasonSeg to achieve competitive performance, they require substantial computational resources and retraining whenever new foundation models emerge. In contrast, RSVP achieves state-of-the-art gIOU and cIOU in zero-shot settings, outperforming fine-tuned models without additional training.

<table>
  <tr>
    <th>Method</th>
    <th colspan="2">refCOCOg</th>
  </tr>
  <tr>
    <th></th>
    <th>val(cIOU)</th>
    <th>test(cIOU)</th>
  </tr>
  <tr>
    <td>MCN (Luo et al., 2020)</td>
    <td>49.2</td>
    <td>49.4</td>
  </tr>
  <tr>
    <td>VLT (Ding et al., 2021)</td>
    <td>55.0</td>
    <td>57.7</td>
  </tr>
  <tr>
    <td>CRIS (Wang et al., 2021)</td>
    <td>59.9</td>
    <td>60.4</td>
  </tr>
  <tr>
    <td>LAVT (Yang et al., 2021)</td>
    <td>61.2</td>
    <td>62.1</td>
  </tr>
  <tr>
    <td>ReLA (Liu et al., 2023a)</td>
    <td>65.0</td>
    <td>66.0</td>
  </tr>
  <tr>
    <td>X-Decoder (Zou et al., 2022)</td>
    <td>64.6</td>
    <td>-</td>
  </tr>
  <tr>
    <td>SEEM (Zou et al., 2024)</td>
    <td>65.7</td>
    <td>-</td>
  </tr>
  <tr>
    <td>LISA (Lai et al., 2024)</td>
    <td>66.4</td>
    <td>68.5</td>
  </tr>
  <tr>
    <td>RSVP (VLSM only)</td>
    <td><b>65.5</b></td>
    <td><b>66.4</b></td>
  </tr>
</table>

Table 3: cIOU metric report on refCOCOg dataset’s validation and test split. “-”: data is not reported by original work. Our result: highlighted with gray background and bold text.

Referring Segmentation. We further evaluate VLSM on refCOCOg (Table 3). RSVP achieves competitive results and demonstrates segmentation model remains effective in standard referring segmentation tasks.

Open-World Segmentation.

Table 4 shows the result in SegInW. Although the reasoning part of the Segmentation in the Wild task is relatively straightforward, the target categories are more diverse, allowing us to validate the generalization capabilities of RSVP. Our method achieved a mean average precision (mAP) of 49.7 across 25 categories, demonstrating the effectiveness of our proposed approach.

4.4 Ablation Study

We conduct ablation studies to analyze design choices in RSVP’s CoT reasoning process.

First-stage Multimodal Information Distillation. Table 2 compares different MLLMs. GPT-4o outperforms LLaVA by +6.4 cIOU, +5.5 gIOU, and Qwen2-VL by +14.6 cIOU, +6.1 gIOU, showing that stronger reasoning models yield better segmentation performance. However, Gemini-Flash does not outperform LLaVA, indicating that applying our multi-modal chain-of-thought region proposal with MLLMs which are stronger in reasoning, comprehending, and distilling information conveyed in multi-modal inputs is crucial for achieving powerful performance in reasoning segmentation tasks.

<table>
  <tr>
    <th>Distillation Modality</th>
    <th>GPT-4o</th>
    <th>LLaVA</th>
  </tr>
  <tr>
    <td>Visual Only</td>
    <td>33.7 (−29.6)</td>
    <td>31.2 (−25.5)</td>
  </tr>
  <tr>
    <td>Text Only</td>
    <td>56.6 (−6.5)</td>
    <td>53.5 (−3.2)</td>
  </tr>
  <tr>
    <td>Both Modalities</td>
    <td><b>63.1</b></td>
    <td><b>56.7</b></td>
  </tr>
</table>

Table 5: Comparsion of our model’s cIOU metric on ReasonSeg-Val split utilizing different Multi-Modal Information Distillation Approaches. Values in brackets indicate the amount of cIOU decrease of this approach compared to the "Both Modalities" approach.
<table>
  <tr>
    <th>Method</th>
    <th colspan="2">mean SegIoU</th>
    <th colspan="18">Objects</th>
  </tr>
  <tr>
    <th></th>
    <th>Elephants</th>
    <th>Heart-Metal</th>
    <th>Watermelon</th>
    <th>Wheat-Buns</th>
    <th>Household-Items</th>
    <th>Strawberry</th>
    <th>Fruits</th>
    <th>Neatly-Squeezed</th>
    <th>Head</th>
    <th>Gazelle</th>
    <th>Crickets</th>
    <th>Rail</th>
    <th>Appliance-Parts</th>
    <th>Brain-Tumor</th>
    <th>Poles</th>
    <th>Electric-Shaver</th>
    <th>Birds</th>
    <th>Tools</th>
    <th>Salmon-Fillet</th>
    <th>Purples</th>
    <th>Tablets</th>
    <th>Planes</th>
    <th>Cows</th>
    <th>Ginger-Garlic</th>
  </tr>
  <tr>
    <td>X-Decoder-T (Zou et al., 2022)</td>
    <td>22.6</td>
    <td>16.6</td>
    <td>22.4</td>
    <td>16.2</td>
    <td>5.5</td>
    <td>50.6</td>
    <td>41.6</td>
    <td>66.5</td>
    <td>62.1</td>
    <td>0.6</td>
    <td>28.7</td>
    <td>12.0</td>
    <td>0.7</td>
    <td>10.5</td>
    <td>1.1</td>
    <td>3.6</td>
    <td>1.2</td>
    <td>19.0</td>
    <td>9.5</td>
    <td>19.3</td>
    <td>15.0</td>
    <td>48.9</td>
    <td>15.2</td>
    <td>29.9</td>
    <td>12.0</td>
    <td>7.9</td>
  </tr>
  <tr>
    <td>X-Decoder-L-IN22K</td>
    <td>26.6</td>
    <td>63.9</td>
    <td>20.3</td>
    <td>13.5</td>
    <td>4.9</td>
    <td>50.5</td>
    <td>74.4</td>
    <td>79.1</td>
    <td>58.8</td>
    <td>0.0</td>
    <td>24.3</td>
    <td>3.5</td>
    <td>1.3</td>
    <td>12.3</td>
    <td>0.5</td>
    <td>13.4</td>
    <td>18.8</td>
    <td>43.2</td>
    <td>14.6</td>
    <td>20.1</td>
    <td>12.3</td>
    <td>57.3</td>
    <td>6.9</td>
    <td>43.4</td>
    <td>12.3</td>
    <td>15.6</td>
  </tr>
  <tr>
    <td>X-Decoder-B</td>
    <td>27.7</td>
    <td>68.0</td>
    <td>18.5</td>
    <td>13.0</td>
    <td>6.7</td>
    <td>51.7</td>
    <td>81.6</td>
    <td>76.7</td>
    <td>53.1</td>
    <td>20.6</td>
    <td>30.2</td>
    <td>13.6</td>
    <td>0.8</td>
    <td>13.0</td>
    <td>0.3</td>
    <td>5.6</td>
    <td>4.2</td>
    <td>45.9</td>
    <td>13.0</td>
    <td>27.3</td>
    <td>18.2</td>
    <td>55.4</td>
    <td>8.0</td>
    <td>8.9</td>
    <td>36.8</td>
    <td>19.4</td>
  </tr>
  <tr>
    <td>X-Decoder</td>
    <td>32.2</td>
    <td>66.0</td>
    <td>42.1</td>
    <td>13.8</td>
    <td>7.3</td>
    <td>53.0</td>
    <td>67.1</td>
    <td>79.2</td>
    <td>68.4</td>
    <td>75.9</td>
    <td>3.0</td>
    <td>8.6</td>
    <td>2.3</td>
    <td>13.1</td>
    <td>2.2</td>
    <td>20.1</td>
    <td>7.4</td>
    <td>42.1</td>
    <td>9.9</td>
    <td>22.0</td>
    <td>19.0</td>
    <td>59.0</td>
    <td>15.6</td>
    <td>9.9</td>
    <td>11.6</td>
  </tr>
  <tr>
    <td>X-Decoder (Xu et al., 2023)</td>
    <td>32.1</td>
    <td>66.0</td>
    <td>42.1</td>
    <td>13.8</td>
    <td>7.3</td>
    <td>53.0</td>
    <td>67.1</td>
    <td>79.2</td>
    <td>68.4</td>
    <td>75.9</td>
    <td>3.0</td>
    <td>8.6</td>
    <td>2.3</td>
    <td>13.1</td>
    <td>2.2</td>
    <td>20.1</td>
    <td>7.4</td>
    <td>42.1</td>
    <td>9.9</td>
    <td>22.0</td>
    <td>19.0</td>
    <td>59.0</td>
    <td>15.6</td>
    <td>9.9</td>
    <td>11.6</td>
  </tr>
  <tr>
    <td>UNINET-H (Yan et al., 2023)</td>
    <td>42.1</td>
    <td>72.1</td>
    <td>57.0</td>
    <td>56.3</td>
    <td>0.0</td>
    <td>54.0</td>
    <td>80.7</td>
    <td>81.1</td>
    <td>84.1</td>
    <td>93.7</td>
    <td>16.9</td>
    <td>75.2</td>
    <td>0.0</td>
    <td>15.1</td>
    <td>2.6</td>
    <td>13.4</td>
    <td>71.2</td>
    <td>46.1</td>
    <td>10.8</td>
    <td>44.4</td>
    <td>64.6</td>
    <td>61.6</td>
    <td>21.0</td>
    <td>6.1</td>
    <td>52.7</td>
    <td>23.7</td>
  </tr>
  <tr>
    <td>Grounded-SAM (B+H)</td>
    <td>48.7</td>
    <td>77.9</td>
    <td>81.2</td>
    <td>64.2</td>
    <td>8.4</td>
    <td>60.1</td>
    <td>83.5</td>
    <td>82.3</td>
    <td>71.3</td>
    <td>70.0</td>
    <td>24.0</td>
    <td>84.5</td>
    <td>8.7</td>
    <td>37.2</td>
    <td>11.9</td>
    <td>23.3</td>
    <td>71.7</td>
    <td>65.4</td>
    <td>20.4</td>
    <td>30.9</td>
    <td>50.1</td>
    <td>29.8</td>
    <td>35.4</td>
    <td>47.5</td>
    <td>45.8</td>
  </tr>
  <tr>
    <td>Grounded-SAM (H+H)</td>
    <td>46.0</td>
    <td>78.6</td>
    <td>75.2</td>
    <td>61.5</td>
    <td>7.2</td>
    <td>35.0</td>
    <td>82.5</td>
    <td>86.9</td>
    <td>70.9</td>
    <td>90.7</td>
    <td>22.8</td>
    <td>84.6</td>
    <td>7.2</td>
    <td>38.4</td>
    <td>10.2</td>
    <td>17.4</td>
    <td>59.7</td>
    <td>43.7</td>
    <td>26.3</td>
    <td>22.4</td>
    <td>27.1</td>
    <td>63.2</td>
    <td>38.6</td>
    <td>3.4</td>
    <td>49.4</td>
    <td>40.0</td>
  </tr>
  <tr>
    <td>RSVP</td>
    <td><b>49.7</b></td>
    <td><b>84.7</b></td>
    <td><b>61.6</b></td>
    <td><b>69.1</b></td>
    <td><b>42.3</b></td>
    <td><b>90.7</b></td>
    <td><b>81.6</b></td>
    <td><b>84.3</b></td>
    <td><b>79.6</b></td>
    <td><b>90.2</b></td>
    <td><b>34.7</b></td>
    <td><b>82.3</b></td>
    <td><b>34.1</b></td>
    <td><b>61.2</b></td>
    <td><b>13.4</b></td>
    <td><b>52.4</b></td>
    <td><b>75.6</b></td>
    <td><b>83.8</b></td>
    <td><b>12.6</b></td>
    <td><b>41.1</b></td>
    <td><b>76.4</b></td>
    <td><b>91.7</b></td>
    <td><b>78.6</b></td>
    <td><b>45.7</b></td>
    <td><b>45.6</b></td>
  </tr>
</table>

Table 4: mAP reported on SegInW Open-world segmentation dataset. Metrics in bold represent the top 1 result for each subtask, our result is highlighted with the gray background.

Multimodal Information Distillation Strategy.
We explore different approaches to distilling multi-modal input using MLLMs for reasoning segmentation tasks. Specifically, we adopt different methodologies on our model with two representative MLLMs as the first-stage model, GPT-4o and LLaVA: (a) The MLLM comprehends and distills the input’s textual modality only. (b) The MLLM comprehends and distills the input’s visual modality only. (c) The MLLM comprehends and distills both of the input’s modalities.

Results in Table 5 show that excluding textual reasoning leads to a 29.6 cIoU drop, while omitting visual information decreases performance by 6.5 cIoU, while distilling both modalities yields the most superior performance in reasoning segmentation, confirming that reasoning about object attributes as well as generating rough object grounding proposal is crucial for reasoning segmentation.

<table>
  <tr>
    <th>VLSM Combination</th>
    <th>gIoU (%)</th>
    <th>cIoU (%)</th>
  </tr>
  <tr>
    <td>RSVP-OVSeg</td>
    <td>43.5</td>
    <td>37.2</td>
  </tr>
  <tr>
    <td>RSVP-LLaVA</td>
    <td>55.9</td>
    <td>50.7</td>
  </tr>
  <tr>
    <td>RSVP-GPT</td>
    <td><b>60.3</b></td>
    <td>60.0</td>
  </tr>
  <tr>
    <td>RSVP-GPT (ft)</td>
    <td>57.5</td>
    <td><b>61.6</b></td>
  </tr>
</table>

Table 6: Ablation study on the VLSM Module in RSVP, evaluated on the ReasonSeg Test split. Best result rows are highlighted in gray.

Importance of Reasoning and Segmentation Modules. We examined the importance of the modules of both stages in the performance of the reasoning segmentation task. Table 6 demonstrates the ablation result, where RSVP-GPT (ft) refer to finetuned VLSM using GPT-4o’s reasoning on ReasonSeg-Train, RSVP-GPT and RSVP-LLaVA are zero-shot models trained on refCOCOg. RSVP-LLaVA uses LLaVA-7B’s reasoning for inference and RSVP-GPT (ft) and RSVP-GPT use GPT-4o’s reasoning on ReasonSeg-Test. Replacing VLSM with OVSeg reduces cIoU by -22.8 on ReasonSeg-Test, aligning with OVSeg’s weaker referring segmentation capability. Further, RSVP with weaker reasoning models (LLaVA vs. GPT-4o) underperforms despite an identical segmentation module, emphasizing that a strong MLLM is essential for strong, top-tier performance.

<table>
  <tr>
    <th>Visual Modality Distill Strategy</th>
    <th>LLaVA</th>
    <th>GPT-4o</th>
  </tr>
  <tr>
    <td>9 × 9 Grid</td>
    <td>53.1</td>
    <td>56.8</td>
  </tr>
  <tr>
    <td>5 × 5 Split</td>
    <td>54.3</td>
    <td>59.6</td>
  </tr>
  <tr>
    <td>9 × 9 Split</td>
    <td><b>56.7</b></td>
    <td><b>63.1</b></td>
  </tr>
  <tr>
    <td>13 × 13 Split</td>
    <td>52.3</td>
    <td>57.6</td>
  </tr>
  <tr>
    <td>No Visual Prompt</td>
    <td>53.5</td>
    <td>56.6</td>
  </tr>
</table>

Table 7: Visual Modality Distillation Strategy. Grid: Grid visual prompt. Split: Region-aware visual prompting with varying densities (5 × 5, 9 × 9, 13 × 13 regions). Best result is highlighted in gray background and bold text.

Visual Modality Distillation Strategy. We explore two types of visual prompting strategies to assist in visual modality distillation: (A) Grid-based Visual Prompt. (B) Region-aware Visual Prompt. For (B), we further experimented with three densities: (a) 5 horizontal and vertical regions, (b) 9 horizontal and vertical regions, and (c) 13 horizontal and vertical regions. The tests were carried out on two representative models: open-source LLaVA and close-source GPT4o. Neither of them is trained in comprehending our region-aware horizontal/vertical separation visual prompts or grid visual prompts. Results in Table 7 shows:

(1) With the appropriate density, 9 × 9 being set, region-aware visual prompting led to notable improvements in cIoU, which is +3.2 cIoU for LLaVA and +6.5 cIoU for GPT-4o.

(2) Visual markers’ density may strongly impact models’ performance. Too many visual markers degrade performance (e.g., 13 × 13 results in up to -6.3 cIoU drop), which could be explained as over-detailed visual prompts exceed MLLMs’ capacity for recognizing fine details and impact the semantic information’s quality obtained by the model, while too few markers fail to filter irrelevant regions, lead-
<table>
  <tr>
    <th>Multi-Modal CoT Design</th>
    <th>RSVP-LLaVA</th>
    <th>RSVP-GPT</th>
  </tr>
  <tr>
    <td>Prompt (A)</td>
    <td>50.7</td>
    <td><b>60.0</b></td>
  </tr>
  <tr>
    <td>Prompt (B)</td>
    <td>45.5</td>
    <td>55.7</td>
  </tr>
</table>

Table 8: Multi-Modal Chain-of-Thought Prompt Design. (A): manual-crafted multi-step prompt. (B): simple CoT prompt. Best result rows are highlighted in gray, while the best results are highlighted in bold.

ing to segmentation accuracy declines.

Multi-Modal Chain-of-Thought Prompting Design. An ablation test on CoT prompt design is conducted to demonstrate the importance of manually designed Chain-of-Thought prompts. RSVP’s cloU results on ReasonSeg dataset’s test split across different models are examined with two distinct prompt designs:

(A) A hierarchical Chain-of-Thought prompt instructing the model to comprehend both intricate queries and provided images in a structuralized, step-by-step manner.

(B) A plain Chain-of-Thought prompt describing the overall problem, and provide plain instructions on generating structuralized answers.

As Table 8 shows, both models demonstrated stronger performance on the human-designed CoT prompt, while a suboptimal prompt has a noticeable negative impact on model’s reasoning segmentation performance. For both models, significant decreases in cloU are observed, which is -5.2 cloU for RSVP-LLaVA and -4.3 for RSVP-GPT. This finding suggests that carefully designed reasoning principles could activate models’ inherent inference capability and world knowledge, providing more robust and credible answers.

In conclusion, these results highlight the importance of balanced visual prompts for optimal multi-modal grounding.

5 Conclusion

In this work, we identified that the core challenge of reasoning segmentation is query comprehension and object localization. To address this, we introduced RSVP, a two-stage multi-modal reasoning framework that leverages MLLMs’ intrinsic reasoning and visual localization capabilities through Multi-Modal Chain-of-Thought Visual Prompting. By explicitly modeling the interaction between step-wise reasoning and segmentation, RSVP achieves state-of-the-art performance on zero-shot reasoning segmentation tasks.

Apart from performance improvements, we demonstrate the potential of integrating multi-modal reasoning with visual localization, providing new insights into bridging the gap between cognitive inference and fine-grained visual perception. We hope our work could lay the foundation for future research on reasoning segmentation, visual prompting, and broader integration of MLLMs in vision-language tasks.

6 Limitations

Although RSVP effectively integrates reasoning-driven object localization with structured segmentation, several challenges remain.

Dependence on MLLMs. RSVP relies on MLLMs for reasoning, making its performance sensitive to the capabilities of the underlying model. While stronger models like GPT-4o produce high-quality region proposals and nuanced query understanding, weaker or smaller MLLMs may struggle with complex reasoning and detailed visual interpretation. Future work could explore model distillation techniques or mixture-of-experts to enhance performance with lightweight models.

Visual Prompting Strategy. While our region-aware visual prompting effectively guides MLLMs for spatial reasoning, the optimal visual prompt design design remains an open question. Future directions include discovering diverse visual prompt designs or fine-tuned visual prompting mechanisms to further boost MLLMs’ spatial understanding and visual grounding capabilities.

Computational Overhead. RSVP eliminates the need for fine-tuning while multiple processing steps are still involved, leading to potential latency in real-time applications. Further work could investigate efficient model compression techniques to improve inference speed.

Data Bias and Generalization. While RSVP performs well on ReasonSeg and SegInW, its robustness on broader real-world datasets remains underexplored. The reliance on MLLM may introduce bias inherent from pretraining data, affecting the fairness and reliability of the model’s output. Future work could investigate domain adaptation techniques to improve generalization beyond curated benchmarks and explore MLLM guardrails to maintain the safety of model output.

Despite these limitations, RSVP establishes a scalable and interpretable framework for reasoning-based segmentation, offering new insights for future improvements in multimodal vision-language grounding.
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A Appendix

B Supplementary Ablation Experiment Results

In this section, we present more ablation experiments designed to validate our pipeline’s design choices as complementary material to ablation experiments in previous sections.

B.1 Padding Ablation Study.

To investigate the performance impact of padding size, we experimented on padding ratios set to 0%, 20% and 40% relative to the height/width of the cropped image region. Our main results reported are obtained with the 20% padding. With three diverse padding sizes, we report the following results on ReasonSeg-Test split:

<table>
  <tr>
    <th>Model</th>
    <th>Padding Size (%)</th>
    <th>gIoU</th>
    <th>cIoU</th>
  </tr>
  <tr>
    <td rowspan="3">RSVP-GPT</td>
    <td>0%</td>
    <td>58.8</td>
    <td>57.1</td>
  </tr>
  <tr>
    <td>20%</td>
    <td><b>60.3</b></td>
    <td><b>60.0</b></td>
  </tr>
  <tr>
    <td>40%</td>
    <td>60.0</td>
    <td>59.3</td>
  </tr>
  <tr>
    <td rowspan="3">RSVP-LLaVA</td>
    <td>0%</td>
    <td>52.6</td>
    <td>49.9</td>
  </tr>
  <tr>
    <td>20%</td>
    <td><b>55.9</b></td>
    <td><b>50.7</b></td>
  </tr>
  <tr>
    <td>40%</td>
    <td>53.7</td>
    <td>50.1</td>
  </tr>
</table>

Table 9: Ablation study on the effect of padding ratio in the visual prompt. Best-performing rows for each model are highlighted in gray, and best values are shown in bold.

As the above result reveals, the moderate-sized padding provides the most optimal balance between object boundaries preservation and minimizing irrelevant background inclusion.

B.2 CoT method Ablation Study.

We conducted an ablation experiment for investigating the impact of Multi-modal CoT design on our system’s performance. Our design is compared with the prompt design adapting (Wu et al., 2023), which force the MLLM to first summarize all perceptable visual cues in the given media, then perform analysis according to the summarization.

<table>
  <tr>
    <th>Model</th>
    <th>gIoU</th>
    <th>cIoU</th>
  </tr>
  <tr>
    <td>RSVP-LLaVA</td>
    <td><b>55.6</b></td>
    <td><b>50.9</b></td>
  </tr>
  <tr>
    <td>RSVP-LLaVA, with (Wu et al., 2023)</td>
    <td>52.5</td>
    <td>46.2</td>
  </tr>
  <tr>
    <td>RSVP-GPT</td>
    <td><b>60.3</b></td>
    <td><b>60.0</b></td>
  </tr>
  <tr>
    <td>RSVP-GPT, with (Wu et al., 2023)</td>
    <td>57.6</td>
    <td>56.4</td>
  </tr>
</table>

Table 10: Comparison of RSVP performance with/without integrating the CoT framework (Wu et al., 2023). Best-performing rows are highlighted in gray and best values are highlighted in bold.

Adopting CoT design from literature Wu et al. (2023) degrades performance, likely due to fundamental differences between our reasoning-and-localization task and the caption-based selection tasks focused by the literature, demonstrating the importance of choosing appropriate CoT frameworks for achieving satisfiable reasoning segmentation performance. Prompt is attached in Figure 10.

B.3 Model Temperature Ablation Study.

Temperature can significantly impact the consistency of MLLM’s output. We evaluated our pipeline under different temperature settings on non-MoE models where the randomness of outputs are controllable by temperature parameter, to evaluate our system’s robustness. Experiments on the ReasonSeg-Test split yield the following results:

<table>
  <tr>
    <th>Model</th>
    <th>Inference Temperature</th>
    <th>gIoU</th>
    <th>cIoU</th>
  </tr>
  <tr>
    <td rowspan="3">RSVP-LLaVA</td>
    <td>0.0</td>
    <td><b>55.9</b></td>
    <td><b>50.7</b></td>
  </tr>
  <tr>
    <td>0.4</td>
    <td>55.8</td>
    <td>50.5</td>
  </tr>
  <tr>
    <td>0.8</td>
    <td>55.4</td>
    <td>50.9</td>
  </tr>
  <tr>
    <td rowspan="3">RSVP-Qwen</td>
    <td>0.0</td>
    <td><b>56.6</b></td>
    <td><b>51.6</b></td>
  </tr>
  <tr>
    <td>0.4</td>
    <td>56.4</td>
    <td>51.7</td>
  </tr>
  <tr>
    <td>0.8</td>
    <td>56.5</td>
    <td>51.4</td>
  </tr>
</table>

Table 11: Effect of varying temperature in RSVP-LLaVA and RSVP-Qwen. Rows with the best gIoU or cIoU for each model are highlighted in gray, and best values are bolded.

Although slight variations are observed across settings, the overall performance remains stable. Temperature 0.0’s result are what we reported in our paper (bold text in the table). These findings demonstrate that our method is robust to changes in the temperature parameter.

B.4 Dependency on First Stage MLLM Ablation Study

To further explore our system’s performance reliance on first-stage MLLM choice, we experimented an additional ablation experiment on Qwen2-VL with its two variants (7B and 2B parameter size) on the ReasonSeg-Test split (with temperature set to 0) varying significantly in model parameter sizes.

<table>
  <tr>
    <th>Model</th>
    <th>gIoU</th>
    <th>cIoU</th>
  </tr>
  <tr>
    <td>RSVP-Qwen (7B First-Stage MLLM)</td>
    <td><b>56.6</b></td>
    <td><b>51.6</b></td>
  </tr>
  <tr>
    <td>RSVP-Qwen (2B First-Stage MLLM)</td>
    <td>45.8</td>
    <td>43.7</td>
  </tr>
  <tr>
    <td>LISA (7B MLLM, Zero-shot)</td>
    <td>48.7</td>
    <td>48.8</td>
  </tr>
</table>

Table 12: Comparison of First-stage MLLM choices with different model sizes and types. RSVP-Qwen (7B) achieves the highest performance.
The performance drop for the smaller 2B model is likely caused by its limited reasoning abilities, along with its reduced capacity to fully comprehend task requirements. It is worth notice that the performance difference between the RSVP-Qwen (2B) and LISA-7B is only up to 5.1% in terms of IoU metrics. In practice, deploying the pipeline with models smaller than 7B with the cost of a small level of performance dropping in reason segmentation tasks is practical and acceptable.

C Reasoning Segmentation Case Study

In this section, we demonstrate case studies regarding our model’s results with LISA-13B on the ReasonSeg Dataset Test split. We mainly report two types of demonstrative results: Incorrect localization and low-quality segmentation masks.

Figure 4: Illustration of Incorrect Localization cases produced by LISA.

Figure 5: Illustration of Low-quality Segmentation mask cases produced by LISA.

C.1 Case: Incorrect Localization

Fig. 4 shows three distinct cases that demonstrated the limitation of LISA in correctly comprehending the query and localizing the correct object in the image. In all three examples, our RSVP successfully identified the object of interest by reasoning about the provided query, while LISA failed to detect the correct object of interest. In the first case, LISA segmented out the tree branch which has nothing to do with the query, while in the second case, LISA was not aware of the requirement of distinguishing the more advanced fighter, while RSVP correctly captured this subtle requirement and identified the double-engine jet. For the final example, LISA mistook the strap as the item that protects the lens.

![Illustration of Low-quality Segmentation mask cases produced by LISA.](page_1016_186_377_120.png)

Figure 6: Illustration of Low-quality Segmentation mask cases produced by LISA.

C.2 Case: Low-Quality Segmentation Mask

Fig. 5 and Fig. 6 demonstrated another limitation of LISA, which is the possibility of producing poorly shaped segmentation masks. In the first case, LISA only managed to segment out the dog’s tongue, while RSVP correctly segmented out the entire mouth along with the tongue. For the horse case, our model precisely identified the pole on the front, while LISA produced a shattered segmentation mask for the front pole, and also incorrectly segmented out the unrelated pole in the background as well. The final example shown in Fig. 6 of LISA segmented a part of the tire, but the segmentation mask is deformed and only covers a very small portion of the object of interest.

C.3 Analysis and Summary

The above case study demonstrated two main limitations of LISA. On the one hand, LISA may incorrectly reason about the provided query, therefore producing off-the-track localized results that lead to the segmentation of unrelated objects. On the other hand, the latent segmentation proposal token that is produced by LISA’s front-end MLLM may not be able to efficiently guide the second-stage segmentation model to generate high-quality, complete segmentation masks. The generated visual prompt and the produced region proposal are visualized in Fig. 12.

C.4 Bad Case Analysis

In this subsection, we conduct a qualitative analysis of failure cases. Our observations reveal two primary categories of error:

False Localization or Misinterpretation. THe first bad case type originates from the first-stage false interpretation of user prompt or incorrect/imprecise localization, where the initial object localization or comprehension of object type is suboptimal.
Figure 7: Illustration of bad cases caused by false localization or prompt misinterpretation.

As shown in Fig. 7, in the first example, the query referred to the ceramic cup lid, but the system misinterpreted it as the plate, resulting in completely incorrect localization and segmentation. In the second example, the system only captured the left half of the table because the first-stage MLLM failed to include the right half in its localization.

Suboptimal Segmentation. This type of bad cases are mainly caused by the second-stage VLSM module which produces unsatisfactory segmentation masks with holes or incomplete/hard edges, resulting in suboptimal gIoU and cIoU metrics.

Figure 8: Illustration of bad cases caused by suboptimal segmentation masks.

As shown in Fig. 8, in both cases, our system correctly identifies the target object but fails to fully segment it. In the cigarette advertisement, the system recognizes the cigarette box but the mask contains a hole. In the whale case, the whale is mostly captured by RSVP, but the tail is not fully segmented, leaving holes and hard edges on the mask, partly due to the challenging visual conditions, as the whale’s tail is obscured by intense waves.

D Implementation Detail of Region-aware Visual Prompt

During the experimentation, evaluation, and ablation study, we designed the following prompt universally as demonstrated in Fig. 9. For “vertically-segmented image”, we refer to the image that is being processed by a horizontally-dividing visual prompt, while for “horizontally-segmented image”, it refers to the image being processed by the vertically-dividing visual prompt. The algorithm is demonstrated as in Algorithm 1. The example of a generated visual prompt is shown in Fig. 11.

Each image is resized to a resolution of \(1000 \times 1000\) during processing, and the margin width/height is dynamically set as 20% of the width / height of the uniformly divided vertical / horizontal regions.

Algorithm 1 Visual Prompt Generation

<table>
  <tr>
    <th>Algorithm 1</th>
    <th>Visual Prompt Generation</th>
  </tr>
  <tr>
    <td colspan="2">
      <pre>
procedure GENERATEVISUALPROMPT(I, n)  // Generate prompts for I with n segments
    h, w = HEIGHT(I), WIDTH(I)  // Get image dimensions
    s_h = h/n, s_w = w/n  // Compute segment sizes
    for i = 1 to n do
        for j = 1 to n do
            DRAW_HORIZONTAL_LINE(I, y = (i - 1) * s_h, color=green)  // Draw horizontal line
            PLACE_NUMERIC_LABEL(I, x = w/2, y = (i - 0.5) * s_h, color=white)  // Center label
        end for
        DRAW_VERTICAL_LINE(I, x = (j - 1) * s_w, color=green)  // Draw vertical line
        PLACE_NUMERIC_LABEL(I, x = (j - 0.5) * s_w, y = h/2, color=white)  // Center label
    end for
    return I_h, I_v  // Return image with visual prompts
      </pre>
    </td>
  </tr>
</table>

Algorithm 1: Algorithm used for generating visual prompts.

Figure 11: Visual prompt example: the image is evenly divided into vertical/horizontal regions. The dividing density is set to five for demonstrative purposes.
CoT Prompt Applied in Evaluations and Experiments

You are now acting as an image annotator. I will give you several abstract descriptions which refer to the same object which may appear in the given image and you need to locate them if they exists.

Now I will provide you with two images featuring exactly the same scene but annotated with vertical and horizontal visual segmentation prompt.

The first provided image is divided into <<segment_density>> segments vertically, and the second one is divided horizontally into <<segment_density>> segments. Each segment is labeled with a number from 1 to <<segment_density>>.

Now please carefully follow the below instruction step by step:
1. Infer what is (are) the objects that the questions are jointly referring to, carefully review the given image and find the object(s) that is the most probable candidate(s).

2. Denote from which labeled segment to which contain any parts of the object(s) that the questions described, both vertically and horizontally.

Tell me these segment ids in json as follows:
{
    "instance": "<words(adj+noun) short summarative description of the answer object>",
    "ids_vertical": [1,2,3,...],
    "ids_horizontal": [1,2,3,...],
    "reason": "<your rationale about what is(are) being referred>"
}

Attention: you MUST give all segment numbers that contain any parts of the object(s) that the questions described. The answer object may not be the salient object in the frame, and may even not appear in the frame.

If the answer is not in the image, please return empty ids like:
{
    "instance": "<words(adj+noun) short summarative description of the answer object>",
    "ids_vertical": [],
    "ids_horizontal": [],
    "reason": "<your rationale about what is being referred and why none of the object in the scene matches>"
}

Now, the questions are presented as follows, do NOT output anything other than the required json.

questions:
<<questions>>

Figure 9: The prompt utilized to query MLLMs in our implementation.
Prompt Applied in CoT Framework Ablation

You are now acting as an image annotator. I will give you several abstract descriptions which refer to the same object which may appear in the given image and you need to locate them if they exists.
Now I will provide you with two images featuring exactly the same scene but annotated with vertical and horizontal visual segmentation prompt.
The first provided image is divided into <<segment_density>> segments vertically, and the second one is divided horizontally into <<segment_density>> segments. Each segment is labeled with a number from 1 to <<segment_density>>.

Now please carefully follow the below instruction step by step:
1. Determine if the provided descriptions are long, complex or not. If the descriptions are long, abstract and complex, please perform the following task:
1.1. Infer what is (are) the objects that the questions are jointly referring to, carefully review the given image and find the object(s) that is the most probable candidate(s).
1.2. Describe this object in a short, concise, summarative way.
1.3. Denote from which labeled segment to which contain any parts of the object(s) that the questions described, both vertically and horizontally.
1.4. Set "is_complex" flag to True.
2. Otherwise, perform the following task instead:
2.1 Infer what is (are) the objects that the questions are jointly referring to, carefully review the given image and find the object(s) that is the most probable candidate(s).
2.2 Describe this object in a short, concise way, also include its relative location (top/bottom, left/right, center, ...) in the image.
2.3 Set "is_complex" flag to False.

Tell me these segment ids in json as follows:
{
    "is_complex": <true/false>,
    "instance": "<words(adj+noun) short summarative description of the answer object>",
    "ids_vertical": [1,2,3,...],
    "ids_horizontal": [1,2,3...],
    "reason": "<your rationale about what is(are) being referred>"
}

Attention: you MUST give all segment numbers that contain any parts of the object(s) that the questions described. The answer object may not be the salient object in the frame, and may even not appear in the frame.
If the answer is not in the image, please return empty ids like:
{"instance": "<words(adj+noun) short summarative description of the answer object>", "ids_vertical": [], "ids_horizontal": [], "reason": "<your rationale about what is being referred and why none of the object in the scene matches>"

Now, the questions are presented as follows, do NOT output anything other than the required json.
questions:
<<questions>>
<table>
  <tr>
    <th>Original Image</th>
    <th>Horizontal Visual Prompt</th>
    <th>Vertical Visual Prompt</th>
    <th>Visualized Region Proposal</th>
    <th>Segmentation Result</th>
  </tr>
  <tr>
    <td>![Original image of two horses jumping](page_246_312_158_97.png)</td>
    <td>![Horizontal visual prompt of two horses jumping](page_404_312_158_97.png)</td>
    <td>![Vertical visual prompt of two horses jumping](page_562_312_158_97.png)</td>
    <td>![Visualized region proposal of two horses jumping](page_720_312_158_97.png)</td>
    <td>![Segmentation result of two horses jumping](page_878_312_158_97.png)</td>
  </tr>
  <tr>
    <td>![Original image of a dog](page_246_410_158_97.png)</td>
    <td>![Horizontal visual prompt of a dog](page_404_410_158_97.png)</td>
    <td>![Vertical visual prompt of a dog](page_562_410_158_97.png)</td>
    <td>![Visualized region proposal of a dog](page_720_410_158_97.png)</td>
    <td>![Segmentation result of a dog](page_878_410_158_97.png)</td>
  </tr>
  <tr>
    <td>![Original image of a camera](page_246_508_158_97.png)</td>
    <td>![Horizontal visual prompt of a camera](page_404_508_158_97.png)</td>
    <td>![Vertical visual prompt of a camera](page_562_508_158_97.png)</td>
    <td>![Visualized region proposal of a camera](page_720_508_158_97.png)</td>
    <td>![Segmentation result of a camera](page_878_508_158_97.png)</td>
  </tr>
  <tr>
    <td>![Original image of a monkey](page_246_606_158_97.png)</td>
    <td>![Horizontal visual prompt of a monkey](page_404_606_158_97.png)</td>
    <td>![Vertical visual prompt of a monkey](page_562_606_158_97.png)</td>
    <td>![Visualized region proposal of a monkey](page_720_606_158_97.png)</td>
    <td>![Segmentation result of a monkey](page_878_606_158_97.png)</td>
  </tr>
  <tr>
    <td>![Original image of two boars](page_246_704_158_97.png)</td>
    <td>![Horizontal visual prompt of two boars](page_404_704_158_97.png)</td>
    <td>![Vertical visual prompt of two boars](page_562_704_158_97.png)</td>
    <td>![Visualized region proposal of two boars](page_720_704_158_97.png)</td>
    <td>![Segmentation result of two boars](page_878_704_158_97.png)</td>
  </tr>
  <tr>
    <td>![Original image of airplanes](page_246_802_158_97.png)</td>
    <td>![Horizontal visual prompt of airplanes](page_404_802_158_97.png)</td>
    <td>![Vertical visual prompt of airplanes](page_562_802_158_97.png)</td>
    <td>![Visualized region proposal of airplanes](page_720_802_158_97.png)</td>
    <td>![Segmentation result of airplanes](page_878_802_158_97.png)</td>
  </tr>
</table>

Produced Region Proposal   Region Proposal Margin

Figure 12: Visualization of produced visual prompts and region proposals for demonstrated cases.

Reviewer paper summaries: This paper introduces a new framework, RSVP, aimed at bridging the gap between MLLMs reasoning capabilities and visual perception. RSVP is a two-stage framework: in the reasoning stage, multimodal chain-of-thought visual prompts generate region proposals to enhance visual grounding; in the segmentation stage, a Vision-Language Segmentation Module refines these proposals into precise segmentation masks.
Summary of strengths: 1. The use of MLLMs in combination with the Vision-Language Segmentation Module enables image segmentation without the need for model training.
2. The proposed RSVP framework demonstrates strong performance, surpassing baseline models.
3. The paper exhibits a well-organized writing structure.
Summary of weaknesses: 1. The paper could compare the performance improvements brought by other CoT methods, such as Desp-CoT [1], Plan-and-Solve [2], and others.
2. I think it may be necessary to discuss the robustness of the paper method. After all, the CoT system is affected by multiple parameters such as prompt and temperature.
3. Can I analyze the correlation between the length of CoT and the accuracy of segmentation? I want to know whether there will also be test-time scaling law or limited by the reasoning boundary on this task?

[1] Wu Y, Zhang P, Xiong W, et al. The role of chain-of-thought in complex vision-language reasoning task.

[2] Wang L, Xu W, Lan Y, et al. Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models.

[3] Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models.

[4] Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models.
Comments, suggestions and typos: 1. Missing Related works about multi-modal chain-of-thought:
    - Multimodal Chain-of-Thought Reasoning in Language Models
    - M3CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought
    - Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey
    - Visual CoT: Advancing MLLMs with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning
2. The caption in Figure 1 refers to "Appendix B" however, the paper does not include an Appendix.
Overall assessment scores: 4