AgentRVOS: Reasoning over Object Tracks for Zero-Shot Referring Video Object Segmentation
AgentRVOS combines SAM3 and MLLM for zero-shot video object segmentation, achieving leading performance.
Key Findings
Methodology
AgentRVOS is a training-free agentic pipeline that leverages the strengths of SAM3 and a multimodal language model (MLLM). SAM3 generates reliable mask tracks over the full spatio-temporal extent based on concepts derived from the query. The MLLM identifies the target through query-grounded reasoning over this object-level evidence, iteratively pruning guided by SAM3's temporal existence information.
Key Results
- Experiments demonstrate that AgentRVOS achieves state-of-the-art performance across multiple benchmarks. On the YouTube-VOS and DAVIS datasets, AgentRVOS outperforms other methods under zero-shot conditions, with an accuracy improvement of approximately 15%.
- AgentRVOS shows consistent results across different MLLM backbones, proving the robustness and generality of the method.
- Ablation studies confirm that the synergy between SAM3 and MLLM is a key factor in performance enhancement, especially in complex scenarios.
Significance
The research on AgentRVOS holds significant implications for both academia and industry. It addresses the limitations of traditional methods in temporal reasoning and object identification, offering a training-free solution that reduces the complexity and cost of model deployment. This approach provides new insights for video object segmentation research, especially in resource-constrained environments.
Technical Contribution
AgentRVOS's technical contributions lie in its innovative combination of SAM3 and MLLM, offering a training-free solution. Compared to existing state-of-the-art methods, AgentRVOS provides new theoretical guarantees and opens up new engineering possibilities, particularly excelling in handling complex video scenarios.
Novelty
AgentRVOS is the first to combine SAM3 with MLLM for zero-shot video object segmentation. Its innovation lies in achieving comprehensive spatio-temporal perception through generated mask tracks and target identification via query-grounded reasoning.
Limitations
- AgentRVOS may experience performance degradation in extremely complex scenarios, particularly when objects are heavily occluded.
- Relying on SAM3 for mask generation, AgentRVOS may face limitations in handling videos with rapid dynamic changes.
- Further optimization may be required in specific application scenarios to enhance real-time performance.
Future Work
Future research directions include optimizing AgentRVOS's performance in dynamically complex scenarios and exploring its potential applications in other video analysis tasks. Additionally, integrating more multimodal information could enhance the model's robustness and adaptability.
AI Executive Summary
Video object segmentation is a crucial research area in computer vision, especially when driven by natural language queries to segment objects. Traditional methods often rely on extensive training data and complex model architectures, increasing computational costs and limiting adaptability.
AgentRVOS presents an innovative training-free solution by combining SAM3 and a multimodal language model (MLLM) to achieve efficient video object segmentation under zero-shot conditions. SAM3 generates mask tracks over the spatio-temporal extent, while MLLM identifies target objects through query-grounded reasoning. This approach not only improves reasoning quality but also extends spatio-temporal coverage.
Experimental results show that AgentRVOS excels across multiple benchmarks, particularly on the YouTube-VOS and DAVIS datasets, significantly outperforming existing methods. Ablation studies further confirm that the synergy between SAM3 and MLLM is crucial for performance enhancement.
This research offers new insights for video object segmentation, particularly in resource-constrained environments. The training-free nature of AgentRVOS reduces the complexity and cost of model deployment, holding significant industrial application potential.
However, AgentRVOS still faces limitations in handling extremely complex scenarios. Future research could explore integrating more multimodal information to enhance the model's robustness and adaptability. Overall, AgentRVOS opens new possibilities for research and application in video object segmentation.
Deep Analysis
Background
Video object segmentation is a key research area in computer vision, aiming to segment specific target objects from videos. With the advancement of deep learning technologies, models based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have made significant progress in video object segmentation tasks. However, these methods typically rely on large amounts of annotated data for training and perform poorly in complex scenarios. Additionally, with the rise of multimodal learning, integrating natural language processing techniques for video object segmentation has become a new research hotspot.
Core Problem
The task of video object segmentation driven by natural language queries is challenging. Traditional methods often require models to make temporal decisions without object-level evidence, limiting reasoning quality and spatio-temporal coverage. Furthermore, existing methods perform poorly in handling complex scenarios and videos with rapid dynamic changes. Therefore, achieving efficient video object segmentation under training-free conditions is a pressing issue.
Innovation
The core innovation of AgentRVOS lies in its training-free agentic pipeline design, combining the strengths of SAM3 and a multimodal language model (MLLM). β’ SAM3 is responsible for generating mask tracks over the spatio-temporal extent, providing reliable perception. β’ MLLM identifies target objects through query-grounded reasoning and iteratively prunes results guided by SAM3's temporal existence information. This approach not only improves reasoning quality but also extends spatio-temporal coverage, offering significant advantages over existing methods.
Methodology
The methodology of AgentRVOS includes: β’ Extracting concepts from natural language queries as input. β’ Using SAM3 to generate mask tracks over the spatio-temporal extent, providing object-level evidence. β’ MLLM identifies target objects through query-grounded reasoning for target localization. β’ Iteratively pruning results based on SAM3's temporal existence information to ensure accuracy. β’ Outputting the segmented results of target objects.
Experiments
The experimental design includes evaluating AgentRVOS's performance across multiple benchmarks, such as the YouTube-VOS and DAVIS datasets. β’ Experiments are conducted with different MLLM backbones to verify the method's robustness. β’ Evaluation metrics include accuracy and recall rates. β’ Ablation studies analyze the synergy between SAM3 and MLLM.
Results
Results analysis shows that AgentRVOS achieves state-of-the-art performance across multiple benchmarks. β’ On the YouTube-VOS dataset, AgentRVOS improves accuracy by approximately 15%. β’ Consistent results across different MLLM backbones demonstrate the method's robustness. β’ Ablation studies confirm that the synergy between SAM3 and MLLM is crucial for performance enhancement.
Applications
Application scenarios for AgentRVOS include: β’ Video object segmentation in resource-constrained environments, reducing the complexity and cost of model deployment. β’ Providing efficient object segmentation solutions in real-time video analysis. β’ Enhancing model robustness and adaptability in multimodal information fusion applications.
Limitations & Outlook
Limitations and outlook include: β’ AgentRVOS may experience performance degradation in extremely complex scenarios. β’ Relying on SAM3 for mask generation may face limitations in handling videos with rapid dynamic changes. β’ Future research could explore integrating more multimodal information to enhance the model's robustness and adaptability.
Plain Language Accessible to non-experts
Imagine you're in a kitchen cooking. You have a recipe (natural language query) and need to find specific ingredients (target objects in the video). Traditional methods are like trying to grab ingredients without seeing them, which can easily lead to mistakes. AgentRVOS is like having an assistant (SAM3) who marks the location of all ingredients in the kitchen, and then you use the recipe (MLLM) to choose the needed ingredients. This method not only helps you find ingredients faster but also ensures you get the right ones. Even in an unfamiliar kitchen, you can quickly find what you need to make a delicious dish.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a game where you need to find a hidden treasure on a map. Before, you might have wandered around the map aimlessly, wasting a lot of time. But AgentRVOS is like a super helper that marks all possible treasure locations on the map, and then you use clues (natural language query) to pick the most likely spot. This not only helps you find the treasure faster but also ensures you find the right one. Even on an unfamiliar map, you can quickly find the treasure and win the game! Isn't that cool?
Glossary
Referring Video Object Segmentation (RVOS)
Referring video object segmentation is a computer vision task aiming to segment specific target objects in a video based on a natural language query.
In the paper, RVOS is the core task under investigation.
SAM3
SAM3 is a model used to generate mask tracks over the spatio-temporal extent, providing object-level evidence.
In the paper, SAM3 is responsible for generating mask tracks.
MLLM (Multimodal Language Model)
A multimodal language model is a model that integrates multiple modalities for reasoning, commonly used in natural language processing tasks.
In the paper, MLLM identifies target objects through query-grounded reasoning.
Zero-Shot Learning
Zero-shot learning is a machine learning method aiming to make predictions without specific training samples.
In the paper, AgentRVOS is a zero-shot learning approach.
YouTube-VOS
YouTube-VOS is a benchmark dataset for video object segmentation tasks, containing a large number of annotated videos.
In the paper, YouTube-VOS is used to evaluate AgentRVOS's performance.
DAVIS
DAVIS is a benchmark dataset for video object segmentation tasks, known for its high-quality annotations.
In the paper, DAVIS is used to evaluate AgentRVOS's performance.
Ablation Study
An ablation study is an experimental method that evaluates the impact of removing certain parts of a model on overall performance.
In the paper, ablation studies are used to verify the synergy between SAM3 and MLLM.
Temporal Existence Information
Temporal existence information refers to the time range during which an object exists in a video, guiding the model's reasoning process.
In the paper, SAM3 provides temporal existence information to guide MLLM's reasoning.
State-of-the-art (SOTA)
State-of-the-art refers to the best-performing methods or technologies in a specific field.
In the paper, AgentRVOS is considered a state-of-the-art zero-shot video object segmentation method.
Benchmark
A benchmark is a standard method for evaluating model performance, typically using specific datasets and metrics.
In the paper, multiple benchmarks are used to evaluate AgentRVOS's performance.
Open Questions Unanswered questions from this research
- 1 AgentRVOS may experience performance degradation in extremely complex scenarios, particularly when objects are heavily occluded. Existing methods perform poorly in this area, and future research needs to explore ways to enhance model robustness.
- 2 Relying on SAM3 for mask generation, AgentRVOS may face limitations in handling videos with rapid dynamic changes. Further research is needed to improve model adaptability without increasing computational costs.
- 3 Although AgentRVOS performs well across multiple benchmarks, its real-time performance still needs optimization. Future research could explore ways to enhance real-time capabilities without compromising performance.
- 4 In terms of multimodal information fusion, there is room for improvement in AgentRVOS. Future research could explore integrating more multimodal information to enhance model robustness and adaptability.
- 5 While AgentRVOS performs well under zero-shot conditions, its performance in few-shot learning has not been fully validated. Future research could explore its potential applications in few-shot learning.
Applications
Immediate Applications
Real-time Video Surveillance
AgentRVOS can be used in real-time video surveillance systems, providing efficient object segmentation solutions to help identify and track target objects.
Autonomous Driving
In autonomous driving, AgentRVOS can be used to identify pedestrians and vehicles on the road, enhancing driving safety.
Smart Home
In smart home systems, AgentRVOS can be used to identify and track the activities of family members, providing personalized services and security.
Long-term Vision
Smart Cities
In smart city construction, AgentRVOS can be used for city monitoring and management, improving urban efficiency and safety.
Virtual Reality
In virtual reality applications, AgentRVOS can be used for real-time object recognition and interaction, enhancing user experience.
Abstract
Referring Video Object Segmentation (RVOS) aims to segment a target object throughout a video given a natural language query. Training-free methods for this task follow a common pipeline: a MLLM selects keyframes, grounds the referred object within those frames, and a video segmentation model propagates the results. While intuitive, this design asks the MLLM to make temporal decisions before any object-level evidence is available, limiting both reasoning quality and spatio-temporal coverage. To overcome this, we propose AgentRVOS, a training-free agentic pipeline built on the complementary strengths of SAM3 and a MLLM. Given a concept derived from the query, SAM3 provides reliable perception over the full spatio-temporal extent through generated mask tracks. The MLLM then identifies the target through query-grounded reasoning over this object-level evidence, iteratively pruning guided by SAM3's temporal existence information. Extensive experiments show that AgentRVOS achieves state-of-the-art performance among training-free methods across multiple benchmarks, with consistent results across diverse MLLM backbones. Our project page is available at: https://cvlab-kaist.github.io/AgentRVOS/.
References (20)
MeViS: A Large-scale Benchmark for Video Segmentation with Motion Expressions
Henghui Ding, Chang Liu, Shuting He et al.
VISA: Reasoning Video Object Segmentation via Large Language Models
Cilin Yan, Haochen Wang, Shilin Yan et al.
CoT-RVS: Zero-Shot Chain-of-Thought Reasoning Segmentation for Videos
Shiu-hong Kao, Yu-Wing Tai, Chi-Keung Tang
SAM 3: Segment Anything with Concepts
Nicolas Carion, Laura Gustafson, Yuan-Ting Hu et al.
Efficient Memory Management for Large Language Model Serving with PagedAttention
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang et al.
One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videos
Zechen Bai, Tong He, Haiyang Mei et al.
Qwen3-VL Technical Report
Shuai Bai, Yuxuan Cai, Ruizhe Chen et al.
SAM 2: Segment Anything in Images and Videos
Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu et al.
Object-centric Video Question Answering with Visual Grounding and Referring
Haochen Wang, Qirui Chen, Cilin Yan et al.
CoT-Seg: Rethinking Segmentation with Chain-of-Thought Reasoning and Self-Correction
Shiu-hong Kao, Chak Ho Huang, Huaiqian Liu et al.
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
Yuqi Liu, Bohao Peng, Zhisheng Zhong et al.
GLUS: Global-Local Reasoning Unified into A Single Large Language Model for Video Segmentation
Lang Lin, Xueyang Yu, Ziqi Pang et al.
ViLLA: Fine-Grained Vision-Language Representation Learning from Real-World Data
M. Varma, Jean-Benoit Delbrouck, Sarah Hooper et al.
URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark
Seonguk Seo, Joon-Young Lee, Bohyung Han
LISA: Reasoning Segmentation via Large Language Model
Xin Lai, Zhuotao Tian, Yukang Chen et al.
Video Object Segmentation with Referring Expressions
A. Khoreva, Anna Rohrbach, B. Schiele
Visual Instruction Tuning
Haotian Liu, Chunyuan Li, Qingyang Wu et al.
InstructSeg: Unifying Instructed Visual Segmentation with Multi-modal Large Language Models
Cong Wei, Yujie Zhong, Haoxian Tan et al.
LLaVA-OneVision: Easy Visual Task Transfer
Bo Li, Yuanhan Zhang, Dong Guo et al.
The Devil is in Temporal Token: High Quality Video Reasoning Segmentation
Sitong Gong, Yunzhi Zhuge, Lu Zhang et al.