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<h1 align="center"> RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation </h1> <p align="center"> <a href="https://arxiv.org/abs/2307.00997"><img src="https://img.shields.io/badge/arXiv-Paper-<color>"></a> </p> <h5 align="center"><em>Yonglin Li, Jing Zhang, Xiao Teng, Long Lan</em></h5> <p align="center"> <a href="#news">News</a> | <a href="#introduction">Abstract</a> | <a href="#usage">Usage</a> | <a href="#results">Results</a> | <a href="#statement">Statement</a> </p>News
2023.07.04
- The paper is post on arxiv!
Introduction
This is the official repository of the paper <a href=""> RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation </a>
<figure> <img src="Figs/overall_network.png"> <figcaption align = "center"><b>Figure 1: The overall pipeline of RefSAM. It mainly consists of five key components: Visual Encoder of SAM , Text Encoder, Cross Modal MLP, Dense Attention, Mask Decoder of SAM. </b></figcaption> </figure> <p> <p align="left"> In this study, we present the RefSAM model, which for the first time explores the potential of <a href="https://arxiv.org/abs/2304.02643"> SAM </a> for RVOS by incorporating multi-view information from diverse modalities and successive frames at different timestamps. Our proposed approach adapts the original SAM model to enhance cross-modality learning by employing a lightweight Cross-Modal MLP that projects the text embedding of the referring expression into sparse and dense embeddings, serving as user-interactive prompts. Subsequently, a parameter-efficient tuning strategy is employed to effectively align and fuse the language and vision features. Through comprehensive ablation studies, we demonstrate the practical and effective design choices of our strategy. Extensive experiments conducted on Ref-Youtu-VOS and Ref-DAVIS17 datasets validate the superiority and effectiveness of our RefSAM model over existing methods.Usage
The code will be released soon.
Results
Results on RVOS datasets
<figure style="text-align: center;"> <img src="Figs/Results%20on%20Ref-DAVIS17.png"> <figcaption align = "center"><b>Figure 2: Results on Ref-DAVIS17. </b></figcaption> </figure> <p> <figure style="text-align: center;"> <img src="Figs/Results on Ref-Youtube-VOS.jpeg"> <figcaption align = "center"><b>Figure 3: Results on Ref-Youtube-VOS. </b></figcaption> </figure> <p>A comprehensive comparison between RefSAM and existing methods.
Visualization Results
Visualization results of our RefSAM model on Ref-DAVIS17.
<figure> <img src="Figs/example.png"> <figcaption align = "center"><b>Figure 4: Some examples of RefSAM segmentation results on Ref-DAVIS17. </b></figcaption> </figure> <p>We show the visualization results of our RefSAM model. It can be seen that RefSAM is capable of effectively segmenting and tracking the referred object even in challenging scenarios, such as variations in person poses, and occlusions between instances.
Visualization of different models on Ref-DAVIS17.
<figure> <img src="Figs/Visualization%20to%20compare%20different%20model.jpeg"> <figcaption align = "center"><b>Figure 5: Visualization of different models on Ref-DAVIS17. From left to right: RefSAM, ReferFormer, SAM-Track + Ground DINO, and PerSAM + Ground DINO. </b></figcaption> </figure> <p>Furthermore, we present the results of differnt models. It is clear that our RefSAM demonstrates significantly enhanced cross-modal understanding capability.
Model Analysis
The influence of different learning rates for the learnable modules
<figure> <img src="Figs/The influence of different learning rates for the learnable modules of RefSAM.png"> <figcaption align = "center"><b>Figure 6: The influence of different learning rates for the learnable modules of RefSAM.</a> </b></figcaption> </figure>Ablation study of different module designs.
<figure> <img src="Figs/Ablation study of different module designs.png"> <figcaption align = "center"><b>Figure 7: Ablation study of different module designs. </a> </b></figcaption> </figure>Ablation study of the key components
<figure style="text-align: center;"> <img src="Figs/Ablation%20study%20of%20the%20key%20compoents%20of%20RefSAM.png"> <figcaption align = "center"><b>Figure 8: Ablation study of the key compoents of RefSAM</a> </b></figcaption> </figure>Influence of the model size of Visual Encoder
<figure style="text-align: center;"> <img src="Figs/Influence of the model size of Visual Encoder.png"> <figcaption align = "center"><b>Figure 9: Influence of the model size of Visual Encoder. We do not use the data augmentation in this experiment. </a> </b></figcaption> </figure>Number of learnable parameters of different Models
<figure style="text-align: center;"> <img src="Figs/Number of learnable parameters of different Models.png"> <figcaption align = "center"><b>Figure 10: Number of learnable parameters of different Models</a> </b></figcaption> </figure>Inference speed of different models.
<figure style="text-align: center;"> <img src="Figs/Inference%20speed%20of%20different%20models..png"> <figcaption align = "center"><b>Figure 11: Inference speed of different models.</a> </b></figcaption> </figure>Statement
This project is for research purpose only. For any other questions please contact yonglin_edu@163.com.