# Interactive Video Object Segmentation Using Global and Local Transfer Modules

arXiv: https://arxiv.org/abs/2007.08139 · HTML: https://mlsanity.com/paper/2007.08139/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2020.

## Built on by (most cited first, 26 of 26)

- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [MOSE: A New Dataset for Video Object Segmentation in Complex Scenes](https://mlsanity.com/paper/2302.01872/) (2023)
- [Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation](https://mlsanity.com/paper/2106.05210/) (2021)
- [A Survey on Deep Learning Technique for Video Segmentation](https://mlsanity.com/paper/2107.01153/) (2021)
- [Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion](https://mlsanity.com/paper/2103.07941/) (2021)
- [Segment Anything Meets Point Tracking](https://mlsanity.com/paper/2307.01197/) (2023)
- [Full-Duplex Strategy for Video Object Segmentation](https://mlsanity.com/paper/2108.03151/) (2021)
- [iSegFormer: Interactive Segmentation via Transformers with Application to 3D Knee MR Images](https://mlsanity.com/paper/2112.11325/) (2021)
- [Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps](https://mlsanity.com/paper/2104.10386/) (2021)
- [Efficient Track Anything](https://mlsanity.com/paper/2411.18933/) (2024)
- [Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild](https://mlsanity.com/paper/2103.10391/) (2021)
- [ScribbleSeg: Scribble-based Interactive Image Segmentation](https://mlsanity.com/paper/2303.11320/) (2023)
- [Caption Anything in Video: Fine-grained Object-centric Captioning via Spatiotemporal Multimodal Prompting](https://mlsanity.com/paper/2504.05541/) (2025)
- [VideoClick: Video Object Segmentation with a Single Click](https://mlsanity.com/paper/2101.06545/) (2021)
- [SAM-I2V: Upgrading SAM to Support Promptable Video Segmentation with Less than 0.2% Training Cost](https://mlsanity.com/paper/2506.01304/) (2025)
- [LiVOS: Light Video Object Segmentation with Gated Linear Matching](https://mlsanity.com/paper/2411.02818/) (2024)
- [Revisiting Click-based Interactive Video Object Segmentation](https://mlsanity.com/paper/2203.01784/) (2022)
- [Fast Interactive Video Object Segmentation with Graph Neural Networks](https://mlsanity.com/paper/2103.03821/) (2021)
- [Strike the Balance: On-the-Fly Uncertainty based User Interactions for Long-Term Video Object Segmentation](https://mlsanity.com/paper/2408.00169/) (2024)
- [Training-Free Robust Interactive Video Object Segmentation](https://mlsanity.com/paper/2406.05485/) (2024)
- [AuralSAM2: Enabling SAM2 Hear Through Pyramid Audio-Visual Feature Prompting](https://mlsanity.com/paper/2506.01015/) (2025)
- [IDPro: Flexible Interactive Video Object Segmentation by ID-queried Concurrent Propagation](https://mlsanity.com/paper/2401.12480/) (2024)
- [ClickVOS: Click Video Object Segmentation](https://mlsanity.com/paper/2403.06130/) (2024)
- [AutoExpert: Automating 3D LiDAR Annotation from Expert-Crafted Guidelines](https://mlsanity.com/paper/2506.02914/) (2025)
- [Fast Self-Supervised depth and mask aware Association for Multi-Object Tracking](https://mlsanity.com/paper/2510.09878/) (2025)
- [FiVOS: A Fish Segmentation Algorithm Based on Interactive Video Object Segmentation and Filter Enhancement](https://mlsanity.com/paper/2610.01480/) (2026)

## Abstract (arXiv)

An interactive video object segmentation algorithm, which takes scribble annotations on query objects as input, is proposed in this paper. We develop a deep neural network, which consists of the annotation network (A-Net) and the transfer network (T-Net). First, given user scribbles on a frame, A-Net yields a segmentation result based on the encoder-decoder architecture. Second, T-Net transfers the segmentation result bidirectionally to the other frames, by employing the global and local transfer modules. The global transfer module conveys the segmentation information in an annotated frame to a target frame, while the local transfer module propagates the segmentation information in a temporally adjacent frame to the target frame. By applying A-Net and T-Net alternately, a user can obtain desired segmentation results with minimal efforts. We train the entire network in two stages, by emulating user scribbles and employing an auxiliary loss. Experimental results demonstrate that the proposed interactive video object segmentation algorithm outperforms the state-of-the-art conventional algorithms. Codes and models are available at https://github.com/yuk6heo/IVOS-ATNet.