# Motion-Attentive Transition for Zero-Shot Video Object Segmentation

arXiv: https://arxiv.org/abs/2003.04253 · HTML: https://mlsanity.com/paper/2003.04253/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2020.

## Built on by (most cited first, 30 of 62)

- [Video Polyp Segmentation: A Deep Learning Perspective](https://mlsanity.com/paper/2203.14291/) (2022)
- [Full-Duplex Strategy for Video Object Segmentation](https://mlsanity.com/paper/2108.03151/) (2021)
- [Video Instance Segmentation with a Propose-Reduce Paradigm](https://mlsanity.com/paper/2103.13746/) (2021)
- [AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder](https://mlsanity.com/paper/2306.06370/) (2023)
- [Opening up Open-World Tracking](https://mlsanity.com/paper/2104.11221/) (2021)
- [Learning to Segment Rigid Motions from Two Frames](https://mlsanity.com/paper/2101.03694/) (2021)
- [Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2207.08485/) (2022)
- [VideoCutLER: Surprisingly Simple Unsupervised Video Instance Segmentation](https://mlsanity.com/paper/2308.14710/) (2023)
- [Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2209.03138/) (2022)
- [EM-driven unsupervised learning for efficient motion segmentation](https://mlsanity.com/paper/2201.02074/) (2022)
- [Surgformer: Surgical Transformer with Hierarchical Temporal Attention for Surgical Phase Recognition](https://mlsanity.com/paper/2408.03867/) (2024)
- [Unsupervised Video Object Segmentation via Prototype Memory Network](https://mlsanity.com/paper/2209.03712/) (2022)
- [Weakly Supervised Video Salient Object Detection](https://mlsanity.com/paper/2104.02391/) (2021)
- [Detail-Preserving Transformer for Light Field Image Super-Resolution](https://mlsanity.com/paper/2201.00346/) (2022)
- [A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic Information](https://mlsanity.com/paper/2206.02846/) (2022)
- [Global Spectral Filter Memory Network for Video Object Segmentation](https://mlsanity.com/paper/2210.05567/) (2022)
- [Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation](https://mlsanity.com/paper/2104.04782/) (2021)
- [Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps](https://mlsanity.com/paper/2104.10386/) (2021)
- [D^2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos](https://mlsanity.com/paper/2111.07774/) (2021)
- [Handwritten Mathematical Expression Recognition via Attention Aggregation based Bi-directional Mutual Learning](https://mlsanity.com/paper/2112.03603/) (2021)
- [Region Aware Video Object Segmentation with Deep Motion Modeling](https://mlsanity.com/paper/2207.10258/) (2022)
- [GaTector: A Unified Framework for Gaze Object Prediction](https://mlsanity.com/paper/2112.03549/) (2021)
- [Modeling Motion with Multi-Modal Features for Text-Based Video Segmentation](https://mlsanity.com/paper/2204.02547/) (2022)
- [A Simple and Powerful Global Optimization for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2209.09341/) (2022)
- [Video Salient Object Detection via Contrastive Features and Attention Modules](https://mlsanity.com/paper/2111.02368/) (2021)
- [Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction](https://mlsanity.com/paper/2104.05345/) (2021)
- [PSNet: Parallel Symmetric Network for Video Salient Object Detection](https://mlsanity.com/paper/2210.05912/) (2022)
- [Implicit Motion-Compensated Network for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2204.02791/) (2022)
- [DONet: Dual-Octave Network for Fast MR Image Reconstruction](https://mlsanity.com/paper/2105.05980/) (2021)
- [Panoramic Video Salient Object Detection with Ambisonic Audio Guidance](https://mlsanity.com/paper/2211.14419/) (2022)

## Abstract (arXiv)

In this paper, we present a novel Motion-Attentive Transition Network (MATNet) for zero-shot video object segmentation, which provides a new way of leveraging motion information to reinforce spatio-temporal object representation. An asymmetric attention block, called Motion-Attentive Transition (MAT), is designed within a two-stream encoder, which transforms appearance features into motion-attentive representations at each convolutional stage. In this way, the encoder becomes deeply interleaved, allowing for closely hierarchical interactions between object motion and appearance. This is superior to the typical two-stream architecture, which treats motion and appearance separately in each stream and often suffers from overfitting to appearance information. Additionally, a bridge network is proposed to obtain a compact, discriminative and scale-sensitive representation for multi-level encoder features, which is further fed into a decoder to achieve segmentation results. Extensive experiments on three challenging public benchmarks (i.e. DAVIS-16, FBMS and Youtube-Objects) show that our model achieves compelling performance against the state-of-the-arts.