# VideoMatch: Matching based Video Object Segmentation

arXiv: https://arxiv.org/abs/1809.01123 · HTML: https://mlsanity.com/paper/1809.01123/

## Key facts

- Cited by 90 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2018.

## Built on by (most cited first, 30 of 90)

- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Associating Objects with Transformers for Video Object Segmentation](https://mlsanity.com/paper/2106.02638/) (2021)
- [Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation](https://mlsanity.com/paper/2106.05210/) (2021)
- [Putting the Object Back into Video Object Segmentation](https://mlsanity.com/paper/2310.12982/) (2023)
- [Occluded Video Instance Segmentation: A Benchmark](https://mlsanity.com/paper/2102.01558/) (2021)
- [Decoupling Features in Hierarchical Propagation for Video Object Segmentation](https://mlsanity.com/paper/2210.09782/) (2022)
- [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)
- [SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation](https://mlsanity.com/paper/2101.08833/) (2021)
- [Towards Grand Unification of Object Tracking](https://mlsanity.com/paper/2207.07078/) (2022)
- [Efficient Regional Memory Network for Video Object Segmentation](https://mlsanity.com/paper/2103.12934/) (2021)
- [SAM2Long: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree](https://mlsanity.com/paper/2410.16268/) (2024)
- [LVOS: A Benchmark for Long-term Video Object Segmentation](https://mlsanity.com/paper/2211.10181/) (2022)
- [Relational Embedding for Few-Shot Classification](https://mlsanity.com/paper/2108.09666/) (2021)
- [Hierarchical Memory Matching Network for Video Object Segmentation](https://mlsanity.com/paper/2109.11404/) (2021)
- [Learning Position and Target Consistency for Memory-based Video Object Segmentation](https://mlsanity.com/paper/2104.04329/) (2021)
- [Joint Inductive and Transductive Learning for Video Object Segmentation](https://mlsanity.com/paper/2108.03679/) (2021)
- [Recurrent Dynamic Embedding for Video Object Segmentation](https://mlsanity.com/paper/2205.03761/) (2022)
- [Breaking the "Object" in Video Object Segmentation](https://mlsanity.com/paper/2212.06200/) (2022)
- [LVOS: A Benchmark for Large-scale Long-term Video Object Segmentation](https://mlsanity.com/paper/2404.19326/) (2024)
- [Local-Global Context Aware Transformer for Language-Guided Video Segmentation](https://mlsanity.com/paper/2203.09773/) (2022)
- [Per-Clip Video Object Segmentation](https://mlsanity.com/paper/2208.01924/) (2022)
- [Robust Visual Tracking by Segmentation](https://mlsanity.com/paper/2203.11191/) (2022)
- [Reliable Propagation-Correction Modulation for Video Object Segmentation](https://mlsanity.com/paper/2112.02853/) (2021)
- [Look Before You Match: Instance Understanding Matters in Video Object Segmentation](https://mlsanity.com/paper/2212.06826/) (2022)
- [Locality-Aware Inter-and Intra-Video Reconstruction for Self-Supervised Correspondence Learning](https://mlsanity.com/paper/2203.14333/) (2022)
- [SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-Maximization](https://mlsanity.com/paper/2208.10128/) (2022)
- [DropMAE: Learning Representations via Masked Autoencoders with Spatial-Attention Dropout for Temporal Matching Tasks](https://mlsanity.com/paper/2304.00571/) (2023)
- [Object Segmentation-Assisted Inter Prediction for Versatile Video Coding](https://mlsanity.com/paper/2403.11694/) (2024)
- [RMem: Restricted Memory Banks Improve Video Object Segmentation](https://mlsanity.com/paper/2406.08476/) (2024)

## Abstract (arXiv)

Video object segmentation is challenging yet important in a wide variety of applications for video analysis. Recent works formulate video object segmentation as a prediction task using deep nets to achieve appealing state-of-the-art performance. Due to the formulation as a prediction task, most of these methods require fine-tuning during test time, such that the deep nets memorize the appearance of the objects of interest in the given video. However, fine-tuning is time-consuming and computationally expensive, hence the algorithms are far from real time. To address this issue, we develop a novel matching based algorithm for video object segmentation. In contrast to memorization based classification techniques, the proposed approach learns to match extracted features to a provided template without memorizing the appearance of the objects. We validate the effectiveness and the robustness of the proposed method on the challenging DAVIS-16, DAVIS-17, Youtube-Objects and JumpCut datasets. Extensive results show that our method achieves comparable performance without fine-tuning and is much more favorable in terms of computational time.