# Learning to Learn Better for Video Object Segmentation

arXiv: https://arxiv.org/abs/2212.02112 · HTML: https://mlsanity.com/paper/2212.02112/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 31 references that are in the map.
- First posted on arXiv 5 Dec 2022.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [The 2017 DAVIS Challenge on Video Object Segmentation](https://mlsanity.com/paper/1704.00675/) (2017)
- [Video Object Segmentation using Space-Time Memory Networks](https://mlsanity.com/paper/1904.00607/) (2019)
- [One-Shot Video Object Segmentation](https://mlsanity.com/paper/1611.05198/) (2016)
- [YouTube-VOS: Sequence-to-Sequence Video Object Segmentation](https://mlsanity.com/paper/1809.00461/) (2018)
- [Associating Objects with Transformers for Video Object Segmentation](https://mlsanity.com/paper/2106.02638/) (2021)
- [ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias](https://mlsanity.com/paper/2106.03348/) (2021)
- [Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation](https://mlsanity.com/paper/2106.05210/) (2021)
- [Video Object Segmentation with Episodic Graph Memory Networks](https://mlsanity.com/paper/2007.07020/) (2020)
- [Kernelized Memory Network for Video Object Segmentation](https://mlsanity.com/paper/2007.08270/) (2020)
- [Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement](https://mlsanity.com/paper/2010.07958/) (2020)
- [Learning What to Learn for Video Object Segmentation](https://mlsanity.com/paper/2003.11540/) (2020)
- [Efficient Regional Memory Network for Video Object Segmentation](https://mlsanity.com/paper/2103.12934/) (2021)
- [PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation](https://mlsanity.com/paper/1807.09190/) (2018)
- [Collaborative Video Object Segmentation by Multi-Scale Foreground-Background Integration](https://mlsanity.com/paper/2010.06349/) (2020)
- [Learning Fast and Robust Target Models for Video Object Segmentation](https://mlsanity.com/paper/2003.00908/) (2020)
- [Hierarchical Memory Matching Network for Video Object Segmentation](https://mlsanity.com/paper/2109.11404/) (2021)
- [SwiftNet: Real-time Video Object Segmentation](https://mlsanity.com/paper/2102.04604/) (2021)
- [Fast Video Object Segmentation using the Global Context Module](https://mlsanity.com/paper/2001.11243/) (2020)
- [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)
- [A Transductive Approach for Video Object Segmentation](https://mlsanity.com/paper/2004.07193/) (2020)
- [State-Aware Tracker for Real-Time Video Object Segmentation](https://mlsanity.com/paper/2003.00482/) (2020)
- [Recurrent Dynamic Embedding for Video Object Segmentation](https://mlsanity.com/paper/2205.03761/) (2022)
- [Omnimatte: Associating Objects and Their Effects in Video](https://mlsanity.com/paper/2105.06993/) (2021)
- [SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-Maximization](https://mlsanity.com/paper/2208.10128/) (2022)
- [Siamese Network with Interactive Transformer for Video Object Segmentation](https://mlsanity.com/paper/2112.13983/) (2021)

## Built on by (most cited first, 6 of 6)

- [Token Contrast for Weakly-Supervised Semantic Segmentation](https://mlsanity.com/paper/2303.01267/) (2023)
- [XMem++: Production-level Video Segmentation From Few Annotated Frames](https://mlsanity.com/paper/2307.15958/) (2023)
- [MPG-SAM 2: Adapting SAM 2 with Mask Priors and Global Context for Referring Video Object Segmentation](https://mlsanity.com/paper/2501.13667/) (2025)
- [Bidirectional Correlation-Driven Inter-Frame Interaction Transformer for Referring Video Object Segmentation](https://mlsanity.com/paper/2307.00536/) (2023)
- [RS2-SAM2: Customized SAM2 for Referring Remote Sensing Image Segmentation](https://mlsanity.com/paper/2503.07266/) (2025)
- [Efficient Video Object Segmentation via Modulated Cross-Attention Memory](https://mlsanity.com/paper/2403.17937/) (2024)

## Abstract (arXiv)

Recently, the joint learning framework (JOINT) integrates matching based transductive reasoning and online inductive learning to achieve accurate and robust semi-supervised video object segmentation (SVOS). However, using the mask embedding as the label to guide the generation of target features in the two branches may result in inadequate target representation and degrade the performance. Besides, how to reasonably fuse the target features in the two different branches rather than simply adding them together to avoid the adverse effect of one dominant branch has not been investigated. In this paper, we propose a novel framework that emphasizes Learning to Learn Better (LLB) target features for SVOS, termed LLB, where we design the discriminative label generation module (DLGM) and the adaptive fusion module to address these issues. Technically, the DLGM takes the background-filtered frame instead of the target mask as input and adopts a lightweight encoder to generate the target features, which serves as the label of the online few-shot learner and the value of the decoder in the transformer to guide the two branches to learn more discriminative target representation. The adaptive fusion module maintains a learnable gate for each branch, which reweighs the element-wise feature representation and allows an adaptive amount of target information in each branch flowing to the fused target feature, thus preventing one branch from being dominant and making the target feature more robust to distractor. Extensive experiments on public benchmarks show that our proposed LLB method achieves state-of-the-art performance.