# ClawCraneNet: Leveraging Object-level Relation for Text-based Video Segmentation

arXiv: https://arxiv.org/abs/2103.10702 · HTML: https://mlsanity.com/paper/2103.10702/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 54 references that are in the map.
- First posted on arXiv 19 Mar 2021.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://mlsanity.com/paper/1711.07280/) (2017)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [MAttNet: Modular Attention Network for Referring Expression Comprehension](https://mlsanity.com/paper/1801.08186/) (2018)
- [Conditional Convolutions for Instance Segmentation](https://mlsanity.com/paper/2003.05664/) (2020)
- [Hierarchical Question-Image Co-Attention for Visual Question Answering](https://mlsanity.com/paper/1606.00061/) (2016)
- [Cross-Modal Self-Attention Network for Referring Image Segmentation](https://mlsanity.com/paper/1904.04745/) (2019)
- [Image Captioning with Semantic Attention](https://mlsanity.com/paper/1603.03925/) (2016)
- [Segmentation from Natural Language Expressions](https://mlsanity.com/paper/1603.06180/) (2016)
- [A Fast and Accurate One-Stage Approach to Visual Grounding](https://mlsanity.com/paper/1908.06354/) (2019)
- [Referring Image Segmentation via Cross-Modal Progressive Comprehension](https://mlsanity.com/paper/2010.00514/) (2020)
- [Recurrent Multimodal Interaction for Referring Image Segmentation](https://mlsanity.com/paper/1703.07939/) (2017)
- [Where Does It Exist: Spatio-Temporal Video Grounding for Multi-Form Sentences](https://mlsanity.com/paper/2001.06891/) (2020)
- [PhraseCut: Language-based Image Segmentation in the Wild](https://mlsanity.com/paper/2008.01187/) (2020)
- [Actor and Action Video Segmentation from a Sentence](https://mlsanity.com/paper/1803.07485/) (2018)
- [Linguistic Structure Guided Context Modeling for Referring Image Segmentation](https://mlsanity.com/paper/2010.00515/) (2020)
- [Dynamic Multimodal Instance Segmentation guided by natural language queries](https://mlsanity.com/paper/1807.02257/) (2018)
- [Video Captioning via Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1711.11135/) (2017)
- [Referring Expression Object Segmentation with Caption-Aware Consistency](https://mlsanity.com/paper/1910.04748/) (2019)
- [Segment as Points for Efficient Online Multi-Object Tracking and Segmentation](https://mlsanity.com/paper/2007.01550/) (2020)
- [Grounding-Tracking-Integration](https://mlsanity.com/paper/1912.06316/) (2019)
- [Video Object Grounding using Semantic Roles in Language Description](https://mlsanity.com/paper/2003.10606/) (2020)
- [Vision-Dialog Navigation by Exploring Cross-modal Memory](https://mlsanity.com/paper/2003.06745/) (2020)
- [Decoupled Novel Object Captioner](https://mlsanity.com/paper/1804.03803/) (2018)

## Built on by (most cited first, 21 of 21)

- [Language as Queries for Referring Video Object Segmentation](https://mlsanity.com/paper/2201.00487/) (2022)
- [OnlineRefer: A Simple Online Baseline for Referring Video Object Segmentation](https://mlsanity.com/paper/2307.09356/) (2023)
- [SOC: Semantic-Assisted Object Cluster for Referring Video Object Segmentation](https://mlsanity.com/paper/2305.17011/) (2023)
- [Decoupling Static and Hierarchical Motion Perception for Referring Video Segmentation](https://mlsanity.com/paper/2404.03645/) (2024)
- [Towards Robust Referring Video Object Segmentation with Cyclic Relational Consensus](https://mlsanity.com/paper/2207.01203/) (2022)
- [Towards Robust Referring Image Segmentation](https://mlsanity.com/paper/2209.09554/) (2022)
- [Spectrum-guided Multi-granularity Referring Video Object Segmentation](https://mlsanity.com/paper/2307.13537/) (2023)
- [Rethinking Cross-modal Interaction from a Top-down Perspective for Referring Video Object Segmentation](https://mlsanity.com/paper/2106.01061/) (2021)
- [Temporal Collection and Distribution for Referring Video Object Segmentation](https://mlsanity.com/paper/2309.03473/) (2023)
- [Exploring Pre-trained Text-to-Video Diffusion Models for Referring Video Object Segmentation](https://mlsanity.com/paper/2403.12042/) (2024)
- [RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation](https://mlsanity.com/paper/2307.00997/) (2023)
- [LoSh: Long-Short Text Joint Prediction Network for Referring Video Object Segmentation](https://mlsanity.com/paper/2306.08736/) (2023)
- [MaIL: A Unified Mask-Image-Language Trimodal Network for Referring Image Segmentation](https://mlsanity.com/paper/2111.10747/) (2021)
- [Temporally Consistent Referring Video Object Segmentation with Hybrid Memory](https://mlsanity.com/paper/2403.19407/) (2024)
- [SiRi: A Simple Selective Retraining Mechanism for Transformer-based Visual Grounding](https://mlsanity.com/paper/2207.13325/) (2022)
- [Online Video Instance Segmentation via Robust Context Fusion](https://mlsanity.com/paper/2207.05580/) (2022)
- [Bidirectional Correlation-Driven Inter-Frame Interaction Transformer for Referring Video Object Segmentation](https://mlsanity.com/paper/2307.00536/) (2023)
- [EventRR: Event Referential Reasoning for Referring Video Object Segmentation](https://mlsanity.com/paper/2508.07171/) (2025)
- [Mitigating Query Selection Bias in Referring Video Object Segmentation](https://mlsanity.com/paper/2509.13722/) (2025)
- [Referring Video Object Segmentation with Cross-Modality Proxy Queries](https://mlsanity.com/paper/2511.21139/) (2025)
- [Weakly-Supervised Referring Video Object Segmentation through Text Supervision](https://mlsanity.com/paper/2604.17797/) (2026)

## Abstract (arXiv)

Text-based video segmentation is a challenging task that segments out the natural language referred objects in videos. It essentially requires semantic comprehension and fine-grained video understanding. Existing methods introduce language representation into segmentation models in a bottom-up manner, which merely conducts vision-language interaction within local receptive fields of ConvNets. We argue that such interaction is not fulfilled since the model can barely construct region-level relationships given partial observations, which is contrary to the description logic of natural language/referring expressions. In fact, people usually describe a target object using relations with other objects, which may not be easily understood without seeing the whole video. To address the issue, we introduce a novel top-down approach by imitating how we human segment an object with the language guidance. We first figure out all candidate objects in videos and then choose the refereed one by parsing relations among those high-level objects. Three kinds of object-level relations are investigated for precise relationship understanding, i.e., positional relation, text-guided semantic relation, and temporal relation. Extensive experiments on A2D Sentences and J-HMDB Sentences show our method outperforms state-of-the-art methods by a large margin. Qualitative results also show our results are more explainable.