# TubeFormer-DeepLab: Video Mask Transformer

arXiv: https://arxiv.org/abs/2205.15361 · HTML: https://mlsanity.com/paper/2205.15361/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 77 of its 95 references that are in the map.
- First posted on arXiv 30 May 2022.

## Builds on (most cited first, 30 of 77)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [Unified Perceptual Parsing for Scene Understanding](https://mlsanity.com/paper/1807.10221/) (2018)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [CCNet: Criss-Cross Attention for Semantic Segmentation](https://mlsanity.com/paper/1811.11721/) (2018)
- [Panoptic Feature Pyramid Networks](https://mlsanity.com/paper/1901.02446/) (2019)
- [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://mlsanity.com/paper/2012.07177/) (2020)
- [Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation](https://mlsanity.com/paper/1909.11065/) (2019)
- [Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs](https://mlsanity.com/paper/1412.7062/) (2014)
- [HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking](https://mlsanity.com/paper/2009.07736/) (2020)

## Built on by (most cited first, 30 of 35)

- [An Image is Worth 32 Tokens for Reconstruction and Generation](https://mlsanity.com/paper/2406.07550/) (2024)
- [OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understanding](https://mlsanity.com/paper/2406.19389/) (2024)
- [Transformer-Based Visual Segmentation: A Survey](https://mlsanity.com/paper/2304.09854/) (2023)
- [A Generalist Framework for Panoptic Segmentation of Images and Videos](https://mlsanity.com/paper/2210.06366/) (2022)
- [OMG-Seg: Is One Model Good Enough For All Segmentation?](https://mlsanity.com/paper/2401.10229/) (2024)
- [CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation](https://mlsanity.com/paper/2206.08948/) (2022)
- [A Generalized Framework for Video Instance Segmentation](https://mlsanity.com/paper/2211.08834/) (2022)
- [MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models](https://mlsanity.com/paper/2210.01820/) (2022)
- [Panoptic Video Scene Graph Generation](https://mlsanity.com/paper/2311.17058/) (2023)
- [UniVS: Unified and Universal Video Segmentation with Prompts as Queries](https://mlsanity.com/paper/2402.18115/) (2024)
- [Video-kMaX: A Simple Unified Approach for Online and Near-Online Video Panoptic Segmentation](https://mlsanity.com/paper/2304.04694/) (2023)
- [Tube-Link: A Flexible Cross Tube Framework for Universal Video Segmentation](https://mlsanity.com/paper/2303.12782/) (2023)
- [ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation](https://mlsanity.com/paper/2306.17319/) (2023)
- [ViCaS: A Dataset for Combining Holistic and Pixel-level Video Understanding using Captions with Grounded Segmentation](https://mlsanity.com/paper/2412.09754/) (2024)
- [kMaX-DeepLab: k-means Mask Transformer](https://mlsanity.com/paper/2207.04044/) (2022)
- [Temporal-aware Hierarchical Mask Classification for Video Semantic Segmentation](https://mlsanity.com/paper/2309.08020/) (2023)
- [Video Object Segmentation in Panoptic Wild Scenes](https://mlsanity.com/paper/2305.04470/) (2023)
- [Towards Deeply Unified Depth-aware Panoptic Segmentation with Bi-directional Guidance Learning](https://mlsanity.com/paper/2307.14786/) (2023)
- [RMP-SAM: Towards Real-Time Multi-Purpose Segment Anything](https://mlsanity.com/paper/2401.10228/) (2024)
- [General and Task-Oriented Video Segmentation](https://mlsanity.com/paper/2407.06540/) (2024)
- [Offline-to-Online Knowledge Distillation for Video Instance Segmentation](https://mlsanity.com/paper/2302.07516/) (2023)
- [MaskConver: Revisiting Pure Convolution Model for Panoptic Segmentation](https://mlsanity.com/paper/2312.06052/) (2023)
- [OW-VISCapTor: Abstractors for Open-World Video Instance Segmentation and Captioning](https://mlsanity.com/paper/2404.03657/) (2024)
- [A Simple Video Segmenter by Tracking Objects Along Axial Trajectories](https://mlsanity.com/paper/2311.18537/) (2023)
- [Enhancing Temporal Consistency in Video Editing by Reconstructing Videos with 3D Gaussian Splatting](https://mlsanity.com/paper/2406.02541/) (2024)
- [A Comprehensive Survey on Video Scene Parsing:Advances, Challenges, and Prospects](https://mlsanity.com/paper/2506.13552/) (2025)
- [Segment Anything for Video: A Comprehensive Review of Video Object Segmentation and Tracking from Past to Future](https://mlsanity.com/paper/2507.22792/) (2025)
- [SVT: Supertoken Video Transformer for Efficient Video Understanding](https://mlsanity.com/paper/2304.00325/) (2023)
- [MED-VT++: Unifying Multimodal Learning with a Multiscale Encoder-Decoder Video Transformer](https://mlsanity.com/paper/2304.05930/) (2023)
- [Balancing Shared and Task-Specific Representations: A Hybrid Approach to Depth-Aware Video Panoptic Segmentation](https://mlsanity.com/paper/2412.07966/) (2024)

## Abstract (arXiv)

We present TubeFormer-DeepLab, the first attempt to tackle multiple core video segmentation tasks in a unified manner. Different video segmentation tasks (e.g., video semantic/instance/panoptic segmentation) are usually considered as distinct problems. State-of-the-art models adopted in the separate communities have diverged, and radically different approaches dominate in each task. By contrast, we make a crucial observation that video segmentation tasks could be generally formulated as the problem of assigning different predicted labels to video tubes (where a tube is obtained by linking segmentation masks along the time axis) and the labels may encode different values depending on the target task. The observation motivates us to develop TubeFormer-DeepLab, a simple and effective video mask transformer model that is widely applicable to multiple video segmentation tasks. TubeFormer-DeepLab directly predicts video tubes with task-specific labels (either pure semantic categories, or both semantic categories and instance identities), which not only significantly simplifies video segmentation models, but also advances state-of-the-art results on multiple video segmentation benchmarks