# NOVIS: A Case for End-to-End Near-Online Video Instance Segmentation

arXiv: https://arxiv.org/abs/2308.15266 · HTML: https://mlsanity.com/paper/2308.15266/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 30 references that are in the map.
- First posted on arXiv 29 Aug 2023.

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

- [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)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [End-to-End Video Instance Segmentation with Transformers](https://mlsanity.com/paper/2011.14503/) (2020)
- [PointRend: Image Segmentation as Rendering](https://mlsanity.com/paper/1912.08193/) (2019)
- [Video Instance Segmentation](https://mlsanity.com/paper/1905.04804/) (2019)
- [YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark](https://mlsanity.com/paper/1809.03327/) (2018)
- [Mask2Former for Video Instance Segmentation](https://mlsanity.com/paper/2112.10764/) (2021)
- [Occluded Video Instance Segmentation: A Benchmark](https://mlsanity.com/paper/2102.01558/) (2021)
- [Classifying, Segmenting, and Tracking Object Instances in Video with Mask Propagation](https://mlsanity.com/paper/1912.04573/) (2019)
- [STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videos](https://mlsanity.com/paper/2003.08429/) (2020)
- [SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation](https://mlsanity.com/paper/2007.14772/) (2020)
- [Video Instance Segmentation using Inter-Frame Communication Transformers](https://mlsanity.com/paper/2106.03299/) (2021)
- [SeqFormer: Sequential Transformer for Video Instance Segmentation](https://mlsanity.com/paper/2112.08275/) (2021)
- [In Defense of Online Models for Video Instance Segmentation](https://mlsanity.com/paper/2207.10661/) (2022)
- [VITA: Video Instance Segmentation via Object Token Association](https://mlsanity.com/paper/2206.04403/) (2022)
- [MinVIS: A Minimal Video Instance Segmentation Framework without Video-based Training](https://mlsanity.com/paper/2208.02245/) (2022)
- [SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation](https://mlsanity.com/paper/2103.10284/) (2021)
- [Crossover Learning for Fast Online Video Instance Segmentation](https://mlsanity.com/paper/2104.05970/) (2021)
- [TransVOD: End-to-End Video Object Detection with Spatial-Temporal Transformers](https://mlsanity.com/paper/2201.05047/) (2022)
- [Video Instance Segmentation with a Propose-Reduce Paradigm](https://mlsanity.com/paper/2103.13746/) (2021)
- [Temporally Efficient Vision Transformer for Video Instance Segmentation](https://mlsanity.com/paper/2204.08412/) (2022)
- [CompFeat: Comprehensive Feature Aggregation for Video Instance Segmentation](https://mlsanity.com/paper/2012.03400/) (2020)
- [Spatial Feature Calibration and Temporal Fusion for Effective One-stage Video Instance Segmentation](https://mlsanity.com/paper/2104.05606/) (2021)
- [VISOLO: Grid-Based Space-Time Aggregation for Efficient Online Video Instance Segmentation](https://mlsanity.com/paper/2112.04177/) (2021)
- [DeVIS: Making Deformable Transformers Work for Video Instance Segmentation](https://mlsanity.com/paper/2207.11103/) (2022)

## Built on by (most cited first, 8 of 8)

- [General Object Foundation Model for Images and Videos at Scale](https://mlsanity.com/paper/2312.09158/) (2023)
- [DVIS++: Improved Decoupled Framework for Universal Video Segmentation](https://mlsanity.com/paper/2312.13305/) (2023)
- [DVIS-DAQ: Improving Video Segmentation via Dynamic Anchor Queries](https://mlsanity.com/paper/2404.00086/) (2024)
- [Unified Embedding Alignment for Open-Vocabulary Video Instance Segmentation](https://mlsanity.com/paper/2407.07427/) (2024)
- [Trackastra: Transformer-based cell tracking for live-cell microscopy](https://mlsanity.com/paper/2405.15700/) (2024)
- [Latest Object Memory Management for Temporally Consistent Video Instance Segmentation](https://mlsanity.com/paper/2507.19754/) (2025)
- [Multi-Granularity Video Object Segmentation](https://mlsanity.com/paper/2412.01471/) (2024)
- [QueenVIS: Rethinking Image-Only Training for Video Instance Segmentation via Query Enrichment](https://mlsanity.com/paper/2607.24598/) (2026)

## Abstract (arXiv)

Until recently, the Video Instance Segmentation (VIS) community operated under the common belief that offline methods are generally superior to a frame by frame online processing. However, the recent success of online methods questions this belief, in particular, for challenging and long video sequences. We understand this work as a rebuttal of those recent observations and an appeal to the community to focus on dedicated near-online VIS approaches. To support our argument, we present a detailed analysis on different processing paradigms and the new end-to-end trainable NOVIS (Near-Online Video Instance Segmentation) method. Our transformer-based model directly predicts spatio-temporal mask volumes for clips of frames and performs instance tracking between clips via overlap embeddings. NOVIS represents the first near-online VIS approach which avoids any handcrafted tracking heuristics. We outperform all existing VIS methods by large margins and provide new state-of-the-art results on both YouTube-VIS (2019/2021) and the OVIS benchmarks.