# Tracking Anything in High Quality

arXiv: https://arxiv.org/abs/2307.13974 · HTML: https://mlsanity.com/paper/2307.13974/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 39 references that are in the map.
- First posted on arXiv 26 Jul 2023.

## Builds on (most cited first, 27 of 27)

- [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)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [The 2017 DAVIS Challenge on Video Object Segmentation](https://mlsanity.com/paper/1704.00675/) (2017)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions](https://mlsanity.com/paper/2211.05778/) (2022)
- [Video Object Segmentation using Space-Time Memory Networks](https://mlsanity.com/paper/1904.00607/) (2019)
- [Transformer Tracking](https://mlsanity.com/paper/2103.15436/) (2021)
- [YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark](https://mlsanity.com/paper/1809.03327/) (2018)
- [Segment Anything in High Quality](https://mlsanity.com/paper/2306.01567/) (2023)
- [Learning Spatio-Temporal Transformer for Visual Tracking](https://mlsanity.com/paper/2103.17154/) (2021)
- [Learning Discriminative Model Prediction for Tracking](https://mlsanity.com/paper/1904.07220/) (2019)
- [Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework](https://mlsanity.com/paper/2203.11991/) (2022)
- [MixFormer: End-to-End Tracking with Iterative Mixed Attention](https://mlsanity.com/paper/2203.11082/) (2022)
- [ECO: Efficient Convolution Operators for Tracking](https://mlsanity.com/paper/1611.09224/) (2016)
- [One-Shot Video Object Segmentation](https://mlsanity.com/paper/1611.05198/) (2016)
- [Associating Objects with Transformers for Video Object Segmentation](https://mlsanity.com/paper/2106.02638/) (2021)
- [MOTS: Multi-Object Tracking and Segmentation](https://mlsanity.com/paper/1902.03604/) (2019)
- [Siam R-CNN: Visual Tracking by Re-Detection](https://mlsanity.com/paper/1911.12836/) (2019)
- [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)
- [Collaborative Video Object Segmentation by Foreground-Background Integration](https://mlsanity.com/paper/2003.08333/) (2020)
- [SegFlow: Joint Learning for Video Object Segmentation and Optical Flow](https://mlsanity.com/paper/1709.06750/) (2017)
- [BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in Video](https://mlsanity.com/paper/2209.12118/) (2022)
- [Video Object Segmentation with Joint Re-identification and Attention-Aware Mask Propagation](https://mlsanity.com/paper/1803.04242/) (2018)

## Built on by (most cited first, 9 of 9)

- [Single-Model and Any-Modality for Video Object Tracking](https://mlsanity.com/paper/2311.15851/) (2023)
- [Artemis: Towards Referential Understanding in Complex Videos](https://mlsanity.com/paper/2406.00258/) (2024)
- [VideoOrion: Tokenizing Object Dynamics in Videos](https://mlsanity.com/paper/2411.16156/) (2024)
- [Segment Anything for Videos: A Systematic Survey](https://mlsanity.com/paper/2408.08315/) (2024)
- [Strike the Balance: On-the-Fly Uncertainty based User Interactions for Long-Term Video Object Segmentation](https://mlsanity.com/paper/2408.00169/) (2024)
- [E-SAM: Training-Free Segment Every Entity Model](https://mlsanity.com/paper/2503.12094/) (2025)
- [Multi-Granularity Video Object Segmentation](https://mlsanity.com/paper/2412.01471/) (2024)
- [CRTrack: Low-Light Semi-Supervised Multi-object Tracking Based on Consistency Regularization](https://mlsanity.com/paper/2502.16809/) (2025)
- [`Attention-Guided Cross-Temporal Clustering for Self-Supervised Video Object Segmentation](https://mlsanity.com/paper/2607.07230/) (2026)

## Abstract (arXiv)

Visual object tracking is a fundamental video task in computer vision. Recently, the notably increasing power of perception algorithms allows the unification of single/multiobject and box/mask-based tracking. Among them, the Segment Anything Model (SAM) attracts much attention. In this report, we propose HQTrack, a framework for High Quality Tracking anything in videos. HQTrack mainly consists of a video multi-object segmenter (VMOS) and a mask refiner (MR). Given the object to be tracked in the initial frame of a video, VMOS propagates the object masks to the current frame. The mask results at this stage are not accurate enough since VMOS is trained on several closeset video object segmentation (VOS) datasets, which has limited ability to generalize to complex and corner scenes. To further improve the quality of tracking masks, a pretrained MR model is employed to refine the tracking results. As a compelling testament to the effectiveness of our paradigm, without employing any tricks such as test-time data augmentations and model ensemble, HQTrack ranks the 2nd place in the Visual Object Tracking and Segmentation (VOTS2023) challenge. Code and models are available at https://github.com/jiawen-zhu/HQTrack.