This project is used to update news related to AOT series frameworks:

• DeAOT: Decoupling Features in Hierarchical Propagation for Video Object Segmentation (NeurIPS 2022, Spotlight) [OpenReview][PDF]
• AOST: Scalable Multi-object Identification for Video Object Segmentation (under review) [PDF]
• AOT: Associating Objects with Transformers for Video Object Segmentation (NeurIPS 2021, Score 8/8/7/8) [OpenReview][PDF]

The implementations of AOT series frameworks can be found below:

1. PyTorch

   • AOT-Benchmark from ZJU, which supports both AOT and DeAOT now. Thanks for such an excellent implementation.

2. PaddlePaddle

   We are preparing an official PaddlePaddle implementation.

• 2023/07: WINNER - DeAOT-based Tracker ranked 1st in the VOTS 2023 challenge (leaderboard). In detail, our DMAOT improves DeAOT by storing object-wise long-term memories instead of frame-wise long-term memories. This avoids the memory growth problem when processing long video sequences and produces better results when handling multiple objects.

• 2023/06: WINNER - DeAOT-based Tracker ranked 1st in two tracks of EPIC-Kitchens challenges (leaderboard). In detail, our MS-DeAOT is a multi-scale version of DeAOT and is the winner of Semi-Supervised Video Object Segmentation (segmentation-based tracking) and TREK-150 Object Tracking (BBox-based tracking). Technical reports are coming soon.

• 2023/04: We are pleased to announce the release of our latest project, Segment and Track Anything (SAM-Track). This innovative project merges two kinds of models, SAM and our DeAOT, to achieve seamless segmentation and efficient tracking of any objects in videos.

• 2022/10: WINNER - AOT-based Tracker ranked 1st in four tracks of the VOT 2022 challenge (presentation of results). In detail, our MS-AOT is the winner of two segmentation tracks, VOT-STs2022 and VOT-RTs2022 (real-time). In addition, the bounding box results of MS-AOT (initialized by AlphaRef, and output is bounding box fitted to mask prediction) surpass the winners of two bounding box tracks, VOT-STb2022 and VOT-RTb2022 (real-time). The bounding box results were required by the organizers after the competition deadline but were highlighted in the workshop presentation (ECCV 2022).

• 2022/10: An improved version of AOT, DeAOT (Decoupling Features in Hierarchical Propagation for Video Object Segmentation), has been accepted by NeurIPS 2022 (Spotlight). DeAOT achieves state-of-the-art accuracy and efficiency on VOS/VOT benchmarks, including YouTube-VOS 2018/2019, DAVIS 2016/2017, and VOT 2020.

• 2022/03: An extension of AOT, AOST (under review), is available now. AOST is a more robust and flexible framework, supporting run-time speed-accuracy trade-offs.

• 2021/10: The conference paper has been accepted by NeurIPS 2021 (score 8/8/7/8, OpenReview).

• 2021/05: WINNER - We ranked 1st in the Track 1 (Video Object Segmentation) of the 3rd Large-scale Video Object Segmentation Challenge.

Although AOT successfully introduces the hierarchical propagation into VOS, the hierarchical propagation can gradually propagate information from past frames to the current frame and transfer the current frame feature from object-agnostic to object-specific, and the increase of object-specific information will inevitably lead to the loss of object-agnostic visual information in deep propagation layers. To solve such a problem and further facilitate the learning of visual embeddings, we propose a Decoupling Features in Hierarchical Propagation (DeAOT) approach to decouple the hierarchical propagation of object-agnostic and object-specific embeddings by handling them in two independent branches. Besides, to compensate for the additional computation from dual-branch propagation, we propose an efficient module for constructing hierarchical propagation, Gated Propagation Module.

previous methods always have static network architectures, which are not flexible enough to adapt to different speed-accuracy requirements. To solve the above problems, we proposed an Associating Objects with Scalable Transformers (AOST) approach to match and segment multiple objects collaboratively with online network scalability. In AOST, a Scalable Long Short-Term Transformer (S-LSTT) is designed to construct hierarchical multi-object associations and enable online adaptation of accuracy-efficiency trade-offs. By further introducing scalable supervision and layer-wise ID-based attention, AOST is not only more flexible but more robust than previous methods.

In AOT, we propose an identification mechanism, which enables us to model, propogate, and segment multiple objects as efficiently as processing a single object. Based on the identification mechanism, the AOT framework is lightweight (with less than 10M parameters in default) yet powerful (achieving SOTA performance). Besides, we propose Long Short-Term Transformer (LSTT) for propogating temporal information hierachically, and the balance of performance and efficiency is convenient by adding or reducing LSTT blocks now in VOS.

Please consider citing the related paper(s) in your publications if it helps your research.

@inproceedings{yang2022deaot,
  title={Decoupling Features in Hierarchical Propagation for Video Object Segmentation},
  author={Yang, Zongxin and Yang, Yi},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2022}
}
@article{yang2021aost,
  title={Scalable Video Object Segmentation with Identification Mechanism},
  author={Yang, Zongxin and Wang, Xiaohan and Miao, Jiaxu and Wei, Yunchao and Wang, Wenguan and Yang, Yi},
  journal={arXiv preprint arXiv:2203.11442},
  year={2023}
}
@inproceedings{yang2021aot,
  title={Associating Objects with Transformers for Video Object Segmentation},
  author={Yang, Zongxin and Wei, Yunchao and Yang, Yi},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2021}
}