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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Motion-Attentive Transition for Zero-Shot Video Object Segmentation

February 1, 2023

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Authors

Tianfei Zhou

Inception Institute of Artifical Intelligence


Shunzhou Wang

Beijing Institute of Technology


Yi Zhou

Inception Institute of Artifical Intelligence


Yazhou Yao

Nanjing University of Science and Technology


Jianwu Li

Beijing Institute of Technology


Ling Shao

Inception Institute of Artifical Intelligence


DOI:

10.1609/aaai.v34i07.7008


Abstract:

In this paper, we present a novel Motion-Attentive Transition Network (MATNet) for zero-shot video object segmentation, which provides a new way of leveraging motion information to reinforce spatio-temporal object representation. An asymmetric attention block, called Motion-Attentive Transition (MAT), is designed within a two-stream encoder, which transforms appearance features into motion-attentive representations at each convolutional stage. In this way, the encoder becomes deeply interleaved, allowing for closely hierarchical interactions between object motion and appearance. This is superior to the typical two-stream architecture, which treats motion and appearance separately in each stream and often suffers from overfitting to appearance information. Additionally, a bridge network is proposed to obtain a compact, discriminative and scale-sensitive representation for multi-level encoder features, which is further fed into a decoder to achieve segmentation results. Extensive experiments on three challenging public benchmarks (i.e., DAVIS-16, FBMS and Youtube-Objects) show that our model achieves compelling performance against the state-of-the-arts. Code is available at: https://github.com/tfzhou/MATNet.

Topics: AAAI

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HOW TO CITE:

Tianfei Zhou||Shunzhou Wang||Yi Zhou||Yazhou Yao||Jianwu Li||Ling Shao Motion-Attentive Transition for Zero-Shot Video Object Segmentation Proceedings of the AAAI Conference on Artificial Intelligence (2020) 13066-13073.

Tianfei Zhou||Shunzhou Wang||Yi Zhou||Yazhou Yao||Jianwu Li||Ling Shao Motion-Attentive Transition for Zero-Shot Video Object Segmentation AAAI 2020, 13066-13073.

Tianfei Zhou||Shunzhou Wang||Yi Zhou||Yazhou Yao||Jianwu Li||Ling Shao (2020). Motion-Attentive Transition for Zero-Shot Video Object Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 13066-13073.

Tianfei Zhou||Shunzhou Wang||Yi Zhou||Yazhou Yao||Jianwu Li||Ling Shao. Motion-Attentive Transition for Zero-Shot Video Object Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.13066-13073.

Tianfei Zhou||Shunzhou Wang||Yi Zhou||Yazhou Yao||Jianwu Li||Ling Shao. 2020. Motion-Attentive Transition for Zero-Shot Video Object Segmentation. "Proceedings of the AAAI Conference on Artificial Intelligence". 13066-13073.

Tianfei Zhou||Shunzhou Wang||Yi Zhou||Yazhou Yao||Jianwu Li||Ling Shao. (2020) "Motion-Attentive Transition for Zero-Shot Video Object Segmentation", Proceedings of the AAAI Conference on Artificial Intelligence, p.13066-13073

Tianfei Zhou||Shunzhou Wang||Yi Zhou||Yazhou Yao||Jianwu Li||Ling Shao, "Motion-Attentive Transition for Zero-Shot Video Object Segmentation", AAAI, p.13066-13073, 2020.

Tianfei Zhou||Shunzhou Wang||Yi Zhou||Yazhou Yao||Jianwu Li||Ling Shao. "Motion-Attentive Transition for Zero-Shot Video Object Segmentation". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.13066-13073.

Tianfei Zhou||Shunzhou Wang||Yi Zhou||Yazhou Yao||Jianwu Li||Ling Shao. "Motion-Attentive Transition for Zero-Shot Video Object Segmentation". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 13066-13073.

Tianfei Zhou||Shunzhou Wang||Yi Zhou||Yazhou Yao||Jianwu Li||Ling Shao. Motion-Attentive Transition for Zero-Shot Video Object Segmentation. AAAI[Internet]. 2020[cited 2023]; 13066-13073.


ISSN: 2374-3468


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