no code implementations • 1 Apr 2024 • Shuaiyi Huang, De-An Huang, Zhiding Yu, Shiyi Lan, Subhashree Radhakrishnan, Jose M. Alvarez, Abhinav Shrivastava, Anima Anandkumar
Video instance segmentation (VIS) is a challenging vision task that aims to detect, segment, and track objects in videos.
no code implementations • CVPR 2023 • Bo He, Xitong Yang, Hanyu Wang, Zuxuan Wu, Hao Chen, Shuaiyi Huang, Yixuan Ren, Ser-Nam Lim, Abhinav Shrivastava
Implicit neural representations (INR) have gained increasing attention in representing 3D scenes and images, and have been recently applied to encode videos (e. g., NeRV, E-NeRV).
1 code implementation • 15 Aug 2022 • Shuaiyi Huang, Luyu Yang, Bo He, Songyang Zhang, Xuming He, Abhinav Shrivastava
In this paper, we aim to address the challenge of label sparsity in semantic correspondence by enriching supervision signals from sparse keypoint annotations.
no code implementations • 25 May 2022 • Shramay Palta, Haozhe An, Yifan Yang, Shuaiyi Huang, Maharshi Gor
Retrieval based open-domain QA systems use retrieved documents and answer-span selection over retrieved documents to find best-answer candidates.
no code implementations • 25 Aug 2020 • Shuaiyi Huang, Qiuyue Wang, Xuming He
We are the first that exploit confidence during refinement to improve semantic matching accuracy and develop an end-to-end self-supervised adversarial learning procedure for the entire matching network.
1 code implementation • ICCV 2019 • Shuaiyi Huang, Qiuyue Wang, Songyang Zhang, Shipeng Yan, Xuming He
We instantiate our strategy by designing an end-to-end learnable deep network, named as Dynamic Context Correspondence Network (DCCNet).
1 code implementation • ICCV 2017 • Chen Zhu, Yanpeng Zhao, Shuaiyi Huang, Kewei Tu, Yi Ma
In this paper, we demonstrate the importance of encoding such relations by showing the limited effective receptive field of ResNet on two datasets, and propose to model the visual attention as a multivariate distribution over a grid-structured Conditional Random Field on image regions.