1 code implementation • CVPR 2023 • Liulei Li, Wenguan Wang, Tianfei Zhou, Jianwu Li, Yi Yang
The objective of this paper is self-supervised learning of video object segmentation.
3 code implementations • CVPR 2022 • Liulei Li, Tianfei Zhou, Wenguan Wang, Jianwu Li, Yi Yang
In this paper, we instead address hierarchical semantic segmentation (HSS), which aims at structured, pixel-wise description of visual observation in terms of a class hierarchy.
1 code implementation • 27 Mar 2022 • Liulei Li, Tianfei Zhou, Wenguan Wang, Lu Yang, Jianwu Li, Yi Yang
Our target is to learn visual correspondence from unlabeled videos.
1 code implementation • CVPR 2022 • Tianfei Zhou, Meijie Zhang, Fang Zhao, Jianwu Li
Particularly, we propose i) semantic contrast to drive network learning by contrasting massive categorical object regions, leading to a more holistic object pattern understanding, and ii) semantic aggregation to gather diverse relational contexts in the memory to enrich semantic representations.
no code implementations • CVPR 2022 • Liulei Li, Tianfei Zhou, Wenguan Wang, Lu Yang, Jianwu Li, Yi Yang
Our target is to learn visual correspondence from unlabeled videos.
1 code implementation • 7 Dec 2021 • Xiaohang Bian, Bo Qin, Xiaozhe Xin, Jianwu Li, Xuefeng Su, Yanfeng Wang
Handwritten mathematical expression recognition aims to automatically generate LaTeX sequences from given images.
1 code implementation • journal 2021 • Tianfei Zhou, Liulei Li, Xueyi Li, Chun-Mei Feng, Jianwu Li, Ling Shao
The framework explicitly encodes semantic dependencies in a group of images to discover rich semantic context for estimating more reliable pseudo ground-truths, which are subsequently employed to train more effective segmentation models.
1 code implementation • 20 Jun 2021 • Tianfei Zhou, Liulei Li, Gustav Bredell, Jianwu Li, Ender Konukoglu
The proposed network has two appealing characteristics: 1) The memory-augmented network offers the ability to quickly encode past segmentation information, which will be retrieved for the segmentation of other slices; 2) The quality assessment module enables the model to directly estimate the qualities of segmentation predictions, which allows an active learning paradigm where users preferentially label the lowest-quality slice for multi-round refinement.
no code implementations • CVPR 2021 • Tianfei Zhou, Jianwu Li, Xueyi Li, Ling Shao
To address this, we introduce a novel approach for more accurate and efficient spatio-temporal segmentation.
1 code implementation • 9 Dec 2020 • Xueyi Li, Tianfei Zhou, Jianwu Li, Yi Zhou, Zhaoxiang Zhang
We formulate WSSS as a novel group-wise learning task that explicitly models semantic dependencies in a group of images to estimate more reliable pseudo ground-truths, which can be used for training more accurate segmentation models.
Ranked #37 on Weakly-Supervised Semantic Segmentation on COCO 2014 val (using extra training data)
1 code implementation • 21 Aug 2020 • Zheng Wang, Jianwu Li, Ge Song
Removing rain streaks from rainy images is necessary for many tasks in computer vision, such as object detection and recognition.
1 code implementation • 9 Mar 2020 • Tianfei Zhou, Shunzhou Wang, Yi Zhou, Yazhou Yao, Jianwu Li, Ling Shao
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.
Ranked #9 on Unsupervised Video Object Segmentation on FBMS test
no code implementations • 20 May 2019 • Zheng Wang, Jianwu Li, Ge Song, Tieling Li
Self-attention (SA) mechanisms can capture effectively global dependencies in deep neural networks, and have been applied to natural language processing and image processing successfully.