217 citations · 269 across the 9 of their papers we have counts for
10 papers
Unified Mask Embedding and Correspondence Learning for Self-Supervised Video Segmentation
Liulei Li, Wenguan Wang, Tianfei Zhou +2
The objective of this paper is self-supervised learning of video object segmentation. We develop a unified framework which simultaneously models cross-frame dense correspondence fo…
Deep Hierarchical Semantic Segmentation
Liulei Li, Tianfei Zhou, Wenguan Wang +2
Humans are able to recognize structured relations in observation, allowing us to decompose complex scenes into simpler parts and abstract the visual world in multiple levels. Howev…
Regional Semantic Contrast and Aggregation for Weakly Supervised Semantic Segmentation
Tianfei Zhou, Meijie Zhang, Fang Zhao +1
Learning semantic segmentation from weakly-labeled (e.g., image tags only) data is challenging since it is hard to infer dense object regions from sparse semantic tags. Despite bei…
Handwritten Mathematical Expression Recognition via Attention Aggregation based Bi-directional Mutual Learning
Xiaohang Bian, Bo Qin, Xiaozhe Xin +3
Handwritten mathematical expression recognition aims to automatically generate LaTeX sequences from given images. Currently, attention-based encoder-decoder models are widely used…
Quality-Aware Memory Network for Interactive Volumetric Image Segmentation
Tianfei Zhou, Liulei Li, Gustav Bredell +2
Despite recent progress of automatic medical image segmentation techniques, fully automatic results usually fail to meet the clinical use and typically require further refinement.…
Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation
Tianfei Zhou, Jianwu Li, Xueyi Li +1
This paper addresses the task of unsupervised video multi-object segmentation. Current approaches follow a two-stage paradigm: 1) detect object proposals using pre-trained Mask R-C…
Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation
Xueyi Li, Tianfei Zhou, Jianwu Li +2
Acquiring sufficient ground-truth supervision to train deep visual models has been a bottleneck over the years due to the data-hungry nature of deep learning. This is exacerbated i…
DTDN: Dual-task De-raining Network
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. It needs to address two mutually exclusive objecti…
Motion-Attentive Transition for Zero-Shot Video Object Segmentation
Tianfei Zhou, Shunzhou Wang, Yi Zhou +3
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…
Less Memory, Faster Speed: Refining Self-Attention Module for Image Reconstruction
Zheng Wang, Jianwu Li, Ge Song +1
Self-attention (SA) mechanisms can capture effectively global dependencies in deep neural networks, and have been applied to natural language processing and image processing succes…