no code implementations • 8 Oct 2023 • Yu-Huan Wu, Shi-Chen Zhang, Yun Liu, Le Zhang, Xin Zhan, Daquan Zhou, Jiashi Feng, Ming-Ming Cheng, Liangli Zhen
Semantic segmentation tasks naturally require high-resolution information for pixel-wise segmentation and global context information for class prediction.
1 code implementation • 6 Jul 2023 • Yun Liu, Yu-Huan Wu, Shi-Chen Zhang, Li Liu, Min Wu, Ming-Ming Cheng
This dataset enables the training of sophisticated detectors for high-quality CTD.
no code implementations • 18 Aug 2022 • Yu-Huan Wu, Da Zhang, Le Zhang, Xin Zhan, Dengxin Dai, Yun Liu, Ming-Ming Cheng
Current efficient LiDAR-based detection frameworks are lacking in exploiting object relations, which naturally present in both spatial and temporal manners.
no code implementations • CVPR 2022 • Yunlong Wang, Hongyu Pan, Jun Zhu, Yu-Huan Wu, Xin Zhan, Kun Jiang, Diange Yang
In this paper, we propose a novel Spatial-Temporal Integrated network with Bidirectional Enhancement, BE-STI, to improve the temporal motion prediction performance by spatial semantic features, which points out an efficient way to combine semantic segmentation and motion prediction.
4 code implementations • 22 Jun 2021 • Yu-Huan Wu, Yun Liu, Xin Zhan, Ming-Ming Cheng
A popular solution to this problem is to use a single pooling operation to reduce the sequence length.
Ranked #4 on RGB Salient Object Detection on DUTS-TE (max F-measure metric)
3 code implementations • 6 Jun 2021 • Yun Liu, Yu-Huan Wu, Guolei Sun, Le Zhang, Ajad Chhatkuli, Luc van Gool
This paper tackles the high computational/space complexity associated with Multi-Head Self-Attention (MHSA) in vanilla vision transformers.
1 code implementation • 24 Dec 2020 • Yu-Huan Wu, Yun Liu, Jun Xu, Jia-Wang Bian, Yu-Chao Gu, Ming-Ming Cheng
Therefore, we propose an implicit depth restoration (IDR) technique to strengthen the mobile networks' feature representation capability for RGB-D SOD.
1 code implementation • 24 Dec 2020 • Yu-Huan Wu, Yun Liu, Le Zhang, Ming-Ming Cheng, Bo Ren
In this paper, we tap into this gap and show that enhancing high- level features is essential for SOD as well.
no code implementations • CVPR 2021 • Yu-Chao Gu, Li-Juan Wang, Yun Liu, Yi Yang, Yu-Huan Wu, Shao-Ping Lu, Ming-Ming Cheng
DARTS mainly focuses on the operation search and derives the cell topology from the operation weights.
1 code implementation • 10 Sep 2020 • Yun Liu, Yu-Huan Wu, Pei-Song Wen, Yu-Jun Shi, Yu Qiu, Ming-Ming Cheng
For each proposal, this MIL framework can simultaneously compute probability distributions and category-aware semantic features, with which we can formulate a large undirected graph.
Image-level Supervised Instance Segmentation Multiple Instance Learning +3
1 code implementation • 28 Aug 2020 • Yu-Huan Wu, Yun Liu, Le Zhang, Wang Gao, Ming-Ming Cheng
Much of the recent efforts on salient object detection (SOD) have been devoted to producing accurate saliency maps without being aware of their instance labels.
1 code implementation • 15 Apr 2020 • Yu-Huan Wu, Shang-Hua Gao, Jie Mei, Jun Xu, Deng-Ping Fan, Rong-Guo Zhang, Ming-Ming Cheng
The chest CT scan test provides a valuable complementary tool to the RT-PCR test, and it can identify the patients in the early-stage with high sensitivity.
no code implementations • 26 Aug 2019 • Jia-Wang Bian, Yu-Huan Wu, Ji Zhao, Yun Liu, Le Zhang, Ming-Ming Cheng, Ian Reid
According to this, we propose three high-quality matching systems and a Coarse-to-Fine RANSAC estimator.
1 code implementation • ICCV 2019 • Deng-Ping Fan, Shengchuan Zhang, Yu-Huan Wu, Yun Liu, Ming-Ming Cheng, Bo Ren, Paul L. Rosin, Rongrong Ji
In this paper, we design a perceptual metric, called Structure Co-Occurrence Texture (Scoot), which simultaneously considers the block-level spatial structure and co-occurrence texture statistics.
1 code implementation • 9 Apr 2018 • Deng-Ping Fan, Shengchuan Zhang, Yu-Huan Wu, Ming-Ming Cheng, Bo Ren, Rongrong Ji, Paul L. Rosin
However, human perception of the similarity of two sketches will consider both structure and texture as essential factors and is not sensitive to slight ("pixel-level") mismatches.