1 code implementation • ECCV 2020 • Liyi Chen, Weiwei Wu, Chenchen Fu, Xiao Han, Yuntao Zhang
Weakly supervised semantic segmentation with image-level labels has attracted a lot of attention recently because these labels are already available in most datasets.
1 code implementation • 26 Mar 2024 • Wei Wu, Chao Wang, Dazhong Shen, Chuan Qin, Liyi Chen, Hui Xiong
Collaborative filtering methods based on graph neural networks (GNNs) have witnessed significant success in recommender systems (RS), capitalizing on their ability to capture collaborative signals within intricate user-item relationships via message-passing mechanisms.
no code implementations • 18 Mar 2024 • Yifei Yuan, Chen Shi, Runze Wang, Liyi Chen, Renjun Hu, Zengming Zhang, Feijun Jiang, Wai Lam
To this end, we study low-resource generative conversational query rewrite that is robust to both noise and language style shift.
1 code implementation • CVPR 2023 • Ruihuang Li, Chenhang He, Yabin Zhang, Shuai Li, Liyi Chen, Lei Zhang
Weakly supervised instance segmentation using only bounding box annotations has recently attracted much research attention.
1 code implementation • ICCV 2023 • Liyi Chen, Chenyang Lei, Ruihuang Li, Shuai Li, Zhaoxiang Zhang, Lei Zhang
Without introducing any external supervision and human priors, the proposed FPR effectively suppresses wrong activations from the background objects.
Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation
1 code implementation • 23 Oct 2022 • Yifei Yuan, Chen Shi, Runze Wang, Liyi Chen, Feijun Jiang, Yuan You, Wai Lam
In this paper, we propose the task of multimodal conversational query rewrite (McQR), which performs query rewrite under the multimodal visual conversation setting.
1 code implementation • KDD 2022 • Liyi Chen, Zhi Li, Tong Xu, Han Wu, Zhefeng Wang, Nicholas Jing Yuan, Enhong Chen
To deal with that problem, in this paper, we propose a novel Multi-modal Siamese Network for Entity Alignment (MSNEA) to align entities in different MMKGs, in which multi-modal knowledge could be comprehensively leveraged by the exploitation of inter-modal effect.
Ranked #7 on Multi-modal Entity Alignment on UMVM-oea-d-w-v1 (using extra training data)
1 code implementation • 20 Aug 2020 • Liyi Chen, Zhi Li, Yijun Wang, Tong Xu, Zhefeng Wang, Enhong Chen
To that end, in this paper, we propose a novel solution called Multi-Modal Entity Alignment (MMEA) to address the problem of entity alignment in a multi-modal view.