no code implementations • 2 Dec 2022 • Dianwen Mei, Wei Zhuo, Jiandong Tian, Guangming Lu, Wenjie Pei
To circumvent these two challenges, we propose to activate the discriminability of novel classes explicitly in both the feature encoding stage and the prediction stage for segmentation.
1 code implementation • 19 May 2022 • Beibei Li, Beihong Jin, Jiageng Song, Yisong Yu, Yiyuan Zheng, Wei Zhuo
With the rapid increase of micro-video creators and viewers, how to make personalized recommendations from a large number of candidates to viewers begins to attract more and more attention.
no code implementations • 24 Feb 2022 • YuAn Wang, Wei Zhuo, Yucong Li, Zhi Wang, Qi Ju, Wenwu Zhu
To solve this problem, we proposed a bootstrapped training scheme for semantic segmentation, which fully leveraged the global semantic knowledge for self-supervision with our proposed PGG strategy and CAE module.
Ranked #2 on
Unsupervised Semantic Segmentation
on COCO-Stuff
no code implementations • 19 Nov 2021 • Wei Zhuo, Chenyun Yu, Guang Tan
Graph Neural Networks (GNNs) have received increasing attention for representation learning in various machine learning tasks.
1 code implementation • ICCV 2021 • Haoxi Ran, Wei Zhuo, Jun Liu, Li Lu
We further verify the expandability of RPNet, in terms of both depth and width, on the tasks of classification and segmentation.
Ranked #9 on
Semantic Segmentation
on ScanNet
no code implementations • 27 Jun 2021 • Wei Zhuo, Kunchi Liu, Taofeng Xue, Beihong Jin, Beibei Li, Xinzhou Dong, He Chen, Wenhai Pan, Xuejian Zhang, Shuo Zhou
Interactions between users and videos are the major data source of performing video recommendation.
no code implementations • 23 Jun 2021 • Zejian Chen, Wei Zhuo, Tianfu Wang, Wufeng Xue, Dong Ni
Based on the continuity between slices/frames and the common spatial layout of organs across volumes/sequences, we introduced a novel bootstrap self-supervised representation learning method by leveraging the predictable possibility of neighboring slices.
2 code implementations • CVPR 2022 • Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi, Yang Gao
In this work, we first construct a strong baseline of self-training (namely ST) for semi-supervised semantic segmentation via injecting strong data augmentations (SDA) on unlabeled images to alleviate overfitting noisy labels as well as decouple similar predictions between the teacher and student.
no code implementations • 7 Jun 2021 • Wei Zhuo, Guang Tan
Not restricted by connectivity in the original graph, the generated views allow the model to enhance its expressive power with new and complementary perspectives from which to look at the relationship between nodes.
1 code implementation • ICCV 2021 • Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi, Yang Gao
Our method aims to alleviate this problem and enhance the feature embedding on latent novel classes.
Ranked #28 on
Few-Shot Semantic Segmentation
on PASCAL-5i (5-Shot)
no code implementations • 10 Mar 2021 • Xinzhou Dong, Beihong Jin, Wei Zhuo, Beibei Li, Taofeng Xue
Many practical recommender systems provide item recommendation for different users only via mining user-item interactions but totally ignoring the rich attribute information of items that users interact with.
3 code implementations • CVPR 2020 • Qi Fan, Wei Zhuo, Chi-Keung Tang, Yu-Wing Tai
To train our network, we contribute a new dataset that contains 1000 categories of various objects with high-quality annotations.
Ranked #17 on
Few-Shot Object Detection
on MS-COCO (10-shot)
no code implementations • CVPR 2017 • Wei Zhuo, Mathieu Salzmann, Xuming He, Miaomiao Liu
In particular, while some of them aim at segmenting the image into regions, such as object or surface instances, others aim at inferring the semantic labels of given regions, or their support relationships.
no code implementations • CVPR 2015 • Wei Zhuo, Mathieu Salzmann, Xuming He, Miaomiao Liu
We tackle the problem of single image depth estimation, which, without additional knowledge, suffers from many ambiguities.