no code implementations • 15 Apr 2024 • Jiaqi Zhu, Shaofeng Cai, Fang Deng, Junran Wu
However, existing approaches depend on static anomaly prompts that are prone to cross-semantic ambiguity, and prioritize global image-level representations over crucial local pixel-level image-to-text alignment that is necessary for accurate anomaly localization.
1 code implementation • 26 Mar 2024 • He Zhu, Junran Wu, Ruomei Liu, Yue Hou, Ze Yuan, Shangzhe Li, YiCheng Pan, Ke Xu
Existing self-supervised methods in natural language processing (NLP), especially hierarchical text classification (HTC), mainly focus on self-supervised contrastive learning, extremely relying on human-designed augmentation rules to generate contrastive samples, which can potentially corrupt or distort the original information.
no code implementations • 21 Jun 2023 • Zhi Su, Danni Wu, Zhenkun Zhou, Junran Wu, Libo Yin
This paper investigates the significance of consumer opinions in relation to value in China's A-share market.
1 code implementation • 24 May 2023 • He Zhu, Chong Zhang, JunJie Huang, Junran Wu, Ke Xu
Hierarchical text classification (HTC) is a challenging subtask of multi-label classification as the labels form a complex hierarchical structure.
Hierarchical Multi-label Classification Multi-Label Text Classification +1
1 code implementation • 8 May 2023 • Junran Wu, Xueyuan Chen, Bowen Shi, Shangzhe Li, Ke Xu
In contrastive learning, the choice of ``view'' controls the information that the representation captures and influences the performance of the model.
no code implementations • 17 Nov 2022 • Jiaheng Liu, Tong He, Honghui Yang, Rui Su, Jiayi Tian, Junran Wu, Hongcheng Guo, Ke Xu, Wanli Ouyang
Previous top-performing methods for 3D instance segmentation often maintain inter-task dependencies and the tendency towards a lack of robustness.
1 code implementation • 26 Jun 2022 • Junran Wu, Xueyuan Chen, Ke Xu, Shangzhe Li
In addition to SEP, we further design two classification models, SEP-G and SEP-N for graph classification and node classification, respectively.
1 code implementation • 6 Jun 2022 • Junran Wu, Shangzhe Li, Jianhao Li, YiCheng Pan, Ke Xu
Inspired by structural entropy on graphs, we transform the data sample from graphs to coding trees, which is a simpler but essential structure for graph data.
2 code implementations • COLING 2022 • Chong Zhang, He Zhu, Xingyu Peng, Junran Wu, Ke Xu
Inspired by the structural entropy, we construct the coding tree of the graph by minimizing the structural entropy and propose HINT, which aims to make full use of the hierarchical information contained in the text for the task of text classification.
1 code implementation • 5 Sep 2021 • Junran Wu, Jianhao Li, YiCheng Pan, Ke Xu
We then present an implementation of the scheme in a tree kernel and a convolutional network to perform graph classification.
1 code implementation • 4 Jun 2021 • Junran Wu, Ke Xu, Xueyuan Chen, Shangzhe Li, Jichang Zhao
Then, structural information, referring to associations among temporal points and the node weights, is extracted from the mapped graphs to resolve the problems regarding long-range dependencies and the chaotic property.