no code implementations • ACL 2022 • Shuai Zhang, Yongliang Shen, Zeqi Tan, Yiquan Wu, Weiming Lu
Named entity recognition (NER) is a fundamental task to recognize specific types of entities from a given sentence.
no code implementations • EMNLP 2020 • Yiquan Wu, Kun Kuang, Yating Zhang, Xiaozhong Liu, Changlong Sun, Jun Xiao, Yueting Zhuang, Luo Si, Fei Wu
Court{'}s view generation is a novel but essential task for legal AI, aiming at improving the interpretability of judgment prediction results and enabling automatic legal document generation.
1 code implementation • 7 Mar 2024 • Ang Li, Qiangchao Chen, Yiquan Wu, Ming Cai, Xiang Zhou, Fei Wu, Kun Kuang
In this paper, we introduce a novel From Graph to Word Bag (FWGB) approach, which introduces domain knowledge regarding constituent elements to guide the model in making judgments on confusing charges, much like a judge's reasoning process.
1 code implementation • 7 Mar 2024 • Ang Li, Yiquan Wu, Yifei Liu, Fei Wu, Ming Cai, Kun Kuang
Court View Generation (CVG) is a challenging task in the field of Legal Artificial Intelligence (LegalAI), which aims to generate court views based on the plaintiff claims and the fact descriptions.
no code implementations • 16 Jan 2024 • Huafeng Qin, Yiquan Wu, Mounim A. El-Yacoubi, Jun Wang, Guangxiang Yang
To overcome this problem, in this paper, we propose an adversarial masking contrastive learning (AMCL) approach, that generates challenging samples to train a more robust contrastive learning model for the downstream palm-vein recognition task, by alternatively optimizing the encoder in the contrastive learning model and a set of latent variables.
no code implementations • 13 Oct 2023 • Yiquan Wu, Siying Zhou, Yifei Liu, Weiming Lu, Xiaozhong Liu, Yating Zhang, Changlong Sun, Fei Wu, Kun Kuang
Precedents are the previous legal cases with similar facts, which are the basis for the judgment of the subsequent case in national legal systems.
2 code implementations • 15 Dec 2020 • Yimian Dai, Yiquan Wu, Fei Zhou, Kobus Barnard
To mitigate the issue of minimal intrinsic features for pure data-driven methods, in this paper, we propose a novel model-driven deep network for infrared small target detection, which combines discriminative networks and conventional model-driven methods to make use of both labeled data and the domain knowledge.
4 code implementations • 30 Sep 2020 • Yimian Dai, Yiquan Wu, Fei Zhou, Kobus Barnard
Single-frame infrared small target detection remains a challenge not only due to the scarcity of intrinsic target characteristics but also because of lacking a public dataset.
2 code implementations • 29 Sep 2020 • Yimian Dai, Fabian Gieseke, Stefan Oehmcke, Yiquan Wu, Kobus Barnard
Feature fusion, the combination of features from different layers or branches, is an omnipresent part of modern network architectures.
Ranked #655 on Image Classification on <h2>oi</h2>
1 code implementation • 15 Jul 2020 • Yimian Dai, Stefan Oehmcke, Fabian Gieseke, Yiquan Wu, Kobus Barnard
Inspired by their similarity, we propose a novel type of activation units called attentional activation (ATAC) units as a unification of activation functions and attention mechanisms.
1 code implementation • 27 Mar 2017 • Yimian Dai, Yiquan Wu
They work well on the images with homogeneous backgrounds and high-contrast targets.
no code implementations • 24 Oct 2016 • Yu Song, Yiquan Wu
This problem is solved in the low rank subspace clustering model which decomposes the corrupted data matrix as the sum of a clean and self-expressive dictionary plus a matrix of noise and gross errors.
no code implementations • 12 Oct 2016 • Yu Song, Yiquan Wu
Subspace clustering refers to the problem of segmenting a set of data points approximately drawn from a union of multiple linear subspaces.