no code implementations • SemEval (NAACL) 2022 • Ze Chen, Kangxu Wang, Jiewen Zheng, Zijian Cai, Jiarong He, Jin Gao
This article describes the OPDAI submission to SemEval-2022 Task 11 on Chinese complex NER.
1 code implementation • 27 May 2025 • Ze Chen, Shaode Yu
Kolmogorov-Arnold Network (KAN) has attracted growing interest for its strong function approximation capability.
1 code implementation • 13 Dec 2024 • Yang Qin, Chao Chen, Zhihang Fu, Ze Chen, Dezhong Peng, Peng Hu, Jieping Ye
To address this challenge, we propose a novel RObust mUltitask Tuning and collaboration mEthod (ROUTE) to improve the comprehensive capabilities of open-source LLMs for Text2SQL, thereby providing a more practical solution.
1 code implementation • 9 Dec 2024 • Zitong Huang, Ze Chen, Yuanze Li, Bowen Dong, Erjin Zhou, Yong liu, Rick Siow Mong Goh, Chun-Mei Feng, WangMeng Zuo
Then for each cluster, we employ greedy selection strategy to ensure that the Gaussian distribution of the sampled features closely matches the Gaussian distribution of all unlabeled features within the cluster.
1 code implementation • 12 Sep 2024 • Shaode Yu, Ze Chen, Zhimu Yang, Jiacheng Gu, Bizu Feng
Score prediction is crucial in evaluating realistic image sharpness based on collected informative features.
1 code implementation • 23 Jul 2024 • Kai Liu, Zhihang Fu, Sheng Jin, Chao Chen, Ze Chen, Rongxin Jiang, Fan Zhou, Yaowu Chen, Jieping Ye
Detecting and rejecting unknown out-of-distribution (OOD) samples is critical for deployed neural networks to void unreliable predictions.
1 code implementation • 23 Jul 2024 • Kai Liu, Zhihang Fu, Sheng Jin, Ze Chen, Fan Zhou, Rongxin Jiang, Yaowu Chen, Jieping Ye
The resulting Efficient Small Object Detection (ESOD) approach is a generic framework, which can be applied to both CNN- and ViT-based detectors to save the computation and GPU memory costs.
1 code implementation • 23 Jul 2024 • Kai Liu, Ze Chen, Zhihang Fu, Rongxin Jiang, Fan Zhou, Yaowu Chen, Yue Wu, Jieping Ye
Remarkably, our method demonstrates the potential of comparable improvement against the state-of-the-art MMedLM2 on MMedBench, while significantly reducing the training costs to 5%.
1 code implementation • NeurIPS 2023 • Kai Liu, Zhihang Fu, Chao Chen, Sheng Jin, Ze Chen, Mingyuan Tao, Rongxin Jiang, Jieping Ye
The key to OOD detection has two aspects: generalized feature representation and precise category description.
1 code implementation • 10 Jun 2024 • Peng Xia, Ze Chen, Juanxi Tian, Yangrui Gong, Ruibo Hou, Yue Xu, Zhenbang Wu, Zhiyuan Fan, Yiyang Zhou, Kangyu Zhu, Wenhao Zheng, Zhaoyang Wang, Xiao Wang, Xuchao Zhang, Chetan Bansal, Marc Niethammer, Junzhou Huang, Hongtu Zhu, Yun Li, Jimeng Sun, ZongYuan Ge, Gang Li, James Zou, Huaxiu Yao
Artificial intelligence has significantly impacted medical applications, particularly with the advent of Medical Large Vision Language Models (Med-LVLMs), sparking optimism for the future of automated and personalized healthcare.
no code implementations • 25 Apr 2024 • Zitong Huang, Ze Chen, Bowen Dong, Chaoqi Liang, Erjin Zhou, WangMeng Zuo
Model Weight Averaging (MWA) is a technique that seeks to enhance model's performance by averaging the weights of multiple trained models.
no code implementations • 22 Mar 2024 • Ze Chen, Gongyu Zhang, Jiayu Huo, Joan Nunez do Rio, Charalampos Komninos, Yang Liu, Rachel Sparks, Sebastien Ourselin, Christos Bergeles, Timothy Jackson
This study introduces a novel framework for enhancing domain generalization in medical imaging, specifically focusing on utilizing unlabelled multi-view colour fundus photographs.
no code implementations • 20 Feb 2024 • Chengcheng Wei, Ze Chen, Songtan Fang, Jiarong He, Max Gao
This paper mainly describes a unified system for hallucination detection of LLMs, which wins the second prize in the model-agnostic track of the SemEval-2024 Task 6, and also achieves considerable results in the model-aware track.
1 code implementation • 6 Feb 2024 • Chao Chen, Kai Liu, Ze Chen, Yi Gu, Yue Wu, Mingyuan Tao, Zhihang Fu, Jieping Ye
Knowledge hallucination have raised widespread concerns for the security and reliability of deployed LLMs.
no code implementations • NeurIPS 2023 • Chao Chen, Zhihang Fu, Kai Liu, Ze Chen, Mingyuan Tao, Jieping Ye
Most existing OOD detection methods focused on exploring advanced training skills or training-free tricks to prevent the model from yielding overconfident confidence score for unknown samples.
1 code implementation • 3 Jan 2024 • Zitong Huang, Ze Chen, Zhixing Chen, Erjin Zhou, Xinxing Xu, Rick Siow Mong Goh, Yong liu, WangMeng Zuo, ChunMei Feng
When progressing to a new session, pseudo-features are sampled from old-class distributions combined with training images of the current session to optimize the prompt, thus enabling the model to learn new knowledge while retaining old knowledge.
class-incremental learning
Few-Shot Class-Incremental Learning
+2
1 code implementation • ICCV 2023 • Kai Liu, Sheng Jin, Zhihang Fu, Ze Chen, Rongxin Jiang, Jieping Ye
The resulting accurate pseudo-tracklets boost learning the feature consistency.
no code implementations • 11 Apr 2023 • Jiewen Zheng, Ze Chen
Sentence-level relation extraction aims to identify the relation between two entities for a given sentence.
no code implementations • 14 Feb 2023 • Qi Zhang, Zijian Yang, Yilun Huang, Ze Chen, Zijian Cai, Kangxu Wang, Jiewen Zheng, Jiarong He, Jin Gao
In this paper, we present our solution to the Multilingual Information Retrieval Across a Continuum of Languages (MIRACL) challenge of WSDM CUP 2023\footnote{https://project-miracl. github. io/}.
no code implementations • 6 Jan 2023 • Kangxu Wang, Ze Chen, Jiewen Zheng
In this paper, we present an ensemble approach for the NL4Opt competition subtask 1(NER task).
no code implementations • 7 Nov 2022 • Ze Chen, Kangxu Wang, Zijian Cai, Jiewen Zheng, Jiarong He, Max Gao, Jason Zhang
This paper mainly describes the dma submission to the TempoWiC task, which achieves a macro-F1 score of 77. 05% and attains the first place in this task.
no code implementations • 8 Oct 2022 • Yoshinao Katsu, Xiaozhi Lin, Ruigeng Ji, Ze Chen, Yui Kamisaka, Koto Bamba, Michael E. Baker
Lampreys are jawless fish that evolved about 550 million years ago at the base of the vertebrate line.
no code implementations • 5 Aug 2022 • Qi Zhang, Zijian Yang, Yilun Huang, Ze Chen, Zijian Cai, Kangxu Wang, Jiewen Zheng, Jiarong He, Jin Gao
Our models are all trained with cross-entropy loss to classify the query-product pairs into ESCI 4 categories at first, and then we use weighted sum with the 4-class probabilities to get the score for ranking.
no code implementations • 14 Apr 2022 • Ze Chen, Zhihang Fu, Jianqiang Huang, Mingyuan Tao, Rongxin Jiang, Xiang Tian, Yaowu Chen, Xian-Sheng Hua
The likelihood maps generated by the SLV module are used to supervise the feature learning of the backbone network, encouraging the network to attend to wider and more diverse areas of the image.
no code implementations • 1 Apr 2022 • Ze Chen, Zhihang Fu, Jianqiang Huang, Mingyuan Tao, Shengyu Li, Rongxin Jiang, Xiang Tian, Yaowu Chen, Xian-Sheng Hua
The application of cross-dataset training in object detection tasks is complicated because the inconsistency in the category range across datasets transforms fully supervised learning into semi-supervised learning.
1 code implementation • 25 Nov 2021 • Sen yang, Zhicheng Wang, Ze Chen, YanJie Li, Shoukui Zhang, Zhibin Quan, Shu-Tao Xia, Yiping Bao, Erjin Zhou, Wankou Yang
This paper presents a new method to solve keypoint detection and instance association by using Transformer.
Ranked #10 on
Multi-Person Pose Estimation
on MS COCO
no code implementations • 22 Aug 2021 • Xiaohu Jiang, Ze Chen, Zhicheng Wang, Erjin Zhou, ChunYuan
After DETR was proposed, this novel transformer-based detection paradigm which performs several cross-attentions between object queries and feature maps for predictions has subsequently derived a series of transformer-based detection heads.
no code implementations • CVPR 2020 • Ze Chen, Zhihang Fu, Rongxin Jiang, Yaowu Chen, Xian-Sheng Hua
In this paper, we propose a spatial likelihood voting (SLV) module to converge the proposal localizing process without any bounding box annotations.