1 code implementation • 20 Jan 2025 • Jiaxiang Liu, Tianxiang Hu, Jiawei Du, Ruiyuan Zhang, Joey Tianyi Zhou, Zuozhu Liu
To tackle these challenges, we introduce the Knowledge Proxy Learning (KPL) to mine knowledge from CLIP.
1 code implementation • 28 Dec 2024 • Zhaopeng Feng, Jiayuan Su, Jiamei Zheng, Jiahan Ren, Yan Zhang, Jian Wu, Hongwei Wang, Zuozhu Liu
Recent advancements in large language models (LLMs) have given rise to the LLM-as-a-judge paradigm, showcasing their potential to deliver human-like judgments.
1 code implementation • 18 Dec 2024 • Jiaxiang Liu, YuAn Wang, Jiawei Du, Joey Tianyi Zhou, Zuozhu Liu
Artificial intelligence has advanced in Medical Visual Question Answering (Med-VQA), but prevalent research tends to focus on the accuracy of the answers, often overlooking the reasoning paths and interpretability, which are crucial in clinical settings.
1 code implementation • 10 Dec 2024 • Jianhong Bai, Menghan Xia, Xintao Wang, Ziyang Yuan, Xiao Fu, Zuozhu Liu, Haoji Hu, Pengfei Wan, Di Zhang
Recent advancements in video diffusion models have shown exceptional abilities in simulating real-world dynamics and maintaining 3D consistency.
no code implementations • 6 Nov 2024 • Wen Ma, Huikai Wu, Zikai Xiao, Yang Feng, Jian Wu, Zuozhu Liu
Reconstructing the 3D anatomical structures of the oral cavity, which originally reside in the cone-beam CT (CBCT), from a single 2D Panoramic X-ray(PX) remains a critical yet challenging task, as it can effectively reduce radiation risks and treatment costs during the diagnostic in digital dentistry.
no code implementations • 27 Oct 2024 • Xupeng Chen, Zhixin Lai, Kangrui Ruan, Shichu Chen, Jiaxiang Liu, Zuozhu Liu
Artificial intelligence has made significant strides in medical visual question answering (Med-VQA), yet prevalent studies often interpret images holistically, overlooking the visual regions of interest that may contain crucial information, potentially aligning with a doctor's prior knowledge that can be incorporated with minimal annotations (e. g., bounding boxes).
no code implementations • 25 Oct 2024 • Zhiting Fan, Ruizhe Chen, Tianxiang Hu, Zuozhu Liu
Based on this, we curate a challenging dataset, \texttt{FairMT-1K}, and test 15 current state-of-the-art (SOTA) LLMs on this dataset.
no code implementations • 20 Oct 2024 • Songtao Jiang, Yan Zhang, Ruizhe Chen, Yeying Jin, Zuozhu Liu
To address this, we propose Modality-Fair Preference Optimization (MFPO) to balance text and image preferences.
no code implementations • 5 Oct 2024 • Ruizhe Chen, Xiaotian Zhang, Meng Luo, Wenhao Chai, Zuozhu Liu
Aligning with personalized preferences, which vary significantly across cultural, educational, and political differences, poses a significant challenge due to the computational costs and data demands of traditional alignment methods.
1 code implementation • 17 Sep 2024 • Hanjun Luo, Yingbin Jin, Xuecheng Liu, Tong Shang, Ruizhe Chen, Zuozhu Liu
Large Language Models (LLMs) have supplanted traditional methods in numerous natural language processing tasks.
Multilingual Named Entity Recognition
named-entity-recognition
+2
no code implementations • 2 Sep 2024 • Jiao Chen, Jiayi He, Fangfang Chen, Zuohong Lv, Jianhua Tang, Weihua Li, Zuozhu Liu, Howard H. Yang, Guangjie Han
Second, we discuss how IIoT offers an efficient training infrastructure in low-latency and bandwidth-optimized environments.
no code implementations • 7 Aug 2024 • Ruizhe Chen, Yichen Li, Jianfei Yang, Joey Tianyi Zhou, Zuozhu Liu
Then, we propose a novel debiasing approach, Fairness Stamp (FAST), which enables fine-grained calibration of individual social biases.
1 code implementation • 6 Aug 2024 • Zhaopeng Feng, Zijie Meng, Zuozhu Liu
Large language models (LLMs) have attracted considerable attention in various fields for their cost-effective solutions to diverse challenges, especially with advancements in instruction tuning and quantization.
1 code implementation • 28 Jul 2024 • Hanjun Luo, Ziye Deng, Haoyu Huang, Xuecheng Liu, Ruizhe Chen, Zuozhu Liu
However, existing methods are designed for specific models with fixed prompts, limiting their adaptability to the fast-evolving models and diverse practical scenarios.
1 code implementation • 21 Jul 2024 • Hanjun Luo, Haoyu Huang, Ziye Deng, Xuecheng Liu, Ruizhe Chen, Zuozhu Liu
Text-to-Image (T2I) generative models are becoming increasingly crucial due to their ability to generate high-quality images, which also raises concerns about the social biases in their outputs, especially in the human generation.
no code implementations • 14 Jul 2024 • Zhiting Fan, Ruizhe Chen, Ruiling Xu, Zuozhu Liu
Evaluating the bias in Large Language Models (LLMs) becomes increasingly crucial with their rapid development.
no code implementations • 1 Jul 2024 • Jiabao Pan, Yan Zhang, Chen Zhang, Zuozhu Liu, Hongwei Wang, Haizhou Li
Large language models (LLMs) have demonstrated emergent capabilities across diverse reasoning tasks via popular Chains-of-Thought (COT) prompting.
3 code implementations • 22 Jun 2024 • Zhaopeng Feng, Ruizhe Chen, Yan Zhang, Zijie Meng, Zuozhu Liu
By utilizing Gemma-2B/7B as the backbone, MT-Ladder-2B can elevate raw translations to the level of top-tier open-source models (e. g., refining BigTranslate-13B with +6. 91 BLEU and +3. 52 COMET for XX-En), and MT-Ladder-7B can further enhance model performance to be on par with the state-of-the-art GPT-4.
1 code implementation • 28 May 2024 • Hanjun Luo, Ziye Deng, Ruizhe Chen, Zuozhu Liu
The rapid development and reduced barriers to entry for Text-to-Image (T2I) models have raised concerns about the biases in their outputs, but existing research lacks a holistic definition and evaluation framework of biases, limiting the enhancement of debiasing techniques.
no code implementations • 16 May 2024 • Ruizhe Chen, Tianxiang Hu, Yang Feng, Zuozhu Liu
To bridge this gap, we introduce a pioneering method for pinpointing PII-sensitive neurons (privacy neurons) within LLMs.
no code implementations • 15 May 2024 • Ruizhe Chen, Yichen Li, Zikai Xiao, Zuozhu Liu
Existing debiasing methods inevitably make unreasonable or undesired predictions as they are designated and evaluated to achieve parity across different social groups but leave aside individual facts, resulting in modified existing knowledge.
no code implementations • 18 Apr 2024 • Xiaotang Gai, Chenyi Zhou, Jiaxiang Liu, Yang Feng, Jian Wu, Zuozhu Liu
Medical Visual Question Answering (MedVQA), which offers language responses to image-based medical inquiries, represents a challenging task and significant advancement in healthcare.
2 code implementations • 16 Apr 2024 • Songtao Jiang, Tuo Zheng, Yan Zhang, Yeying Jin, Li Yuan, Zuozhu Liu
Recent advancements in general-purpose or domain-specific multimodal large language models (LLMs) have witnessed remarkable progress for medical decision-making.
1 code implementation • 6 Apr 2024 • Songtao Jiang, Yan Zhang, Chenyi Zhou, Yeying Jin, Yang Feng, Jian Wu, Zuozhu Liu
In this paper, we present a novel approach, Joint Visual and Text Prompting (VTPrompt), that employs fine-grained visual information to enhance the capability of MLLMs in VQA, especially for object-oriented perception.
1 code implementation • 26 Feb 2024 • Zhaopeng Feng, Yan Zhang, Hao Li, Bei Wu, Jiayu Liao, Wenqiang Liu, Jun Lang, Yang Feng, Jian Wu, Zuozhu Liu
Large Language Models (LLMs) have achieved impressive results in Machine Translation (MT).
no code implementations • 20 Feb 2024 • Jianhong Bai, Tianyu He, Yuchi Wang, Junliang Guo, Haoji Hu, Zuozhu Liu, Jiang Bian
Recent advances in text-guided video editing have showcased promising results in appearance editing (e. g., stylization).
1 code implementation • 17 Jan 2024 • Zikai Xiao, Zihan Chen, Liyinglan Liu, Yang Feng, Jian Wu, Wanlu Liu, Joey Tianyi Zhou, Howard Hao Yang, Zuozhu Liu
Federated Long-Tailed Learning (Fed-LT), a paradigm wherein data collected from decentralized local clients manifests a globally prevalent long-tailed distribution, has garnered considerable attention in recent times.
2 code implementations • 10 Jan 2024 • Zijie Meng, Yan Zhang, Zhaopeng Feng, Zuozhu Liu
Subsequently, we propose Filter Choices based Reasoning (FCR) to improve model performance on MCQs with low ($\mathcal{CS}$).
1 code implementation • 22 Nov 2023 • Huimin Xiong, Kunle Li, Kaiyuan Tan, Yang Feng, Joey Tianyi Zhou, Jin Hao, Haochao Ying, Jian Wu, Zuozhu Liu
Optical Intraoral Scanners (IOS) are widely used in digital dentistry to provide detailed 3D information of dental crowns and the gingiva.
1 code implementation • 15 Nov 2023 • Tingyu Xie, Qi Li, Yan Zhang, Zuozhu Liu, Hongwei Wang
Exploring the application of powerful large language models (LLMs) on the named entity recognition (NER) task has drawn much attention recently.
1 code implementation • 14 Nov 2023 • Yan Zhang, Zhaopeng Feng, Zhiyang Teng, Zuozhu Liu, Haizhou Li
Text embedding models have significantly contributed to advancements in natural language processing by adeptly capturing semantic properties of textual data.
1 code implementation • 16 Oct 2023 • Tingyu Xie, Qi Li, Jian Zhang, Yan Zhang, Zuozhu Liu, Hongwei Wang
Large language models (LLMs) exhibited powerful capability in various natural language processing tasks.
1 code implementation • NeurIPS 2023 • Zikai Xiao, Zihan Chen, Songshang Liu, Hualiang Wang, Yang Feng, Jin Hao, Joey Tianyi Zhou, Jian Wu, Howard Hao Yang, Zuozhu Liu
Data privacy and long-tailed distribution are the norms rather than the exception in many real-world tasks.
no code implementations • 5 Oct 2023 • Jianhong Bai, Yuchen Yang, Huanpeng Chu, Hualiang Wang, Zuozhu Liu, Ruizhe Chen, Xiaoxuan He, Lianrui Mu, Chengfei Cai, Haoji Hu
Quantization has emerged as a promising direction for model compression.
1 code implementation • NeurIPS 2023 • Jianhong Bai, Zuozhu Liu, Hualiang Wang, Ruizhe Chen, Lianrui Mu, Xiaomeng Li, Joey Tianyi Zhou, Yang Feng, Jian Wu, Haoji Hu
In this paper, we formally define a more realistic task as distribution-agnostic generalized category discovery (DA-GCD): generating fine-grained predictions for both close- and open-set classes in a long-tailed open-world setting.
no code implementations • 20 Sep 2023 • Yifu Zhang, Zuozhu Liu, Yang Feng, Renjing Xu
Accurate representation of tooth position is extremely important in treatment.
no code implementations • 5 Jul 2023 • Jiaxiang Liu, Tianxiang Hu, Yang Feng, Wanghui Ding, Zuozhu Liu
In computer-assisted orthodontics, three-dimensional tooth models are required for many medical treatments.
no code implementations • 5 Jul 2023 • Jiaxiang Liu, Tianxiang Hu, Yan Zhang, Xiaotang Gai, Yang Feng, Zuozhu Liu
Recent advances in pretrained vision-language models (VLMs) such as CLIP have shown great performance for zero-shot natural image recognition and exhibit benefits in medical applications.
2 code implementations • 8 Jun 2023 • Jianhong Bai, Zuozhu Liu, Hualiang Wang, Jin Hao, Yang Feng, Huanpeng Chu, Haoji Hu
Recent work shows that the long-tailed learning performance could be boosted by sampling extra in-domain (ID) data for self-supervised training, however, large-scale ID data which can rebalance the minority classes are expensive to collect.
2 code implementations • 15 Feb 2023 • Shenghao Hao, Peiyuan Liu, Yibing Zhan, Kaixun Jin, Zuozhu Liu, Mingli Song, Jenq-Neng Hwang, Gaoang Wang
Although cross-view multi-object tracking has received increased attention in recent years, existing datasets still have several issues, including 1) missing real-world scenarios, 2) lacking diverse scenes, 3) owning a limited number of tracks, 4) comprising only static cameras, and 5) lacking standard benchmarks, which hinder the investigation and comparison of cross-view tracking methods.
1 code implementation • 30 Oct 2022 • Yiming Chen, Yan Zhang, Bin Wang, Zuozhu Liu, Haizhou Li
Most sentence embedding techniques heavily rely on expensive human-annotated sentence pairs as the supervised signals.
no code implementations • 29 Oct 2022 • Huimin Xiong, Kunle Li, Kaiyuan Tan, Yang Feng, Joey Tianyi Zhou, Jin Hao, Zuozhu Liu
Optical Intra-oral Scanners (IOS) are widely used in digital dentistry, providing 3-Dimensional (3D) and high-resolution geometrical information of dental crowns and the gingiva.
1 code implementation • 22 Aug 2022 • Hualiang Wang, Siming Fu, Xiaoxuan He, Hangxiang Fang, Zuozhu Liu, Haoji Hu
To our knowledge, this is the first work to measure representation quality of classifiers and features from the perspective of distribution overlap coefficient.
no code implementations • 30 Jun 2022 • Zihan Chen, Songshang Liu, Hualiang Wang, Howard H. Yang, Tony Q. S. Quek, Zuozhu Liu
Data privacy and class imbalance are the norm rather than the exception in many machine learning tasks.
no code implementations • 11 Mar 2022 • Jin Hao, Jiaxiang Liu, Jin Li, Wei Pan, Ruizhe Chen, Huimin Xiong, Kaiwei Sun, Hangzheng Lin, Wanlu Liu, Wanghui Ding, Jianfei Yang, Haoji Hu, Yueling Zhang, Yang Feng, Zeyu Zhao, Huikai Wu, Youyi Zheng, Bing Fang, Zuozhu Liu, Zhihe Zhao
Here, we present a Deep Dental Multimodal Analysis (DDMA) framework consisting of a CBCT segmentation model, an intraoral scan (IOS) segmentation model (the most accurate digital dental model), and a fusion model to generate 3D fused crown-root-bone structures with high fidelity and accurate occlusal and dentition information.
no code implementations • 17 Feb 2022 • Howard H. Yang, Zuozhu Liu, Yaru Fu, Tony Q. S. Quek, H. Vincent Poor
Federated learning (FL) is an emerging machine learning method that can be applied in mobile edge systems, in which a server and a host of clients collaboratively train a statistical model utilizing the data and computation resources of the clients without directly exposing their privacy-sensitive data.
1 code implementation • ICCV 2021 • Gaoang Wang, Renshu Gu, Zuozhu Liu, Weijie Hu, Mingli Song, Jenq-Neng Hwang
In this paper, we try to explore the significance of motion patterns for vehicle tracking without appearance information.
1 code implementation • ACL 2021 • Yan Zhang, Ruidan He, Zuozhu Liu, Lidong Bing, Haizhou Li
As high-quality labeled data is scarce, unsupervised sentence representation learning has attracted much attention.
1 code implementation • EMNLP 2020 • Yan Zhang, Zhijiang Guo, Zhiyang Teng, Wei Lu, Shay B. Cohen, Zuozhu Liu, Lidong Bing
With the help of these strategies, we are able to train a model with fewer parameters while maintaining the model capacity.
1 code implementation • EMNLP 2020 • Yan Zhang, Ruidan He, Zuozhu Liu, Kwan Hui Lim, Lidong Bing
However, SBERT is trained on corpus with high-quality labeled sentence pairs, which limits its application to tasks where labeled data is extremely scarce.
Ranked #19 on
Semantic Textual Similarity
on STS15
no code implementations • 25 Nov 2019 • Zuozhu Liu, Thiparat Chotibut, Christopher Hillar, Shaowei Lin
Motivated by the celebrated discrete-time model of nervous activity outlined by McCulloch and Pitts in 1943, we propose a novel continuous-time model, the McCulloch-Pitts network (MPN), for sequence learning in spiking neural networks.
no code implementations • 17 Aug 2019 • Howard H. Yang, Zuozhu Liu, Tony Q. S. Quek, H. Vincent Poor
Due to limited bandwidth, only a portion of UEs can be scheduled for updates at each iteration.
Information Theory Signal Processing Information Theory
no code implementations • 4 Dec 2017 • Mohammad Emtiyaz Khan, Zuozhu Liu, Voot Tangkaratt, Yarin Gal
Overall, this paper presents Vprop as a principled, computationally-efficient, and easy-to-implement method for Bayesian deep learning.
no code implementations • 21 Nov 2017 • Zuozhu Liu, Tony Q. S. Quek, Shaowei Lin
The quest for biologically plausible deep learning is driven, not just by the desire to explain experimentally-observed properties of biological neural networks, but also by the hope of discovering more efficient methods for training artificial networks.
no code implementations • 15 Nov 2017 • Mohammad Emtiyaz Khan, Wu Lin, Voot Tangkaratt, Zuozhu Liu, Didrik Nielsen
We present the Variational Adaptive Newton (VAN) method which is a black-box optimization method especially suitable for explorative-learning tasks such as active learning and reinforcement learning.