1 code implementation • 26 Mar 2025 • Hao Fu, Hanbin Zhao, Jiahua Dong, Chao Zhang, Hui Qian
Recent pre-trained vision-language models (PT-VLMs) often face a Multi-Domain Class-Incremental Learning (MCIL) scenario in practice, where several classes and domains of multi-modal tasks are incrementally arrived.
no code implementations • 20 Mar 2025 • Peiyi Lin, Fukai Zhang, Kai Niu, Hao Fu
We evaluated the system in real-world medical scenarios.
1 code implementation • 27 Feb 2025 • Haochen Sun, Shuwen Zhang, Lei Ren, Hao Xu, Hao Fu, Caixia Yuan, Xiaojie Wang
Large language models (LLMs) based agent systems have made great strides in real-world applications beyond traditional NLP tasks.
no code implementations • 11 Jan 2025 • Hao Fu, William H. K. Lam, Wei Ma, Yuxin Shi, Rui Jiang, Huijun Sun, Ziyou Gao
To address this critical issue, we introduce a novel static traffic assignment model that explicitly incorporates the residual queue and queue-dependent link capacity.
no code implementations • 22 Dec 2024 • Jianfeng Lu, Ying Zhang, Riheng Jia, Shuqin Cao, Jing Liu, Hao Fu
Federated Learning (FL) mitigates privacy leakage in decentralized machine learning by allowing multiple clients to train collaboratively locally.
no code implementations • 16 Dec 2024 • Gangqiang Hu, Jianfeng Lu, Jianmin Han, Shuqin Cao, Jing Liu, Hao Fu
However, in the context of semi-decentralized FL, clients' communication and training states are dynamic.
no code implementations • 9 Dec 2024 • Hao Fu, Prashanth Krishnamurthy, Siddharth Garg, Farshad Khorrami
Lastly, we show that the proposed OI-based confidence score function inherits nice properties from OI (e. g., insensitivity to small distributional variations and robustness against Huber $\epsilon$-contamination) and is a versatile tool for estimating OI and model accuracy in specific contexts.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
no code implementations • 3 Nov 2024 • Zian Qian, Chenyang Qi, Ka Lung Law, Hao Fu, Chenyang Lei, Qifeng Chen
To address this cross-domain challenge, we present a novel adaptive domain learning (ADL) scheme for cross-domain RAW image denoising by utilizing existing data from different sensors (source domain) plus a small amount of data from the new sensor (target domain).
1 code implementation • 2 Oct 2024 • Xilong Wang, Hao Fu, Jindong Wang, Neil Zhenqiang Gong
In particular, we first propose StringLLM, a method to construct datasets for benchmarking string processing capability of LLMs.
no code implementations • 21 Sep 2024 • Hao Fu, Prashanth Krishnamurthy, Farshad Khorrami
An anomaly detector is used to detect CPS attacks during the controller's working period.
1 code implementation • 9 Sep 2024 • Jiahang Tu, Hao Fu, Fengyu Yang, Hanbin Zhao, Chao Zhang, Hui Qian
We model these granularities of information through text descriptions and propose a fine-grained Text-to-Touch generation method (TextToucher) to generate high-quality tactile samples.
no code implementations • 27 Jul 2024 • Chengzhi Wu, Kaige Wang, Zeyun Zhong, Hao Fu, Junwei Zheng, Jiaming Zhang, Julius Pfrommer, Jürgen Beyerer
In recent years, there have been significant advancements in applying attention mechanisms to point cloud analysis.
no code implementations • 4 Jun 2024 • Hao Fu, Tunhou Zhang, Hai Li, Yiran Chen
In this work, we propose a novel paradigm, Dense Connectivity Search of Outlier Detector (DCSOD), that automatically explore the dense connectivity of CNN architectures on near-OOD detection task using Neural Architecture Search (NAS).
1 code implementation • 23 May 2024 • Hao Fu, Naman Patel, Prashanth Krishnamurthy, Farshad Khorrami
Detection of out-of-distribution (OOD) samples is crucial for safe real-world deployment of machine learning models.
no code implementations • 15 May 2024 • Guozhang Liu, Ting Liu, Mengke Yuan, Tao Pang, Guangxing Yang, Hao Fu, Tao Wang, Tongkui Liao
The ambiguous appearance, tiny scale, and fine-grained classes of objects in remote sensing imagery inevitably lead to the noisy annotations in category labels of detection dataset.
no code implementations • 13 Apr 2024 • Chengpei Xu, Hao Fu, Long Ma, Wenjing Jia, Chengqi Zhang, Feng Xia, Xiaoyu Ai, Binghao Li, Wenjie Zhang
Localizing text in low-light environments is challenging due to visual degradations.
1 code implementation • 11 Jul 2023 • Hao Fu, Prashanth Krishnamurthy, Siddharth Garg, Farshad Khorrami
Having the computed five metrics, five novelty detectors are trained from the validation dataset.
no code implementations • 18 Apr 2023 • Wenping Wang, Yunxi Guo, Chiyao Shen, Shuai Ding, Guangdeng Liao, Hao Fu, Pramodh Karanth Prabhakar
Embedding based retrieval has seen its usage in a variety of search applications like e-commerce, social networking search etc.
no code implementations • 25 Jan 2023 • Andrew Papanicolaou, Hao Fu, Prashanth Krishnamurthy, Farshad Khorrami
When $\epsilon$ is small, we can implement an NN algorithm based on the expansion of the solution in powers of $\epsilon$.
no code implementations • 16 Dec 2022 • Hao Fu, Prashanth Krishnamurthy, Siddharth Garg, Farshad Khorrami
In domain shift analysis, we propose a theorem based on our bound.
no code implementations • 13 Dec 2022 • Alireza Sarmadi, Hao Fu, Prashanth Krishnamurthy, Siddharth Garg, Farshad Khorrami
As a baseline, in Cooperatively Trained Feature Extractor (CTFE) Learning, the entities train models by sharing raw data.
no code implementations • 1 Aug 2022 • Huixuan Chi, Hao Xu, Hao Fu, Mengya Liu, Mengdi Zhang, Yuji Yang, Qinfen Hao, Wei Wu
In particular: 1) existing methods do not explicitly encode and capture the evolution of short-term preference as sequential methods do; 2) simply using last few interactions is not enough for modeling the changing trend.
no code implementations • 13 Jun 2022 • Yongwei Huang, Hao Fu, Sergiy A. Vorobyov, Zhi-Quan Luo
Then a linear matrix inequality (LMI) relaxation for the QMI problem is proposed, with an additional valid linear constraint.
1 code implementation • 7 Jun 2022 • Hao Fu, Guotai Wang, Wenhui Lei, Wei Xu, Qianfei Zhao, Shichuan Zhang, Kang Li, Shaoting Zhang
Accurate segmentation of Anatomical brain Barriers to Cancer spread (ABCs) plays an important role for automatic delineation of Clinical Target Volume (CTV) of brain tumors in radiotherapy.
no code implementations • 20 Apr 2022 • Kelly Payette, Hongwei Li, Priscille de Dumast, Roxane Licandro, Hui Ji, Md Mahfuzur Rahman Siddiquee, Daguang Xu, Andriy Myronenko, Hao liu, Yuchen Pei, Lisheng Wang, Ying Peng, Juanying Xie, Huiquan Zhang, Guiming Dong, Hao Fu, Guotai Wang, ZunHyan Rieu, Donghyeon Kim, Hyun Gi Kim, Davood Karimi, Ali Gholipour, Helena R. Torres, Bruno Oliveira, João L. Vilaça, Yang Lin, Netanell Avisdris, Ori Ben-Zvi, Dafna Ben Bashat, Lucas Fidon, Michael Aertsen, Tom Vercauteren, Daniel Sobotka, Georg Langs, Mireia Alenyà, Maria Inmaculada Villanueva, Oscar Camara, Bella Specktor Fadida, Leo Joskowicz, Liao Weibin, Lv Yi, Li Xuesong, Moona Mazher, Abdul Qayyum, Domenec Puig, Hamza Kebiri, Zelin Zhang, Xinyi Xu, Dan Wu, Kuanlun Liao, Yixuan Wu, Jintai Chen, Yunzhi Xu, Li Zhao, Lana Vasung, Bjoern Menze, Meritxell Bach Cuadra, Andras Jakab
Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context.
no code implementations • 12 Sep 2021 • Hao Fu, Yan Wang, Ruihua Song, Tianran Hu, Jianyun Nie
The ability of a dialog system to express consistent language style during conversations has a direct, positive impact on its usability and on user satisfaction.
no code implementations • 12 Jul 2021 • Zhi Bian, Shaojun Zhou, Hao Fu, Qihong Yang, Zhenqi Sun, Junjie Tang, Guiquan Liu, Kaikui Liu, Xiaolong Li
Specifically, the framework: (i) proposes a feature purification module based on orthogonal mapping, which use the representation of explicit feedback to purify the representation of implicit feedback, and effectively denoise the implicit feedback; (ii) designs a user memory network to model the long-term interests in a fine-grained way by improving the memory network, which is ignored by the existing methods; and (iii) develops a preference-aware interactive representation component to fuse the long-term and short-term interests of users based on gating to understand the evolution of unbiased preferences of users.
no code implementations • 14 Dec 2020 • Hao Fu, Shaojun Zhou, Qihong Yang, Junjie Tang, Guiquan Liu, Kaikui Liu, Xiaolong Li
In this work, we propose a knowledge distillation method LRC-BERT based on contrastive learning to fit the output of the intermediate layer from the angular distance aspect, which is not considered by the existing distillation methods.
2 code implementations • 13 Dec 2020 • Wenhui Lei, Wei Xu, Ran Gu, Hao Fu, Shaoting Zhang, Guotai Wang
To address this problem, we present a one-shot framework for organ and landmark localization in volumetric medical images, which does not need any annotation during the training stage and could be employed to locate any landmarks or organs in test images given a support (reference) image during the inference stage.
no code implementations • 4 Nov 2020 • Hao Fu, Akshaj Kumar Veldanda, Prashanth Krishnamurthy, Siddharth Garg, Farshad Khorrami
This paper proposes a new defense against neural network backdooring attacks that are maliciously trained to mispredict in the presence of attacker-chosen triggers.
no code implementations • EMNLP 2020 • Guoyin Wang, Chunyuan Li, Jianqiao Li, Hao Fu, Yuh-Chen Lin, Liqun Chen, Yizhe Zhang, Chenyang Tao, Ruiyi Zhang, Wenlin Wang, Dinghan Shen, Qian Yang, Lawrence Carin
An extension is further proposed to improve the OT learning, based on the structural and contextual information of the text sequences.
no code implementations • 29 Jun 2020 • Tianbo Gu, Allaukik Abhishek, Hao Fu, Huanle Zhang, Debraj Basu, Prasant Mohapatra
These low-rate attacks are challenging to detect and can persist in the networks.
no code implementations • 6 May 2020 • Li Wang, Dawei Zhao, Tao Wu, Hao Fu, Zhiyu Wang, Liang Xiao, Xin Xu, Bin Dai
3D moving object detection is one of the most critical tasks in dynamic scene analysis.
no code implementations • 3 Jan 2020 • Danning Zheng, Ruihua Song, Tianran Hu, Hao Fu, Jin Zhou
By embedding the framework into a chatbot system, we then enables the chatbot to communicate with users using figurative language.
no code implementations • 25 Sep 2019 • Hao Fu, Liheng Bian, Jun Zhang
The conventional high-level sensing techniques require high-fidelity images as input to extract target features, which are produced by either complex imaging hardware or high-complexity reconstruction algorithms.
no code implementations • 9 Jul 2019 • Xiaoxiang Zhang, Hao Fu, Bin Dai
Object detection and classification based on lidar point cloud is a key technology for UGV.
no code implementations • 9 Apr 2019 • Shan Chen, Weiguo Dai, Yuanxi Dai, Hao Fu, Yang Gao, Jianqi Guo, Haoqing He, Yuhong Liu
This paper presents Thinkey, an efficient, secure, infinitely scalable and decentralized blockchain architecture.
Cryptography and Security
2 code implementations • NAACL 2019 • Hao Fu, Chunyuan Li, Xiaodong Liu, Jianfeng Gao, Asli Celikyilmaz, Lawrence Carin
Variational autoencoders (VAEs) with an auto-regressive decoder have been applied for many natural language processing (NLP) tasks.
no code implementations • WS 2018 • Yunfan Gu, Zhongyu Wei, Maoran Xu, Hao Fu, Yang Liu, Xuanjing Huang
In this paper, we propose to incorporate topic aspects information for online comments convincingness evaluation.