Search Results for author: Feng Liu

Found 207 papers, 82 papers with code

IR2QSM: Quantitative Susceptibility Mapping via Deep Neural Networks with Iterative Reverse Concatenations and Recurrent Modules

no code implementations18 Jun 2024 Min Li, Chen Chen, Zhuang Xiong, Ying Liu, Pengfei Rong, Shanshan Shan, Feng Liu, Hongfu Sun, Yang Gao

Quantitative susceptibility mapping (QSM) is an MRI phase-based post-processing technique to extract the distribution of tissue susceptibilities, demonstrating significant potential in studying neurological diseases.

Candidate Pseudolabel Learning: Enhancing Vision-Language Models by Prompt Tuning with Unlabeled Data

1 code implementation15 Jun 2024 Jiahan Zhang, Qi Wei, Feng Liu, Lei Feng

To alleviate this issue, we propose a Candidate Pseudolabel Learning method, termed CPL, to fine-tune VLMs with suitable candidate pseudolabels of unlabeled data in downstream tasks.

Capacity Credit Evaluation of Generalized Energy Storage Considering Endogenous Uncertainty

no code implementations11 Jun 2024 Ning Qi, Pierre Pinson, Mads R. Almassalkhi, Yingrui Zhuang, Yifan Su, Feng Liu

Generalized energy storage (GES), encompassing both physical and virtual energy storage, can provide remarkable but uncertain adequacy flexibility.

Scheduling

Visual-Text Cross Alignment: Refining the Similarity Score in Vision-Language Models

1 code implementation5 Jun 2024 Jinhao Li, Haopeng Li, Sarah Erfani, Lei Feng, James Bailey, Feng Liu

The local visual areas are then cross-aligned with the finer descriptions by creating a similarity matrix using the pre-trained VLM.

Few-Shot Learning Language Modelling +2

Sample-specific Masks for Visual Reprogramming-based Prompting

1 code implementation5 Jun 2024 Chengyi Cai, Zesheng Ye, Lei Feng, Jianzhong Qi, Feng Liu

Since we generate different masks for individual samples, SMM is theoretically shown to reduce approximation error for the target tasks compared with existing state-of-the-art VR methods.

Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training

1 code implementation2 Jun 2024 Jiacheng Zhang, Feng Liu, Dawei Zhou, Jingfeng Zhang, Tongliang Liu

However, in this paper, we discover that not all pixels contribute equally to the accuracy on AEs (i. e., robustness) and accuracy on natural images (i. e., accuracy).

Robust classification

MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence

1 code implementation29 May 2024 Hongduan Tian, Feng Liu, Tongliang Liu, Bo Du, Yiu-ming Cheung, Bo Han

In cross-domain few-shot classification, \emph{nearest centroid classifier} (NCC) aims to learn representations to construct a metric space where few-shot classification can be performed by measuring the similarities between samples and the prototype of each class.

Cross-Domain Few-Shot

DynRefer: Delving into Region-level Multi-modality Tasks via Dynamic Resolution

1 code implementation25 May 2024 Yuzhong Zhao, Feng Liu, Yue Liu, Mingxiang Liao, Chen Gong, Qixiang Ye, Fang Wan

Unfortunately, most of existing methods using fixed visual inputs remain lacking the resolution adaptability to find out precise language descriptions.

Attribute

BDetCLIP: Multimodal Prompting Contrastive Test-Time Backdoor Detection

no code implementations24 May 2024 Yuwei Niu, Shuo He, Qi Wei, Feng Liu, Lei Feng

In this paper, we provide the first attempt at a computationally efficient backdoor detection method to defend against backdoored CLIP in the inference stage.

Contrastive Learning Language Modelling +2

Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language Model

no code implementations1 May 2024 Yu Cui, Feng Liu, Pengbo Wang, Bohao Wang, Heng Tang, Yi Wan, Jun Wang, Jiawei Chen

Owing to their powerful semantic reasoning capabilities, Large Language Models (LLMs) have been effectively utilized as recommenders, achieving impressive performance.

Knowledge Distillation Language Modelling +1

VideoGigaGAN: Towards Detail-rich Video Super-Resolution

no code implementations18 Apr 2024 Yiran Xu, Taesung Park, Richard Zhang, Yang Zhou, Eli Shechtman, Feng Liu, Jia-Bin Huang, Difan Liu

We introduce VideoGigaGAN, a new generative VSR model that can produce videos with high-frequency details and temporal consistency.

Video Super-Resolution

On the Learnability of Out-of-distribution Detection

no code implementations7 Apr 2024 Zhen Fang, Yixuan Li, Feng Liu, Bo Han, Jie Lu

Based on this observation, we next give several necessary and sufficient conditions to characterize the learnability of OOD detection in some practical scenarios.

Learning Theory Out-of-Distribution Detection +2

Learn to Disguise: Avoid Refusal Responses in LLM's Defense via a Multi-agent Attacker-Disguiser Game

no code implementations3 Apr 2024 Qianqiao Xu, Zhiliang Tian, Hongyan Wu, Zhen Huang, Yiping Song, Feng Liu, Dongsheng Li

In this paper, we propose a multi-agent attacker-disguiser game approach to achieve a weak defense mechanism that allows the large model to both safely reply to the attacker and hide the defense intent.

Prompt Engineering

Negative Label Guided OOD Detection with Pretrained Vision-Language Models

1 code implementation29 Mar 2024 Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han

In this paper, we propose a novel post hoc OOD detection method, called NegLabel, which takes a vast number of negative labels from extensive corpus databases.

Out of Distribution (OOD) Detection

Benchmarking Video Frame Interpolation

no code implementations25 Mar 2024 Simon Kiefhaber, Simon Niklaus, Feng Liu, Simone Schaub-Meyer

Video frame interpolation, the task of synthesizing new frames in between two or more given ones, is becoming an increasingly popular research target.

Benchmarking Computational Efficiency +1

QSMDiff: Unsupervised 3D Diffusion Models for Quantitative Susceptibility Mapping

no code implementations21 Mar 2024 Zhuang Xiong, Wei Jiang, Yang Gao, Feng Liu, Hongfu Sun

In this work, we developed a 3D image patch-based diffusion model, namely QSMDiff, for robust QSM reconstruction across different scan parameters, alongside simultaneous super-resolution and image-denoising tasks.

Image Denoising Image Generation +1

KeyPoint Relative Position Encoding for Face Recognition

2 code implementations CVPR 2024 Minchul Kim, Yiyang Su, Feng Liu, Anil Jain, Xiaoming Liu

By anchoring the significance of pixels around keypoints, the model can more effectively retain spatial relationships, even when those relationships are disrupted by affine transformations.

Face Recognition Gait Recognition +1

IIDM: Image-to-Image Diffusion Model for Semantic Image Synthesis

1 code implementation20 Mar 2024 Feng Liu, Xiaobin-Chang

Semantic image synthesis aims to generate high-quality images given semantic conditions, i. e. segmentation masks and style reference images.

Image Denoising Image Generation

Large Language Multimodal Models for 5-Year Chronic Disease Cohort Prediction Using EHR Data

no code implementations2 Mar 2024 Jun-En Ding, Phan Nguyen Minh Thao, Wen-Chih Peng, Jian-Zhe Wang, Chun-Cheng Chug, Min-Chen Hsieh, Yun-Chien Tseng, Ling Chen, Dongsheng Luo, Chi-Te Wang, Pei-fu Chen, Feng Liu, Fang-Ming Hung

In our experiments, we observe that clinicalBERT and PubMed-BERT, when combined with attention fusion, can achieve an accuracy of 73% in multiclass chronic diseases and diabetes prediction.

Diabetes Prediction

Customize-A-Video: One-Shot Motion Customization of Text-to-Video Diffusion Models

no code implementations22 Feb 2024 Yixuan Ren, Yang Zhou, Jimei Yang, Jing Shi, Difan Liu, Feng Liu, Mingi Kwon, Abhinav Shrivastava

With the emergence of text-to-video (T2V) diffusion models, its temporal counterpart, motion customization, has not yet been well investigated.

Video Generation

Fingerprint Presentation Attack Detector Using Global-Local Model

no code implementations20 Feb 2024 Haozhe Liu, Wentian Zhang, Feng Liu, Haoqian Wu, Linlin Shen

While by using the texture in-painting-based local module, a local spoofness score predicted from fingerprint patches is obtained.

Privacy-Preserving Low-Rank Adaptation for Latent Diffusion Models

1 code implementation19 Feb 2024 Zihao Luo, Xilie Xu, Feng Liu, Yun Sing Koh, Di Wang, Jingfeng Zhang

To mitigate this issue, we further propose a Stable Membership-Privacy-preserving LoRA (SMP-LoRA) that adapts the LDM by minimizing the ratio of the adaptation loss to the MI gain.

Privacy Preserving

Ray Denoising: Depth-aware Hard Negative Sampling for Multi-view 3D Object Detection

2 code implementations6 Feb 2024 Feng Liu, Tengteng Huang, Qianjing Zhang, Haotian Yao, Chi Zhang, Fang Wan, Qixiang Ye, Yanzhao Zhou

Multi-view 3D object detection systems often struggle with generating precise predictions due to the challenges in estimating depth from images, increasing redundant and incorrect detections.

3D Object Detection Denoising +1

From Data to Insights: A Comprehensive Survey on Advanced Applications in Thyroid Cancer Research

no code implementations8 Jan 2024 Xinyu Zhang, Vincent CS Lee, Feng Liu

Thyroid cancer, the most prevalent endocrine cancer, has gained significant global attention due to its impact on public health.

TIGER: Time-Varying Denoising Model for 3D Point Cloud Generation with Diffusion Process

no code implementations CVPR 2024 Zhiyuan Ren, Minchul Kim, Feng Liu, Xiaoming Liu

However few works study the effect of the architecture of the diffusion model in the 3D point cloud resorting to the typical UNet model developed for 2D images.

Denoising Point Cloud Generation

SNED: Superposition Network Architecture Search for Efficient Video Diffusion Model

no code implementations CVPR 2024 Zhengang Li, Yan Kang, Yuchen Liu, Difan Liu, Tobias Hinz, Feng Liu, Yanzhi Wang

Our method employs a supernet training paradigm that targets various model cost and resolution options using a weight-sharing method.

Video Generation

INFAMOUS-NeRF: ImproviNg FAce MOdeling Using Semantically-Aligned Hypernetworks with Neural Radiance Fields

no code implementations23 Dec 2023 Andrew Hou, Feng Liu, Zhiyuan Ren, Michel Sarkis, Ning Bi, Yiying Tong, Xiaoming Liu

We propose INFAMOUS-NeRF, an implicit morphable face model that introduces hypernetworks to NeRF to improve the representation power in the presence of many training subjects.

Face Model

Fast View Synthesis of Casual Videos

no code implementations4 Dec 2023 Yao-Chih Lee, Zhoutong Zhang, Kevin Blackburn-Matzen, Simon Niklaus, Jianming Zhang, Jia-Bin Huang, Feng Liu

Specifically, we build a global static scene model using an extended plane-based scene representation to synthesize temporally coherent novel video.

Novel View Synthesis

Parkinson's Disease Classification Using Contrastive Graph Cross-View Learning with Multimodal Fusion of SPECT Images and Clinical Features

no code implementations25 Nov 2023 Jun-En Ding, Chien-Chin Hsu, Feng Liu

This work proposes a multimodal approach encompassing both image and non-image features, leveraging contrastive cross-view graph fusion for PD classification.

Single Image Compressed Sensing MRI via a Self-Supervised Deep Denoising Approach

no code implementations22 Nov 2023 Marlon Bran Lorenzana, Feng Liu, Shekhar S. Chandra

Popular methods in compressed sensing (CS) are dependent on deep learning (DL), where large amounts of data are used to train non-linear reconstruction models.

Denoising Image Compressed Sensing

Fast Controllable Diffusion Models for Undersampled MRI Reconstruction

1 code implementation20 Nov 2023 Wei Jiang, Zhuang Xiong, Feng Liu, Nan Ye, Hongfu Sun

Supervised deep learning methods have shown promise in undersampled Magnetic Resonance Imaging (MRI) reconstruction, but their requirement for paired data limits their generalizability to the diverse MRI acquisition parameters.

MRI Reconstruction

Plug-and-Play Latent Feature Editing for Orientation-Adaptive Quantitative Susceptibility Mapping Neural Networks

1 code implementation14 Nov 2023 Yang Gao, Zhuang Xiong, Shanshan Shan, Yin Liu, Pengfei Rong, Min Li, Alan H Wilman, G. Bruce Pike, Feng Liu, Hongfu Sun

The proposed OA-LFE-empowered iQSM, which we refer to as iQSM+, is trained in a self-supervised manner on a specially-designed simulation brain dataset.

LRM: Large Reconstruction Model for Single Image to 3D

1 code implementation8 Nov 2023 Yicong Hong, Kai Zhang, Jiuxiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, Hao Tan

We propose the first Large Reconstruction Model (LRM) that predicts the 3D model of an object from a single input image within just 5 seconds.

Image to 3D

Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources

1 code implementation NeurIPS 2023 Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, Bo Han

To this end, we suggest that generated data (with mistaken OOD generation) can be used to devise an auxiliary OOD detection task to facilitate real OOD detection.

Out-of-Distribution Detection Out of Distribution (OOD) Detection +1

Learning to Augment Distributions for Out-of-Distribution Detection

1 code implementation NeurIPS 2023 Qizhou Wang, Zhen Fang, Yonggang Zhang, Feng Liu, Yixuan Li, Bo Han

Accordingly, we propose Distributional-Augmented OOD Learning (DAL), alleviating the OOD distribution discrepancy by crafting an OOD distribution set that contains all distributions in a Wasserstein ball centered on the auxiliary OOD distribution.

Learning Theory Out-of-Distribution Detection

Partition Speeds Up Learning Implicit Neural Representations Based on Exponential-Increase Hypothesis

1 code implementation ICCV 2023 Ke Liu, Feng Liu, Haishuai Wang, Ning Ma, Jiajun Bu, Bo Han

Based on this fact, we introduce a simple partition mechanism to boost the performance of two INR methods for image reconstruction: one for learning INRs, and the other for learning-to-learn INRs.

Image Reconstruction Semantic Segmentation

Auxiliary Features-Guided Super Resolution for Monte Carlo Rendering

no code implementations20 Oct 2023 Qiqi Hou, Feng Liu

This paper investigates super resolution to reduce the number of pixels to render and thus speed up Monte Carlo rendering algorithms.

Denoising Super-Resolution

PINF: Continuous Normalizing Flows for Physics-Constrained Deep Learning

no code implementations26 Sep 2023 Feng Liu, Faguo Wu, Xiao Zhang

The normalization constraint on probability density poses a significant challenge for solving the Fokker-Planck equation.

Multi-Stage Expansion Planning for Decarbonizing Thermal Generation Supported Renewable Power Systems Using Hydrogen and Ammonia Storage

no code implementations31 Aug 2023 Zhipeng Yu, Jin Lin, Feng Liu, Jiarong Li, Yingtian Chi, Yonghua Song, Zhengwei Ren

Large-scale centralized development of wind and solar energy and peer-to-grid transmission of renewable energy source (RES) via high voltage direct current (HVDC) has been regarded as one of the most promising ways to achieve goals of peak carbon and carbon neutrality in China.

Quantitative Susceptibility Mapping through Model-based Deep Image Prior (MoDIP)

no code implementations18 Aug 2023 Zhuang Xiong, Yang Gao, Yin Liu, Amir Fazlollahi, Peter Nestor, Feng Liu, Hongfu Sun

The data-driven approach of supervised learning methods has limited applicability in solving dipole inversion in Quantitative Susceptibility Mapping (QSM) with varying scan parameters across different objects.

Image Reconstruction

Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation

no code implementations12 Jul 2023 Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han

In this paper, we propose a diversity-enhancing generative network (DEG-Net) for the FHA problem, which can generate diverse unlabeled data with the help of a kernel independence measure: the Hilbert-Schmidt independence criterion (HSIC).

Learning Constrained Corner Node Trajectories of a Tether Net System for Space Debris Capture

no code implementations6 Jul 2023 Feng Liu, Achira Boonrath, Prajit KrisshnaKumar, Elenora M. Botta, Souma Chowdhury

The earth's orbit is becoming increasingly crowded with debris that poses significant safety risks to the operation of existing and new spacecraft and satellites.

CoverHunter: Cover Song Identification with Refined Attention and Alignments

1 code implementation15 Jun 2023 Feng Liu, Deyi Tuo, Yinan Xu, Xintong Han

Abstract: Cover song identification (CSI) focuses on finding the same music with different versions in reference anchors given a query track.

Cover song identification

Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score

1 code implementation25 May 2023 Shuhai Zhang, Feng Liu, Jiahao Yang, Yifan Yang, Changsheng Li, Bo Han, Mingkui Tan

Last, we propose an EPS-based adversarial detection (EPS-AD) method, in which we develop EPS-based maximum mean discrepancy (MMD) as a metric to measure the discrepancy between the test sample and natural samples.

DADIN: Domain Adversarial Deep Interest Network for Cross Domain Recommender Systems

no code implementations20 May 2023 Menglin Kong, Muzhou Hou, Shaojie Zhao, Feng Liu, Ri Su, Yinghao Chen

Click-Through Rate (CTR) prediction is one of the main tasks of the recommendation system, which is conducted by a user for different items to give the recommendation results.

Click-Through Rate Prediction Domain Adaptation +2

Attacking Perceptual Similarity Metrics

no code implementations15 May 2023 Abhijay Ghildyal, Feng Liu

In our study, we systematically examine the robustness of these metrics to imperceptible adversarial perturbations.

Adversarial Attack Experimental Design

Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization

1 code implementation NeurIPS 2023 Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan Kankanhalli

To improve transferability, the existing work introduced the standard invariant regularization (SIR) to impose style-independence property to SCL, which can exempt the impact of nuisance style factors in the standard representation.

Contrastive Learning

Detecting Out-of-distribution Data through In-distribution Class Prior

1 code implementation ICML 2023 Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han

In this paper, we show that this assumption makes the above methods incapable when the ID model is trained with class-imbalanced data. Fortunately, by analyzing the causal relations between ID/OOD classes and features, we identify several common scenarios where the OOD-to-ID probabilities should be the ID-class-prior distribution and propose two strategies to modify existing inference-time detection methods: 1) replace the uniform distribution with the ID-class-prior distribution if they explicitly use the uniform distribution; 2) otherwise, reweight their scores according to the similarity between the ID-class-prior distribution and the softmax outputs of the pre-trained model.

Out-of-Distribution Detection Out of Distribution (OOD) Detection

DCFace: Synthetic Face Generation with Dual Condition Diffusion Model

1 code implementation CVPR 2023 Minchul Kim, Feng Liu, Anil Jain, Xiaoming Liu

Our novel Patch-wise style extractor and Time-step dependent ID loss enables DCFace to consistently produce face images of the same subject under different styles with precise control.

Face Generation Synthetic Face Recognition

AnyFlow: Arbitrary Scale Optical Flow with Implicit Neural Representation

no code implementations CVPR 2023 Hyunyoung Jung, Zhuo Hui, Lei Luo, Haitao Yang, Feng Liu, Sungjoo Yoo, Rakesh Ranjan, Denis Demandolx

To apply optical flow in practice, it is often necessary to resize the input to smaller dimensions in order to reduce computational costs.

Optical Flow Estimation

Optimal Sizing of Isolated Renewable Power Systems with Ammonia Synthesis: Model and Solution Approach

no code implementations10 Mar 2023 Zhipeng Yu, Jin Lin, Feng Liu, Jiarong Li, Yuxuan Zhao, Yonghua Song

However, multi-timescale electricity, hydrogen, and ammonia storages, minimum power supply for system safety, and the multi-year uncertainty of renewable generation lead to difficulties in planning.

Out-of-distribution Detection with Implicit Outlier Transformation

1 code implementation9 Mar 2023 Qizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai, Marcus Kalander, Tongliang Liu, Jianye Hao, Bo Han

It leads to a min-max learning scheme -- searching to synthesize OOD data that leads to worst judgments and learning from such OOD data for uniform performance in OOD detection.

Out-of-Distribution Detection

AliasNet: Alias Artefact Suppression Network for Accelerated Phase-Encode MRI

no code implementations17 Feb 2023 Marlon E. Bran Lorenzana, Shekhar S. Chandra, Feng Liu

Sparse reconstruction is an important aspect of MRI, helping to reduce acquisition time and improve spatial-temporal resolution.

Denoising SSIM

Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection

1 code implementation NeurIPS 2023 Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan Kankanhalli

Adversarial contrastive learning (ACL) does not require expensive data annotations but outputs a robust representation that withstands adversarial attacks and also generalizes to a wide range of downstream tasks.

Contrastive Learning

Shift from Texture-bias to Shape-bias: Edge Deformation-based Augmentation for Robust Object Recognition

no code implementations ICCV 2023 Xilin He, Qinliang Lin, Cheng Luo, Weicheng Xie, Siyang Song, Feng Liu, Linlin Shen

Recent studies have shown the vulnerability of CNNs under perturbation noises, which is partially caused by the reason that the well-trained CNNs are too biased toward the object texture, i. e., they make predictions mainly based on texture cues.

Object Recognition

Learning Implicit Functions for Dense 3D Shape Correspondence of Generic Objects

no code implementations29 Dec 2022 Feng Liu, Xiaoming Liu

The objective of this paper is to learn dense 3D shape correspondence for topology-varying generic objects in an unsupervised manner.

Semantic correspondence

Continuous Semi-Supervised Nonnegative Matrix Factorization

no code implementations19 Dec 2022 Michael R. Lindstrom, Xiaofu Ding, Feng Liu, Anand Somayajula, Deanna Needell

Nonnegative matrix factorization can be used to automatically detect topics within a corpus in an unsupervised fashion.

regression

3D-EPI Blip-Up/Down Acquisition (BUDA) with CAIPI and Joint Hankel Structured Low-Rank Reconstruction for Rapid Distortion-Free High-Resolution T2* Mapping

no code implementations1 Dec 2022 Zhifeng Chen, Congyu Liao, Xiaozhi Cao, Benedikt A. Poser, Zhongbiao Xu, Wei-Ching Lo, Manyi Wen, Jaejin Cho, Qiyuan Tian, Yaohui Wang, Yanqiu Feng, Ling Xia, Wufan Chen, Feng Liu, Berkin Bilgic

Purpose: This work aims to develop a novel distortion-free 3D-EPI acquisition and image reconstruction technique for fast and robust, high-resolution, whole-brain imaging as well as quantitative T2* mapping.

Image Reconstruction

Affine Transformation Edited and Refined Deep Neural Network for Quantitative Susceptibility Mapping

no code implementations25 Nov 2022 Zhuang Xiong, Yang Gao, Feng Liu, Hongfu Sun

We propose an end-to-end AFfine Transformation Edited and Refined (AFTER) deep neural network for QSM, which is robust against arbitrary acquisition orientation and spatial resolution up to 0. 6 mm isotropic at the finest.

Watermarking for Out-of-distribution Detection

1 code implementation27 Oct 2022 Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, Bo Han

Out-of-distribution (OOD) detection aims to identify OOD data based on representations extracted from well-trained deep models.

Out-of-Distribution Detection

Is Out-of-Distribution Detection Learnable?

no code implementations26 Oct 2022 Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, Feng Liu

Based on this observation, we next give several necessary and sufficient conditions to characterize the learnability of OOD detection in some practical scenarios.

Learning Theory Out-of-Distribution Detection +2

Cluster and Aggregate: Face Recognition with Large Probe Set

1 code implementation19 Oct 2022 Minchul Kim, Feng Liu, Anil Jain, Xiaoming Liu

Advances in attention and recurrent modules have led to feature fusion that can model the relationship among the images in the input set.

 Ranked #1 on Face Verification on IJB-B (TAR @ FAR=0.001 metric)

Face Recognition Face Verification +4

Mix and Reason: Reasoning over Semantic Topology with Data Mixing for Domain Generalization

no code implementations14 Oct 2022 Chaoqi Chen, Luyao Tang, Feng Liu, Gangming Zhao, Yue Huang, Yizhou Yu

Domain generalization (DG) enables generalizing a learning machine from multiple seen source domains to an unseen target one.

Domain Generalization Relational Reasoning

A Perceptual Quality Metric for Video Frame Interpolation

1 code implementation4 Oct 2022 Qiqi Hou, Abhijay Ghildyal, Feng Liu

In this paper, we present a dedicated perceptual quality metric for measuring video frame interpolation results.

Video Frame Interpolation

A Uniform Representation Learning Method for OCT-based Fingerprint Presentation Attack Detection and Reconstruction

no code implementations25 Sep 2022 Wentian Zhang, Haozhe Liu, Feng Liu, Raghavendra Ramachandra

For reconstruction performance, our method achieves the best performance with 0. 834 mIOU and 0. 937 PA. By comparing with the recognition performance on surface 2D fingerprints, the effectiveness of our proposed method on high quality subsurface fingerprint reconstruction is further proved.

Representation Learning Semantic Segmentation

Two-Stage Submodular Optimization of Dynamic Thermal Rating for Risk Mitigation Considering Placement and Operation Schedule

no code implementations20 Sep 2022 Qinfei Long, Junhong Liu, Chenhao Ren, Wenqian Yin, Feng Liu, Yunhe Hou

From the perspectives of service life and Braess paradox, it is important and challenging to jointly optimize the DTR placement and operation schedule for changing system state, which is a two-stage combinatorial problem with only discrete variables, suffering from no approximation guarantee and dimension curse only based on traditional models.

Neighborhood Collective Estimation for Noisy Label Identification and Correction

1 code implementation5 Aug 2022 Jichang Li, Guanbin Li, Feng Liu, Yizhou Yu

Specifically, our method is divided into two steps: 1) Neighborhood Collective Noise Verification to separate all training samples into a clean or noisy subset, 2) Neighborhood Collective Label Correction to relabel noisy samples, and then auxiliary techniques are used to assist further model optimization.

Learning with noisy labels Model Optimization

Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels

1 code implementation29 Jul 2022 Ganlong Zhao, Guanbin Li, Yipeng Qin, Feng Liu, Yizhou Yu

In this paper, we propose a two-stage clean samples identification method to address the aforementioned challenge.

Ranked #3 on Image Classification on Clothing1M (using extra training data)

Image Classification

Shift-tolerant Perceptual Similarity Metric

2 code implementations27 Jul 2022 Abhijay Ghildyal, Feng Liu

This paper studies the effect of small misalignment, specifically a small shift between the input and reference image, on existing metrics, and accordingly develops a shift-tolerant similarity metric.

Video Quality Assessment

2D GANs Meet Unsupervised Single-view 3D Reconstruction

no code implementations20 Jul 2022 Feng Liu, Xiaoming Liu

In light of this, we propose a novel image-conditioned neural implicit field, which can leverage 2D supervisions from GAN-generated multi-view images and perform the single-view reconstruction of generic objects.

3D Reconstruction Image Generation +1

Controllable and Guided Face Synthesis for Unconstrained Face Recognition

2 code implementations20 Jul 2022 Feng Liu, Minchul Kim, Anil Jain, Xiaoming Liu

To address this problem, we propose a controllable face synthesis model (CFSM) that can mimic the distribution of target datasets in a style latent space.

Face Generation Face Recognition +1

A simple normalization technique using window statistics to improve the out-of-distribution generalization on medical images

1 code implementation7 Jul 2022 Chengfeng Zhou, Songchang Chen, Chenming Xu, Jun Wang, Feng Liu, Chun Zhang, Juan Ye, Hefeng Huang, Dahong Qian

In this study, we present a novel normalization technique called window normalization (WIN) to improve the model generalization on heterogeneous medical images, which is a simple yet effective alternative to existing normalization methods.

Breast Cancer Detection Out-of-Distribution Generalization

Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack

1 code implementation15 Jun 2022 Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng

The AutoAttack (AA) has been the most reliable method to evaluate adversarial robustness when considerable computational resources are available.

Adversarial Robustness Computational Efficiency

Bilateral Dependency Optimization: Defending Against Model-inversion Attacks

2 code implementations11 Jun 2022 Xiong Peng, Feng Liu, Jingfen Zhang, Long Lan, Junjie Ye, Tongliang Liu, Bo Han

To defend against MI attacks, previous work utilizes a unilateral dependency optimization strategy, i. e., minimizing the dependency between inputs (i. e., features) and outputs (i. e., labels) during training the classifier.

Multi-class Classification with Fuzzy-feature Observations: Theory and Algorithms

1 code implementation9 Jun 2022 Guangzhi Ma, Jie Lu, Feng Liu, Zhen Fang, Guangquan Zhang

Hence, in this paper, we propose a novel framework to address a new realistic problem called multi-class classification with imprecise observations (MCIMO), where we need to train a classifier with fuzzy-feature observations.

Classification Multi-class Classification

Incentive Mechanism Design for Emergency Frequency Control in Multi-Infeed Hybrid AC-DC System

no code implementations28 May 2022 Ye Liu, Chen Shen, Zhaojian Wang, Feng Liu

In multi-infeed hybrid AC-DC (MIDC) systems, the emergency frequency control (EFC) with LCC-HVDC systems participating is of vital importance for system frequency stability.

Integrally Migrating Pre-trained Transformer Encoder-decoders for Visual Object Detection

3 code implementations ICCV 2023 Feng Liu, Xiaosong Zhang, Zhiliang Peng, Zonghao Guo, Fang Wan, Xiangyang Ji, Qixiang Ye

Except for the backbone networks, however, other components such as the detector head and the feature pyramid network (FPN) remain trained from scratch, which hinders fully tapping the potential of representation models.

Decoder Few-Shot Object Detection +3

Robust Representation via Dynamic Feature Aggregation

1 code implementation16 May 2022 Haozhe Liu, Haoqin Ji, Yuexiang Li, Nanjun He, Haoqian Wu, Feng Liu, Linlin Shen, Yefeng Zheng

With the regularization and orthogonal classifier, a more compact embedding space can be obtained, which accordingly improves the model robustness against adversarial attacks.

Out of Distribution (OOD) Detection

A novel stereo matching pipeline with robustness and unfixed disparity search range

no code implementations11 Apr 2022 Jiazhi Liu, Feng Liu

The new stereo matching pipeline have the following advantages: It 1) has better generalization performance than most of the current stereo matching methods; 2) relaxes the limitation of a fixed disparity search range; 3) can handle the scenes that involve both positive and negative disparities, which has more potential applications, such as view synthesis in 3D multimedia and VR/AR.

Stereo Matching

BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources

1 code implementation6 Apr 2022 Xuanyu Zhu, Yang Gao, Feng Liu, Stuart Crozier, Hongfu Sun

The BFRnet method is compared with three conventional BFR methods and one previous deep learning method using simulated and in vivo brains from 4 healthy and 2 hemorrhagic subjects.

Undersampled MRI Reconstruction with Side Information-Guided Normalisation

no code implementations7 Mar 2022 Xinwen Liu, Jing Wang, Cheng Peng, Shekhar S. Chandra, Feng Liu, S. Kevin Zhou

In this paper, we investigate the use of such side information as normalisation parameters in a convolutional neural network (CNN) to improve undersampled MRI reconstruction.

MRI Reconstruction

Multi-channel deep convolutional neural networks for multi-classifying thyroid disease

no code implementations6 Mar 2022 Xinyu Zhang, Vincent CS. Lee, Jia Rong, James C. Lee, Jiangning Song, Feng Liu

Therefore, this study proposed a novel multi-channel convolutional neural network (CNN) architecture to address the multi-class classification task of thyroid disease.

Benchmarking Binary Classification +2

Adversarial Attack and Defense for Non-Parametric Two-Sample Tests

1 code implementation7 Feb 2022 Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan Kankanhalli

Furthermore, we theoretically find that the adversary can also degrade the lower bound of a TST's test power, which enables us to iteratively minimize the test criterion in order to search for adversarial pairs.

Adversarial Attack Vocal Bursts Valence Prediction

Balanced Graph Structure Learning for Multivariate Time Series Forecasting

1 code implementation24 Jan 2022 Weijun Chen, Yanze Wang, Chengshuo Du, Zhenglong Jia, Feng Liu, Ran Chen

However, current models do not incorporate the trade-off between efficiency and flexibility and lack the guidance of domain knowledge in the design of graph structure learning algorithms.

Graph Generation Graph Learning +3

The State of Aerial Surveillance: A Survey

no code implementations9 Jan 2022 Kien Nguyen, Clinton Fookes, Sridha Sridharan, YingLi Tian, Feng Liu, Xiaoming Liu, Arun Ross

The rapid emergence of airborne platforms and imaging sensors are enabling new forms of aerial surveillance due to their unprecedented advantages in scale, mobility, deployment and covert observation capabilities.

Motion-Adjustable Neural Implicit Video Representation

no code implementations CVPR 2022 Long Mai, Feng Liu

The model is trained end-to-end on a video to jointly determine the phase-shift values at each time with the mapping from the phase-shifted sinusoidal functions to the corresponding frame, enabling an implicit video representation.

Motion Magnification

Achieving an Accurate Random Process Model for PV Power using Cheap Data: Leveraging the SDE and Public Weather Reports

no code implementations27 Nov 2021 Yiwei Qiu, Jin Lin, Zhipeng Zhou, Ningyi Dai, Feng Liu, Yonghua Song

To fill this gap, this article finds that an accurate SDE model for PV power can be constructed by only using the cheap data from low-resolution public weather reports.

Time Series Time Series Analysis +1

FRT-PAD: Effective Presentation Attack Detection Driven by Face Related Task

1 code implementation22 Nov 2021 Wentian Zhang, Haozhe Liu, Feng Liu, Raghavendra Ramachandra, Christoph Busch

The proposed method, first introduces task specific features from other face related task, then, we design a Cross-Modal Adapter using a Graph Attention Network (GAT) to re-map such features to adapt to PAD task.

Attribute Face Presentation Attack Detection +2

Fingerprint Presentation Attack Detection by Channel-wise Feature Denoising

1 code implementation15 Nov 2021 Feng Liu, Zhe Kong, Haozhe Liu, Wentian Zhang, Linlin Shen

The proposed method learns important features of fingerprint images by weighing the importance of each channel and identifying discriminative channels and "noise" channels.

Denoising

Instant tissue field and magnetic susceptibility mapping from MR raw phase using Laplacian enabled deep neural networks

2 code implementations15 Nov 2021 Yang Gao, Zhuang Xiong, Amir Fazlollahi, Peter J Nestor, Viktor Vegh, Fatima Nasrallah, Craig Winter, G. Bruce Pike, Stuart Crozier, Feng Liu, Hongfu Sun

In addition, experiments on patients with intracranial hemorrhage and multiple sclerosis were also performed to test the generalization of the novel neural networks.

Voxel-based 3D Detection and Reconstruction of Multiple Objects from a Single Image

no code implementations NeurIPS 2021 Feng Liu, Xiaoming Liu

With complementary supervision from both 3D detection and reconstruction, one enables the 3D voxel features to be geometry and context preserving, benefiting both tasks. The effectiveness of our approach is demonstrated through 3D detection and reconstruction in single object and multiple object scenarios.

Keypoint Detection Object

A cross-modal fusion network based on self-attention and residual structure for multimodal emotion recognition

1 code implementation3 Nov 2021 Ziwang Fu, Feng Liu, HanYang Wang, Jiayin Qi, Xiangling Fu, Aimin Zhou, Zhibin Li

Firstly, we perform representation learning for audio and video modalities to obtain the semantic features of the two modalities by efficient ResNeXt and 1D CNN, respectively.

Multimodal Emotion Recognition Representation Learning

EvoGAN: An Evolutionary Computation Assisted GAN

1 code implementation22 Oct 2021 Feng Liu, HanYang Wang, Jiahao Zhang, Ziwang Fu, Aimin Zhou, Jiayin Qi, Zhibin Li

Quantitative and Qualitative results are presented on several compound expressions, and the experimental results demonstrate the feasibility and the potential of EvoGAN.

Image Generation

Fingerprints of Super Resolution Networks

no code implementations29 Sep 2021 Jeremy Vonderfecht, Feng Liu

Compared to previously studied models, SISR networks are a uniquely challenging class of image generation model from which to extract and analyze fingerprints, as they can often generate images that closely match the corresponding ground truth and thus likely leave little flexibility to embed signatures.

Image Generation Image Super-Resolution +1

Approaching the Transient Stability Boundary of a Power System: Theory and Applications

no code implementations26 Sep 2021 Peng Yang, Feng Liu, Wei Wei, Zhaojian Wang

Estimating the stability boundary is a fundamental and challenging problem in transient stability studies.

Manifold-preserved GANs

no code implementations18 Sep 2021 Haozhe Liu, Hanbang Liang, Xianxu Hou, Haoqian Wu, Feng Liu, Linlin Shen

Generative Adversarial Networks (GANs) have been widely adopted in various fields.

Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-Mixing

1 code implementation9 Sep 2021 Zhe Kong, Wentian Zhang, Feng Liu, Wenhan Luo, Haozhe Liu, Linlin Shen, Raghavendra Ramachandra

Even though there are numerous Presentation Attack Detection (PAD) techniques based on both deep learning and hand-crafted features, the generalization of PAD for unknown PAI is still a challenging problem.

Self-Supervised Learning

View Synthesis of Dynamic Scenes based on Deep 3D Mask Volume

no code implementations ICCV 2021 Kai-En Lin, Guowei Yang, Lei Xiao, Feng Liu, Ravi Ramamoorthi

Image view synthesis has seen great success in reconstructing photorealistic visuals, thanks to deep learning and various novel representations.

Escaping the Gradient Vanishing: Periodic Alternatives of Softmax in Attention Mechanism

1 code implementation16 Aug 2021 Shulun Wang, Bin Liu, Feng Liu

Softmax is widely used in neural networks for multiclass classification, gate structure and attention mechanisms.

StereoRel: Relational Triple Extraction from a Stereoscopic Perspective

no code implementations ACL 2021 Xuetao Tian, Liping Jing, Lu He, Feng Liu

Relational triple extraction is critical to understanding massive text corpora and constructing large-scale knowledge graph, which has attracted increasing research interest.

Local Reweighting for Adversarial Training

no code implementations30 Jun 2021 Ruize Gao, Feng Liu, Kaiwen Zhou, Gang Niu, Bo Han, James Cheng

However, when tested on attacks different from the given attack simulated in training, the robustness may drop significantly (e. g., even worse than no reweighting).

Learning Bounds for Open-Set Learning

1 code implementation30 Jun 2021 Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang

In this paper, we target a more challenging and realistic setting: open-set learning (OSL), where there exist test samples from the classes that are unseen during training.

Learning Theory Open Set Learning +1

Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning

no code implementations25 Jun 2021 Weiwen Liu, Feng Liu, Ruiming Tang, Ben Liao, Guangyong Chen, Pheng Ann Heng

Fairness in recommendation has attracted increasing attention due to bias and discrimination possibly caused by traditional recommenders.

Fairness Recommendation Systems +2

Augmented Synchronization of Power Systems

no code implementations24 Jun 2021 Peng Yang, Feng Liu, Tao Liu, David J. Hill

Here, we formulate the empirical wisdom by the concept of augmented synchronization and aim to bridge such a theory-practice gap.

Fast Monte Carlo Rendering via Multi-Resolution Sampling

1 code implementation24 Jun 2021 Qiqi Hou, Zhan Li, Carl S Marshall, Selvakumar Panneer, Feng Liu

Specifically, we formulate this fusion task as a super resolution problem that generates a high resolution rendering from a low resolution input (LRHS), assisted with the HRLS rendering.

Denoising Super-Resolution

Probabilistic Margins for Instance Reweighting in Adversarial Training

1 code implementation NeurIPS 2021 Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama

Reweighting adversarial data during training has been recently shown to improve adversarial robustness, where data closer to the current decision boundaries are regarded as more critical and given larger weights.

Adversarial Robustness

Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data

1 code implementation NeurIPS 2021 Feng Liu, Wenkai Xu, Jie Lu, Danica J. Sutherland

In realistic scenarios with very limited numbers of data samples, however, it can be challenging to identify a kernel powerful enough to distinguish complex distributions.

Two-sample testing Vocal Bursts Valence Prediction

TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation

1 code implementation NeurIPS 2021 Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, William K. Cheung, James T. Kwok

To this end, we propose a target orientated hypothesis adaptation network (TOHAN) to solve the FHA problem, where we generate highly-compatible unlabeled data (i. e., an intermediate domain) to help train a target-domain classifier.

Domain Adaptation

KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation

1 code implementation11 Jun 2021 Chenhong Zhou, Feng Liu, Chen Gong, Rongfei Zeng, Tongliang Liu, William K. Cheung, Bo Han

However, in an open world, the unlabeled test images probably contain unknown categories and have different distributions from the labeled images.

Domain Adaptation Segmentation +1

Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning

no code implementations1 Jun 2021 Xuanyu Zhu, Yang Gao, Feng Liu, Stuart Crozier, Hongfu Sun

Method: A recently proposed deep learning-based QSM method, namely xQSM, is investigated to assess the accuracy of dipole inversion on reduced brain coverages.

SDNet: mutil-branch for single image deraining using swin

3 code implementations31 May 2021 Fuxiang Tan, YuTing Kong, Yingying Fan, Feng Liu, Daxin Zhou, Hao Zhang, Long Chen, Liang Gao, Yurong Qian

The former implements the basic rain pattern feature extraction, while the latter fuses different features to further extract and process the image features.

Autonomous Driving Single Image Deraining

Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction

1 code implementation CVPR 2021 Feng Liu, Luan Tran, Xiaoming Liu

That is, for a 2D image of a generic object, we decompose it into latent representations of category, shape and albedo, lighting and camera projection matrix, decode the representations to segmented 3D shape and albedo respectively, and fuse these components to render an image well approximating the input image.

3D Reconstruction

Deep Simultaneous Optimisation of Sampling and Reconstruction for Multi-contrast MRI

no code implementations31 Mar 2021 Xinwen Liu, Jing Wang, Fangfang Tang, Shekhar S. Chandra, Feng Liu, Stuart Crozier

MRI images of the same subject in different contrasts contain shared information, such as the anatomical structure.

SSIM

Accelerating Quantitative Susceptibility Mapping using Compressed Sensing and Deep Neural Network

1 code implementation17 Mar 2021 Yang Gao, Martijn Cloos, Feng Liu, Stuart Crozier, G. Bruce Pike, Hongfu Sun

In this study, a learning-based Deep Complex Residual Network (DCRNet) is proposed to recover both the magnitude and phase images from incoherently undersampled data, enabling high acceleration of QSM acquisition.

SSIM

Universal Undersampled MRI Reconstruction

no code implementations9 Mar 2021 Xinwen Liu, Jing Wang, Feng Liu, S. Kevin Zhou

Simply mixing images from multiple anatomies for training a single network does not lead to an ideal universal model due to the statistical shift among datasets of various anatomies, the need to retrain from scratch on all datasets with the addition of a new dataset, and the difficulty in dealing with imbalanced sampling when the new dataset is further of a smaller size.

Anatomy MRI Reconstruction

Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection

2 code implementations CVPR 2021 Bohao Li, Boyu Yang, Chang Liu, Feng Liu, Rongrong Ji, Qixiang Ye

Few-shot object detection has made substantial progressby representing novel class objects using the feature representation learned upon a set of base class objects.

Few-Shot Object Detection object-detection

Group-wise Inhibition based Feature Regularization for Robust Classification

1 code implementation ICCV 2021 Haozhe Liu, Haoqian Wu, Weicheng Xie, Feng Liu, Linlin Shen

The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e. g. corrupted and adversarial samples).

Classification Domain Generalization +2

Measurement of the absolute branching fractions for purely leptonic $D_s^+$ decays

no code implementations23 Feb 2021 BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, S. Ahmed, M. Albrecht, R. Aliberti, A. Amoroso, M. R. An, Q. An, X. H. Bai, Y. Bai, O. Bakina, R. Baldini Ferroli, I. Balossino, Y. Ban, K. Begzsuren, N. Berger, M. Bertani, D. Bettoni, F. Bianchi, J. Bloms, A. Bortone, I. Boyko, R. A. Briere, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, J. F. Chang, W. L. Chang, G. Chelkov, D. Y. Chen, G. Chen, H. S. Chen, M. L. Chen, S. J. Chen, X. R. Chen, Y. B. Chen, Z. J Chen, W. S. Cheng, G. Cibinetto, F. Cossio, X. F. Cui, H. L. Dai, X. C. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, Y. Ding, C. Dong, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, S. X. Du, Y. L. Fan, J. Fang, S. S. Fang, Y. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, M. Fritsch, C. D. Fu, Y. Gao, Y. G. Gao, I. Garzia, P. T. Ge, C. Geng, E. M. Gersabeck, A Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, M. Greco, L. M. Gu, M. H. Gu, S. Gu, Y. T. Gu, C. Y Guan, A. Q. Guo, L. B. Guo, R. P. Guo, Y. P. Guo, A. Guskov, T. T. Han, W. Y. Han, X. Q. Hao, F. A. Harris, K. L. He, F. H. Heinsius, C. H. Heinz, T. Held, Y. K. Heng, C. Herold, M. Himmelreich, T. Holtmann, G. Y. Hou, Y. R. Hou, Z. L. Hou, H. M. Hu, J. F. Hu, T. Hu, Y. Hu, G. S. Huang, L. Q. Huang, X. T. Huang, Y. P. Huang, Z. Huang, T. Hussain, N Hüsken, W. Ikegami Andersson, W. Imoehl, M. Irshad, S. Jaeger, S. Janchiv, Q. Ji, Q. P. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, H. B. Jiang, X. S. Jiang, J. B. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, T. Johansson, N. Kalantar-Nayestanaki, X. S. Kang, R. Kappert, M. Kavatsyuk, B. C. Ke, I. K. Keshk, A. Khoukaz, P. Kiese, R. Kiuchi, R. Kliemt, L. Koch, O. B. Kolcu, B. Kopf, M. Kuemmel, M. Kuessner, A. Kupsc, M. G. Kurth, W. Kühn, J. J. Lane, J. S. Lange, P. Larin, A. Lavania, L. Lavezzi, Z. H. Lei, H. Leithoff, M. Lellmann, T. Lenz, C. Li, C. H. Li, Cheng Li, D. M. Li, F. Li, G. Li, H. Li, H. B. Li, H. J. Li, J. L. Li, J. Q. Li, J. S. Li, Ke Li, L. K. Li, Lei LI, P. R. Li, S. Y. Li, W. D. Li, W. G. Li, X. H. Li, X. L. Li, Xiaoyu Li, Z. Y. Li, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, L. Z. Liao, J. Libby, C. X. Lin, B. J. Liu, C. X. Liu, D. Liu, F. H. Liu, Fang Liu, Feng Liu, H. B. Liu, H. M. Liu, Huanhuan Liu, Huihui Liu, J. B. Liu, J. L. Liu, J. Y. Liu, K. Liu, K. Y. Liu, L. Liu, M. H. Liu, P. L. Liu, Q. Liu, S. B. Liu, Shuai Liu, T. Liu, W. M. Liu, X. Liu, Y. Liu, Y. B. Liu, Z. A. Liu, Z. Q. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. D. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. P. Lu, C. L. Luo, M. X. Luo, P. W. Luo, T. Luo, X. L. Luo, S. Lusso, X. R. Lyu, F. C. Ma, H. L. Ma, L. L. Ma, M. M. Ma, Q. M. Ma, R. Q. Ma, R. T. Ma, X. X. Ma, X. Y. Ma, F. E. Maas, M. Maggiora, S. Maldaner, S. Malde, A. Mangoni, Y. J. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, T. J. Min, R. E. Mitchell, X. H. Mo, Y. J. Mo, N. Yu. Muchnoi, H. Muramatsu, S. Nakhoul, Y. Nefedov, F. Nerling, I. B. Nikolaev, Z. Ning, S. Nisar, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, P. Patteri, M. Pelizaeus, H. P. Peng, K. Peters, J. Pettersson, J. L. Ping, R. G. Ping, R. Poling, V. Prasad, H. Qi, H. R. Qi, K. H. Qi, M. Qi, T. Y. Qi, S. Qian, W. B. Qian, Z. Qian, C. F. Qiao, L. Q. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, S. Q. Qu, K. H. Rashid, K. Ravindran, C. F. Redmer, A. Rivetti, V. Rodin, M. Rolo, G. Rong, Ch. Rosner, M. Rump, H. S. Sang, A. Sarantsev, Y. Schelhaas, C. Schnier, K. Schoenning, M. Scodeggio, D. C. Shan, W. Shan, X. Y. Shan, J. F. Shangguan, M. Shao, C. P. Shen, H. F. Shen, P. X. Shen, X. Y. Shen, H. C. Shi, R. S. Shi, X. Shi, X. D Shi, J. J. Song, W. M. Song, Y. X. Song, S. Sosio, S. Spataro, K. X. Su, P. P. Su, F. F. Sui, G. X. Sun, H. K. Sun, J. F. Sun, L. Sun, S. S. Sun, T. Sun, W. Y. Sun, X Sun, Y. J. Sun, Y. K. Sun, Y. Z. Sun, Z. T. Sun, Y. H. Tan, Y. X. Tan, C. J. Tang, G. Y. Tang, J. Tang, J. X. Teng, V. Thoren, W. H. Tian, Y. T. Tian, I. Uman, B. Wang, C. W. Wang, D. Y. Wang, H. J. Wang, H. P. Wang, K. Wang, L. L. Wang, M. Wang, M. Z. Wang, Meng Wang, W. Wang, W. H. Wang, W. P. Wang, X. Wang, X. F. Wang, X. L. Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. Q. Wang, Y. Y. Wang, Z. Wang, Z. Y. Wang, Ziyi Wang, Zongyuan Wang, D. H. Wei, P. Weidenkaff, F. Weidner, S. P. Wen, D. J. White, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, J. F. Wu, L. H. Wu, L. J. Wu, X. Wu, Z. Wu, L. Xia, H. Xiao, S. Y. Xiao, Z. J. Xiao, X. H. Xie, Y. G. Xie, Y. H. Xie, T. Y. Xing, G. F. Xu, Q. J. Xu, W. Xu, X. P. Xu, Y. C. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, Xu Yan, H. J. Yang, H. X. Yang, L. Yang, S. L. Yang, Y. X. Yang, Yifan Yang, Zhi Yang, M. Ye, M. H. Ye, J. H. Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, T. Yu, C. Z. Yuan, L. Yuan, X. Q. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, A. Yuncu, A. A. Zafar, Y. Zeng, A. Q. Zhang, B. X. Zhang, Guangyi Zhang, H. Zhang, H. H. Zhang, H. Y. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. W. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, Jiawei Zhang, L. M. Zhang, L. Q. Zhang, Lei Zhang, S. Zhang, S. F. Zhang, Shulei Zhang, X. D. Zhang, X. Y. Zhang, Y. Zhang, Y. H. Zhang, Y. T. Zhang, Yan Zhang, Yao Zhang, Yi Zhang, Z. H. Zhang, Z. Y. Zhang, G. Zhao, J. Zhao, J. Y. Zhao, J. Z. Zhao, Lei Zhao, Ling Zhao, M. G. Zhao, Q. Zhao, S. J. Zhao, Y. B. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, J. P. Zheng, Y. Zheng, Y. H. Zheng, B. Zhong, C. Zhong, L. P. Zhou, Q. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, A. N. Zhu, J. Zhu, K. Zhu, K. J. Zhu, S. H. Zhu, T. J. Zhu, W. J. Zhu, Y. C. Zhu, Z. A. Zhu, B. S. Zou, J. H. Zou

Constraining our measurement to the Standard Model expectation of lepton universality ($R=9. 75$), we find the more precise results $\cal B(D_s^+\to \tau^+\nu_\tau) = (5. 22\pm0. 10\pm 0. 14)\times10^{-2}$ and $A_{\it CP}(\tau^\pm\nu_\tau) = (-0. 1\pm1. 9\pm1. 0)\%$.

High Energy Physics - Experiment

Meta Discovery: Learning to Discover Novel Classes given Very Limited Data

1 code implementation ICLR 2022 Haoang Chi, Feng Liu, Bo Han, Wenjing Yang, Long Lan, Tongliang Liu, Gang Niu, Mingyuan Zhou, Masashi Sugiyama

In this paper, we demystify assumptions behind NCD and find that high-level semantic features should be shared among the seen and unseen classes.

Clustering Meta-Learning +1

Cross section measurement of $e^+e^- \to p\bar{p}η$ and $e^+e^- \to p\bar{p}ω$ at center-of-mass energies between 3.773 GeV and 4.6 GeV

no code implementations8 Feb 2021 M. Ablikim, M. N. Achasov, P. Adlarson, S. Ahmed, M. Albrecht, R. Aliberti, A. Amoroso, Q. An, X. H. Bai, Y. Bai, O. Bakina, R. Baldini Ferroli, I. Balossino, Y. Ban, K. Begzsuren, N. Berger, M. Bertani, D. Bettoni, F. Bianchi, J Biernat, J. Bloms, A. Bortone, I. Boyko, R. A. Briere, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, J. F. Chang, W. L. Chang, G. Chelkov, D. Y. Chen, G. Chen, H. S. Chen, M. L. Chen, S. J. Chen, X. R. Chen, Y. B. Chen, Z. J Chen, W. S. Cheng, G. Cibinetto, F. Cossio, X. F. Cui, H. L. Dai, X. C. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, Y. Ding, C. Dong, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, S. X. Du, J. Fang, S. S. Fang, Y. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, M. Fritsch, C. D. Fu, Y. Gao, Y. G. Gao, I. Garzia, E. M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, M. Greco, L. M. Gu, M. H. Gu, S. Gu, Y. T. Gu, C. Y Guan, A. Q. Guo, L. B. Guo, R. P. Guo, Y. P. Guo, A. Guskov, T. T. Han, X. Q. Hao, F. A. Harris, K. L. He, F. H. Heinsius, C. H. Heinz, T. Held, Y. K. Heng, C. Herold, M. Himmelreich, T. Holtmann, Y. R. Hou, Z. L. Hou, H. M. Hu, J. F. Hu, T. Hu, Y. Hu, G. S. Huang, L. Q. Huang, X. T. Huang, Y. P. Huang, Z. Huang, T. Hussain, N. Hüsken, W. Ikegami Andersson, W. Imoehl, M. Irshad, S. Jaeger, S. Janchiv, Q. Ji, Q. P. Ji, X. B. Ji, X. L. Ji, H. B. Jiang, X. S. Jiang, J. B. Jiao, Z. Jiao, S. Jin, Y. Jin, T. Johansson, N. Kalantar-Nayestanaki, X. S. Kang, R. Kappert, M. Kavatsyuk, B. C. Ke, I. K. Keshk, A. Khoukaz, P. Kiese, R. Kiuchi, R. Kliemt, L. Koch, O. B. Kolcu, B. Kopf, M. Kuemmel, M. Kuessner, A. Kupsc, M. G. Kurth, W. Kühn, J. J. Lane, J. S. Lange, P. Larin, A. Lavania, L. Lavezzi, Z. H. Lei, H. Leithoff, M. Lellmann, T. Lenz, C. Li, C. H. Li, Cheng Li, D. M. Li, F. Li, G. Li, H. Li, H. B. Li, H. J. Li, J. L. Li, J. Q. Li, Ke Li, L. K. Li, Lei LI, P. L. Li, P. R. Li, S. Y. Li, W. D. Li, W. G. Li, X. H. Li, X. L. Li, Z. Y. Li, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, L. Z. Liao, J. Libby, C. X. Lin, B. J. Liu, C. X. Liu, D. Liu, F. H. Liu, Fang Liu, Feng Liu, H. B. Liu, H. M. Liu, Huanhuan Liu, Huihui Liu, J. B. Liu, J. Y. Liu, K. Liu, K. Y. Liu, L. Liu, M. H. Liu, Q. Liu, S. B. Liu, Shuai Liu, T. Liu, W. M. Liu, X. Liu, Y. B. Liu, Z. A. Liu, Z. Q. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. D. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. P. Lu, C. L. Luo, M. X. Luo, P. W. Luo, T. Luo, X. L. Luo, S. Lusso, X. R. Lyu, F. C. Ma, H. L. Ma, L. L. Ma, M. M. Ma, Q. M. Ma, R. Q. Ma, R. T. Ma, X. X. Ma, X. Y. Ma, F. E. Maas, M. Maggiora, S. Maldaner, S. Malde, Q. A. Malik, A. Mangoni, Y. J. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, T. J. Min, R. E. Mitchell, X. H. Mo, Y. J. Mo, N. Yu. Muchnoi, H. Muramatsu, S. Nakhoul, Y. Nefedov, F. Nerling, I. B. Nikolaev, Z. Ning, S. Nisar, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, P. Patteri, M. Pelizaeus, H. P. Peng, K. Peters, J. Pettersson, J. L. Ping, R. G. Ping, A. Pitka, R. Poling, V. Prasad, H. Qi, H. R. Qi, K. H. Qi, M. Qi, T. Y. Qi, S. Qian, W. B. Qian, Z. Qian, C. F. Qiao, L. Q. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, S. Q. Qu, K. H. Rashid, K. Ravindran, C. F. Redmer, A. Rivetti, V. Rodin, M. Rolo, G. Rong, Ch. Rosner, M. Rump, H. S. Sang, A. Sarantsev, Y. Schelhaas, C. Schnier, K. Schoenning, M. Scodeggio, D. C. Shan, W. Shan, X. Y. Shan, M. Shao, C. P. Shen, P. X. Shen, X. Y. Shen, H. C. Shi, R. S. Shi, X. Shi, X. D Shi, J. J. Song, W. M. Song, Y. X. Song, S. Sosio, S. Spataro, K. X. Su, F. F. Sui, G. X. Sun, J. F. Sun, L. Sun, S. S. Sun, T. Sun, W. Y. Sun, X Sun, Y. J. Sun, Y. K. Sun, Y. Z. Sun, Z. T. Sun, Y. H. Tan, Y. X. Tan, C. J. Tang, G. Y. Tang, J. Tang, J. X. Teng, V. Thoren, I. Uman, B. Wang, C. W. Wang, D. Y. Wang, H. P. Wang, K. Wang, L. L. Wang, M. Wang, M. Z. Wang, Meng Wang, W. H. Wang, W. P. Wang, X. Wang, X. F. Wang, X. L. Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. Q. Wang, Z. Wang, Z. Y. Wang, Ziyi Wang, Zongyuan Wang, D. H. Wei,