Search Results for author: Bo Hu

Found 40 papers, 12 papers with code

Gradual Residuals Alignment: A Dual-Stream Framework for GAN Inversion and Image Attribute Editing

no code implementations22 Feb 2024 Hao Li, Mengqi Huang, Lei Zhang, Bo Hu, Yi Liu, Zhendong Mao

GAN-based image attribute editing firstly leverages GAN Inversion to project real images into the latent space of GAN and then manipulates corresponding latent codes.


Diffusion Model Based Visual Compensation Guidance and Visual Difference Analysis for No-Reference Image Quality Assessment

no code implementations22 Feb 2024 Zhaoyang Wang, Bo Hu, Mingyang Zhang, Jie Li, Leida Li, Maoguo Gong, Xinbo Gao

Firstly, we devise a new diffusion restoration network that leverages the produced enhanced image and noise-containing images, incorporating nonlinear features obtained during the denoising process of the diffusion model, as high-level visual information.

Denoising No-Reference Image Quality Assessment +1

Online Signed Sampling of Bandlimited Graph Signals

no code implementations16 Feb 2024 Wenwei Liu, Hui Feng, Feng Ji, Bo Hu

In this paper, we are interested in how to sample based on sign information in an online manner, by which the direction of the original graph signal can be estimated.

Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigm

1 code implementation16 Feb 2024 Yuanzhen Xie, Xinzhou Jin, Tao Xie, Mingxiong Lin, Liang Chen, Chenyun Yu, Lei Cheng, Chengxiang Zhuo, Bo Hu, Zang Li

To improve the contextual learning capabilities of LLMs in text-to-SQL, a workflow paradigm method is proposed, aiming to enhance the attention and problem-solving scope of LLMs through decomposition.

Active Learning In-Context Learning +1

Graph Relation Distillation for Efficient Biomedical Instance Segmentation

2 code implementations12 Jan 2024 Xiaoyu Liu, Yueyi Zhang, Zhiwei Xiong, Wei Huang, Bo Hu, Xiaoyan Sun, Feng Wu

IGD constructs a graph representing instance features and relations, transferring these two types of knowledge by enforcing instance graph consistency.

Instance Segmentation Knowledge Distillation +2

RSMT: Real-time Stylized Motion Transition for Characters

1 code implementation21 Jun 2023 Xiangjun Tang, Linjun Wu, He Wang, Bo Hu, Xu Gong, Yuchen Liao, Songnan Li, Qilong Kou, Xiaogang Jin

Styled online in-between motion generation has important application scenarios in computer animation and games.

Feature Learning in Image Hierarchies using Functional Maximal Correlation

no code implementations31 May 2023 Bo Hu, Yuheng Bu, José C. Príncipe

This paper proposes the Hierarchical Functional Maximal Correlation Algorithm (HFMCA), a hierarchical methodology that characterizes dependencies across two hierarchical levels in multiview systems.

Self-Supervised Learning

Attention Paper: How Generative AI Reshapes Digital Shadow Industry?

no code implementations26 May 2023 Qichao Wang, Huan Ma, WenTao Wei, Hangyu Li, Liang Chen, Peilin Zhao, Binwen Zhao, Bo Hu, Shu Zhang, Zibin Zheng, Bingzhe Wu

The rapid development of digital economy has led to the emergence of various black and shadow internet industries, which pose potential risks that can be identified and managed through digital risk management (DRM) that uses different techniques such as machine learning and deep learning.


OlaGPT: Empowering LLMs With Human-like Problem-Solving Abilities

no code implementations23 May 2023 Yuanzhen Xie, Tao Xie, Mingxiong Lin, WenTao Wei, Chenglin Li, Beibei Kong, Lei Chen, Chengxiang Zhuo, Bo Hu, Zang Li

At present, most approaches focus on chains of thought (COT) and tool use, without considering the adoption and application of human cognitive frameworks.

Active Learning Decision Making +1

A Soma Segmentation Benchmark in Full Adult Fly Brain

1 code implementation CVPR 2023 Xiaoyu Liu, Bo Hu, Mingxing Li, Wei Huang, Yueyi Zhang, Zhiwei Xiong

Finally, we provide quantitative and qualitative benchmark comparisons on the testset to validate the superiority of the proposed method, as well as preliminary statistics of the reconstructed somas in the full adult fly brain from the biological perspective.

The Normalized Cross Density Functional: A Framework to Quantify Statistical Dependence for Random Processes

no code implementations9 Dec 2022 Bo Hu, Jose C. Principe

We mathematically prove that FMCA learns the dominant eigenvalues and eigenfunctions of NCD directly from realizations.

Intra-class Adaptive Augmentation with Neighbor Correction for Deep Metric Learning

1 code implementation29 Nov 2022 Zheren Fu, Zhendong Mao, Bo Hu, An-An Liu, Yongdong Zhang

They have overlooked the wide characteristic changes of different classes and can not model abundant intra-class variations for generations.

Image Augmentation Image Retrieval +5

One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation

1 code implementation22 Nov 2022 Chenglin Li, Yuanzhen Xie, Chenyun Yu, Bo Hu, Zang Li, Guoqiang Shu, XiaoHu Qie, Di Niu

CAT-ART boosts the recommendation performance in any target domain through the combined use of the learned global user representation and knowledge transferred from other domains, in addition to the original user embedding in the target domain.

Multi-Domain Recommender Systems Recommendation Systems +1

Linear RNNs Provably Learn Linear Dynamic Systems

no code implementations19 Nov 2022 Lifu Wang, Tianyu Wang, Shengwei Yi, Bo Shen, Bo Hu, Xing Cao

We study the learning ability of linear recurrent neural networks with Gradient Descent.

Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems

2 code implementations13 Oct 2022 Guanghu Yuan, Fajie Yuan, Yudong Li, Beibei Kong, Shujie Li, Lei Chen, Min Yang, Chenyun Yu, Bo Hu, Zang Li, Yu Xu, XiaoHu Qie

Existing benchmark datasets for recommender systems (RS) either are created at a small scale or involve very limited forms of user feedback.

Recommendation Systems

Sampling of Correlated Bandlimited Continuous Signals by Joint Time-vertex Graph Fourier Transform

no code implementations10 Oct 2022 Zhongyi Ni, Feng Ji, Hang Sheng, Hui Feng, Bo Hu

When sampling multiple signals, the correlation between the signals can be exploited to reduce the overall number of samples.

Constraining Pseudo-label in Self-training Unsupervised Domain Adaptation with Energy-based Model

no code implementations26 Aug 2022 Lingsheng Kong, Bo Hu, Xiongchang Liu, Jun Lu, Jane You, Xiaofeng Liu

Deep learning is usually data starved, and the unsupervised domain adaptation (UDA) is developed to introduce the knowledge in the labeled source domain to the unlabeled target domain.

Image Classification Pseudo Label +2

Entry-Flipped Transformer for Inference and Prediction of Participant Behavior

no code implementations13 Jul 2022 Bo Hu, Tat-Jen Cham

Our key idea is to model the spatio-temporal relations among participants in a manner that is robust to error accumulation during frame-wise inference and prediction.

TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback

no code implementations13 Jun 2022 Jie Wang, Fajie Yuan, Mingyue Cheng, Joemon M. Jose, Chenyun Yu, Beibei Kong, Xiangnan He, Zhijin Wang, Bo Hu, Zang Li

That is, the users and the interacted items are represented by their unique IDs, which are generally not shareable across different systems or platforms.

Recommendation Systems Transfer Learning

Real-time Controllable Motion Transition for Characters

no code implementations5 May 2022 Xiangjun Tang, He Wang, Bo Hu, Xu Gong, Ruifan Yi, Qilong Kou, Xiaogang Jin

Then, during generation, we design a transition model which is essentially a sampling strategy to sample from the learned manifold, based on the target frame and the aimed transition duration.

Cross-domain Trajectory Prediction with CTP-Net

no code implementations22 Oct 2021 Pingxuan Huang, Zhenhua Cui, Jing Li, Shenghua Gao, Bo Hu, Yanyan Fang

Further, considering the consistency between the observed and the predicted trajectories, a target domain offset discriminator is utilized to adversarially regularize the future trajectory predictions to be in line with the observed trajectories.

Domain Adaptation Pedestrian Trajectory Prediction +1

Information Theoretic Structured Generative Modeling

1 code implementation12 Oct 2021 Bo Hu, Shujian Yu, Jose C. Principe

We test the framework for estimation of mutual information and compare the results with the mutual information neural estimation (MINE), for density estimation, for conditional probability estimation in Markov models as well as for training adversarial networks.

Density Estimation

On the Provable Generalization of Recurrent Neural Networks

no code implementations NeurIPS 2021 Lifu Wang, Bo Shen, Bo Hu, Xing Cao

In this paper, using detailed analysis about the neural tangent kernel matrix, we prove a generalization error bound to learn such functions without normalized conditions and show that some notable concept classes are learnable with the numbers of iterations and samples scaling almost-polynomially in the input length $L$.

Recovery of Graph Signals from Sign Measurements

no code implementations26 Sep 2021 Wenwei Liu, Hui Feng, Kaixuan Wang, Feng Ji, Bo Hu

Sampling and interpolation have been extensively studied, in order to reconstruct or estimate the entire graph signal from the signal values on a subset of vertexes, of which most achievements are about continuous signals.

Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference

no code implementations22 Jul 2021 Xiaofeng Liu, Bo Hu, Linghao Jin, Xu Han, Fangxu Xing, Jinsong Ouyang, Jun Lu, Georges El Fakhri, Jonghye Woo

In this work, we propose a domain generalization (DG) approach to learn on several labeled source domains and transfer knowledge to a target domain that is inaccessible in training.

Bayesian Inference Domain Generalization

Optical Mouse: 3D Mouse Pose From Single-View Video

no code implementations17 Jun 2021 Bo Hu, Bryan Seybold, Shan Yang, David Ross, Avneesh Sud, Graham Ruby, Yi Liu

We present a method to infer the 3D pose of mice, including the limbs and feet, from monocular videos.

Regularized Recovery by Multi-order Partial Hypergraph Total Variation

1 code implementation19 Feb 2021 Ruyuan Qu, Jiaqi He, Hui Feng, Chongbin Xu, Bo Hu

In this work, we take this divergence into consideration, and propose a multi-order hypergraph Laplacian and the corresponding total variation.

Energy-constrained Self-training for Unsupervised Domain Adaptation

no code implementations1 Jan 2021 Xiaofeng Liu, Bo Hu, Xiongchang Liu, Jun Lu, Jane You, Lingsheng Kong

Unsupervised domain adaptation (UDA) aims to transfer the knowledge on a labeled source domain distribution to perform well on an unlabeled target domain.

Image Classification Semantic Segmentation +1

Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis

no code implementations1 Jan 2021 Xiaofeng Liu, Xiongchang Liu, Bo Hu, Wenxuan Ji, Fangxu Xing, Jun Lu, Jane You, C. -C. Jay Kuo, Georges El Fakhri, Jonghye Woo

Recent advances in unsupervised domain adaptation (UDA) show that transferable prototypical learning presents a powerful means for class conditional alignment, which encourages the closeness of cross-domain class centroids.

Medical Diagnosis Unsupervised Domain Adaptation

DR 21 South Filament: a Parsec-sized Dense Gas Accretion Flow onto the DR 21 Massive Young Cluster

no code implementations4 Dec 2020 Bo Hu, Keping Qiu, Yue Cao, Junhao Liu, Yuwei Wang, Guangxing Li, Zhiqiang Shen, Juan Li, Junzhi Wang, Bin Li, Jian Dong

DR21 south filament (DR21SF) is a unique component of the giant network of filamentary molecular clouds in the north region of Cygnus X complex.

Astrophysics of Galaxies

Sampling Policy Design for Tracking Time-Varying Graph Signals with Adaptive Budget Allocation

no code implementations14 Feb 2020 Xuan Xie, Hui Feng, Bo Hu

There have been many works that focus on the sampling set design for a static graph signal, but few for time-varying graph signals (GS).

On Critical Sampling of Time-Vertex Graph Signals

1 code implementation6 Sep 2019 Junhao Yu, Xuan Xie, Hui Feng, Bo Hu

This paper focuses on the fundamental problem of sampling and reconstruction of joint time-vertex graph signals.

Locality-constrained Spatial Transformer Network for Video Crowd Counting

1 code implementation18 Jul 2019 Yanyan Fang, Biyun Zhan, Wandi Cai, Shenghua Gao, Bo Hu

Then to relate the density maps between neighbouring frames, a Locality-constrained Spatial Transformer (LST) module is introduced to estimate the density map of next frame with that of current frame.

Crowd Counting Translation

Progress Regression RNN for Online Spatial-Temporal Action Localization in Unconstrained Videos

no code implementations1 Mar 2019 Bo Hu, Jianfei Cai, Tat-Jen Cham, Junsong Yuan

Previous spatial-temporal action localization methods commonly follow the pipeline of object detection to estimate bounding boxes and labels of actions.

object-detection Object Detection +3

Talent Search and Recommendation Systems at LinkedIn: Practical Challenges and Lessons Learned

no code implementations18 Sep 2018 Sahin Cem Geyik, Qi Guo, Bo Hu, Cagri Ozcaglar, Ketan Thakkar, Xianren Wu, Krishnaram Kenthapadi

LinkedIn Talent Solutions business contributes to around 65% of LinkedIn's annual revenue, and provides tools for job providers to reach out to potential candidates and for job seekers to find suitable career opportunities.

Information Retrieval Recommendation Systems +1

Deploying Deep Ranking Models for Search Verticals

no code implementations6 Jun 2018 Rohan Ramanath, Gungor Polatkan, Liqin Xu, Harold Lee, Bo Hu, Shan Zhou

In this paper, we present an architecture executing a complex machine learning model such as a neural network capturing semantic similarity between a query and a document; and deploy to a real-world production system serving 500M+users.

BIG-bench Machine Learning Semantic Similarity +1

Traffic-Aware Transmission Mode Selection in D2D-enabled Cellular Networks with Token System

no code implementations2 Mar 2017 Yiling Yuan, Tao Yang, Hui Feng, Bo Hu, Jianqiu Zhang, Bin Wang, Qiyong Lu

We consider a D2D-enabled cellular network where user equipments (UEs) owned by rational users are incentivized to form D2D pairs using tokens.

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