Search Results for author: He Liu

Found 17 papers, 6 papers with code

A Hubness Perspective on Representation Learning for Graph-Based Multi-View Clustering

1 code implementation CVPR 2025 Zheming Xu, He Liu, Congyan Lang, Tao Wang, Yidong Li, Michael C. Kampffmeyer

To the best of our knowledge, we are the first to highlight the detrimental effect of hubness in GMVC methods and introduce the hubREP (hub-aware Representation Embedding and Pairing) framework.

Clustering Representation Learning

Precise Facial Landmark Detection by Dynamic Semantic Aggregation Transformer

1 code implementation1 Dec 2024 Jun Wan, He Liu, Yujia Wu, Zhihui Lai, Wenwen Min, Jun Liu

Finally, by integrating the DSA model and DSS model into our proposed DSAT in both dynamic architecture and dynamic parameter manners, more specialized features can be learned for achieving more precise face alignment.

Face Alignment Facial Landmark Detection

An Artificial Neural Network for Image Classification Inspired by Aversive Olfactory Learning Circuits in Caenorhabditis Elegans

no code implementations28 Aug 2024 Xuebin Wang, Chunxiuzi Liu, Meng Zhao, Ke Zhang, Zengru Di, He Liu

This study introduces an artificial neural network (ANN) for image classification task, inspired by the aversive olfactory learning circuits of the nematode Caenorhabditis elegans (C. elegans).

Classification image-classification +1

Small Scale Data-Free Knowledge Distillation

1 code implementation CVPR 2024 He Liu, Yikai Wang, Huaping Liu, Fuchun Sun, Anbang Yao

In this line of research, existing methods typically follow an inversion-and-distillation paradigm in which a generative adversarial network on-the-fly trained with the guidance of the pre-trained teacher network is used to synthesize a large-scale sample set for knowledge distillation.

Data-free Knowledge Distillation Generative Adversarial Network +3

Transparent Object Depth Completion

no code implementations24 May 2024 Yifan Zhou, Wanli Peng, Zhongyu Yang, He Liu, Yi Sun

The perception of transparent objects for grasp and manipulation remains a major challenge, because existing robotic grasp methods which heavily rely on depth maps are not suitable for transparent objects due to their unique visual properties.

Depth Completion Depth Estimation +2

TransFR: Transferable Federated Recommendation with Pre-trained Language Models

no code implementations2 Feb 2024 Honglei Zhang, He Liu, Haoxuan Li, Yidong Li

To this end, we propose a transferable federated recommendation model with universal textual representations, TransFR, which delicately incorporates the general capabilities empowered by pre-trained language models and the personalized abilities by fine-tuning local private data.

Privacy Preserving

HRTF upsampling with a generative adversarial network using a gnomonic equiangular projection

1 code implementation9 Jun 2023 Aidan O. T. Hogg, Mads Jenkins, He Liu, Isaac Squires, Samuel J. Cooper, Lorenzo Picinali

An individualised head-related transfer function (HRTF) is very important for creating realistic virtual reality (VR) and augmented reality (AR) environments.

Generative Adversarial Network Super-Resolution

KinD-LCE Curve Estimation And Retinex Fusion On Low-Light Image

no code implementations19 Jul 2022 Xiaochun Lei, Weiliang Mai, Junlin Xie, He Liu, Zetao Jiang, Zhaoting Gong, Chang Lu, Linjun Lu

The proposed method, KinD-LCE, uses a light curve estimation module to enhance the illumination map in the Retinex decomposed image, improving the overall image brightness.

Instance Segmentation object-detection +4

Multimodal Dual Emotion with Fusion of Visual Sentiment for Rumor Detection

no code implementations25 Apr 2022 Ge Wang, Li Tan, Ziliang Shang, He Liu

In recent years, rumors have had a devastating impact on society, making rumor detection a significant challenge.

Deep Probabilistic Graph Matching

no code implementations5 Jan 2022 He Liu, Tao Wang, Yidong Li, Congyan Lang, Songhe Feng, Haibin Ling

Most previous learning-based graph matching algorithms solve the \textit{quadratic assignment problem} (QAP) by dropping one or more of the matching constraints and adopting a relaxed assignment solver to obtain sub-optimal correspondences.

Graph Matching

GLAN: A Graph-based Linear Assignment Network

no code implementations5 Jan 2022 He Liu, Tao Wang, Congyan Lang, Songhe Feng, Yi Jin, Yidong Li

The experimental results on a synthetic dataset reveal that our method outperforms state-of-the-art baselines and achieves consistently high accuracy with the increment of the problem size.

Multi-Object Tracking

Joint Graph Learning and Matching for Semantic Feature Correspondence

2 code implementations1 Sep 2021 He Liu, Tao Wang, Yidong Li, Congyan Lang, Yi Jin, Haibin Ling

In this paper, we propose a joint \emph{graph learning and matching} network, named GLAM, to explore reliable graph structures for boosting graph matching.

Graph Learning Graph Matching +1

Target-specified Sequence Labeling with Multi-head Self-attention for Target-oriented Opinion Words Extraction

1 code implementation NAACL 2021 Yuhao Feng, Yanghui Rao, Yuyao Tang, Ninghua Wang, He Liu

Many recent works on ABSA focus on Target-oriented Opinion Words (or Terms) Extraction (TOWE), which aims at extracting the corresponding opinion words for a given opinion target.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +4

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