Search Results for author: Han Yang

Found 31 papers, 20 papers with code

LightGBM robust optimization algorithm based on topological data analysis

no code implementations19 Jun 2024 Han Yang, Guangjun Qin, Ziyuan Liu, Yongqing Hu, Qinglong Dai

To enhance the robustness of the Light Gradient Boosting Machine (LightGBM) algorithm for image classification, a topological data analysis (TDA)-based robustness optimization algorithm for LightGBM, TDA-LightGBM, is proposed to address the interference of noise on image classification.

Classification Feature Engineering +2

Benchmarking Retrieval-Augmented Large Language Models in Biomedical NLP: Application, Robustness, and Self-Awareness

no code implementations13 May 2024 Mingchen Li, Zaifu Zhan, Han Yang, Yongkang Xiao, Jiatan Huang, Rui Zhang

To this end, we proposed an evaluation framework to assess the RALs' performance on different biomedical NLP tasks and establish four different testbeds based on the aforementioned fundamental abilities.

Benchmarking counterfactual +5

Product-Level Try-on: Characteristics-preserving Try-on with Realistic Clothes Shading and Wrinkles

no code implementations20 Jan 2024 Yanlong Zang, Han Yang, Jiaxu Miao, Yi Yang

Image-based virtual try-on systems, which fit new garments onto human portraits, are gaining research attention. An ideal pipeline should preserve the static features of clothes(like textures and logos)while also generating dynamic elements(e. g. shadows, folds)that adapt to the model's pose and environment. Previous works fail specifically in generating dynamic features, as they preserve the warped in-shop clothes trivially with predicted an alpha mask by composition. To break the dilemma of over-preserving and textures losses, we propose a novel diffusion-based Product-level virtual try-on pipeline,\ie PLTON, which can preserve the fine details of logos and embroideries while producing realistic clothes shading and wrinkles. The main insights are in three folds:1)Adaptive Dynamic Rendering:We take a pre-trained diffusion model as a generative prior and tame it with image features, training a dynamic extractor from scratch to generate dynamic tokens that preserve high-fidelity semantic information.

Denoising Virtual Try-on

SPT: Fine-Tuning Transformer-based Language Models Efficiently with Sparsification

1 code implementation16 Dec 2023 Yuntao Gui, Xiao Yan, Peiqi Yin, Han Yang, James Cheng

Thus, we design the sparse MHA module, which computes and stores only large attention weights to reduce memory consumption, and the routed FFN module, which dynamically activates a subset of model parameters for each token to reduce computation cost.


HyperLips: Hyper Control Lips with High Resolution Decoder for Talking Face Generation

1 code implementation9 Oct 2023 Yaosen Chen, Yu Yao, Zhiqiang Li, Wei Wang, Yanru Zhang, Han Yang, Xuming Wen

First, FaceEncoder is used to obtain latent code by extracting features from the visual face information taken from the video source containing the face frame. Then, HyperConv, which weighting parameters are updated by HyperNet with the audio features as input, will modify the latent code to synchronize the lip movement with the audio.

Decoder Talking Face Generation

SILT: Shadow-aware Iterative Label Tuning for Learning to Detect Shadows from Noisy Labels

1 code implementation ICCV 2023 Han Yang, Tianyu Wang, Xiaowei Hu, Chi-Wing Fu

Existing shadow detection datasets often contain missing or mislabeled shadows, which can hinder the performance of deep learning models trained directly on such data.

Shadow Detection

Baby's CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models

1 code implementation3 Aug 2023 Zheyu Zhang, Han Yang, Bolei Ma, David Rügamer, Ercong Nie

Large Language Models (LLMs) demonstrate remarkable performance on a variety of natural language understanding (NLU) tasks, primarily due to their in-context learning ability.

In-Context Learning Natural Language Understanding +1

NLUT: Neural-based 3D Lookup Tables for Video Photorealistic Style Transfer

1 code implementation16 Mar 2023 Yaosen Chen, Han Yang, Yuexin Yang, Yuegen Liu, Wei Wang, Xuming Wen, Chaoping Xie

However, existing methods obtain stylized video sequences by performing frame-by-frame photorealistic style transfer, which is inefficient and does not ensure the temporal consistency of the stylized video.

8k Style Transfer

Lung Nodule Segmentation and Uncertain Region Prediction with an Uncertainty-Aware Attention Mechanism

no code implementations15 Mar 2023 Han Yang, Qiuli Wang, Yue Zhang, Zhulin An, Chen Liu, Xiaohong Zhang, S. Kevin Zhou

Radiologists possess diverse training and clinical experiences, leading to variations in the segmentation annotations of lung nodules and resulting in segmentation uncertainty. Conventional methods typically select a single annotation as the learning target or attempt to learn a latent space comprising multiple annotations.

Lung Nodule Segmentation Segmentation

Retinex-qDPC: automatic background rectified quantitative differential phase contrast imaging

no code implementations21 Jul 2022 Shuhe Zhang, Tao Peng, Zeyu Ke, Han Yang, Tos T. J. M. Berendschot, Jinhua Zhou

To tackle the mismatch of background and increases the experimental robustness, we propose the Retinex-qDPC in which we use the images edge features as data fidelity term yielding L2-Retinex-qDPC and L1-Retinex-qDPC for high background-robustness qDPC reconstruction.

Understanding and Improving Graph Injection Attack by Promoting Unnoticeability

1 code implementation ICLR 2022 Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng

Recently Graph Injection Attack (GIA) emerges as a practical attack scenario on Graph Neural Networks (GNNs), where the adversary can merely inject few malicious nodes instead of modifying existing nodes or edges, i. e., Graph Modification Attack (GMA).

Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs

3 code implementations11 Feb 2022 Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng

Despite recent success in using the invariance principle for out-of-distribution (OOD) generalization on Euclidean data (e. g., images), studies on graph data are still limited.

Drug Discovery Graph Learning +1

Uncertainty-Guided Lung Nodule Segmentation with Feature-Aware Attention

2 code implementations24 Oct 2021 Han Yang, Lu Shen, Mengke Zhang, Qiuli Wang

With an Uncertainty-Aware Module, this network can provide a Multi-Confidence Mask (MCM), pointing out regions with different segmentation uncertainty levels.

Lung Nodule Segmentation Segmentation

Disentangled Cycle Consistency for Highly-realistic Virtual Try-On

1 code implementation CVPR 2021 Chongjian Ge, Yibing Song, Yuying Ge, Han Yang, Wei Liu, Ping Luo

To this end, DCTON can be naturally trained in a self-supervised manner following cycle consistency learning.

Virtual Try-on

Calibrating and Improving Graph Contrastive Learning

1 code implementation27 Jan 2021 Kaili Ma, Haochen Yang, Han Yang, Yongqiang Chen, James Cheng

To assess the discrepancy between the prediction and the ground-truth in the downstream tasks for these contrastive pairs, we adapt the expected calibration error (ECE) to graph contrastive learning.

Contrastive Learning Graph Clustering +4

Time-Continuous Energy-Conservation Neural Network for Structural Dynamics Analysis

no code implementations16 Dec 2020 Yuan Feng, Hexiang Wang, Han Yang, Fangbo Wang

Although the basic neural network provides an alternative approach for structural dynamics analysis, the lack of physics law inside the neural network limits the model accuracy and fidelity.

Rethinking Graph Regularization for Graph Neural Networks

1 code implementation4 Sep 2020 Han Yang, Kaili Ma, James Cheng

The graph Laplacian regularization term is usually used in semi-supervised representation learning to provide graph structure information for a model $f(X)$.

Node Classification Representation Learning

Towards Photo-Realistic Virtual Try-On by Adaptively Generating-Preserving Image Content

1 code implementation CVPR 2020 Han Yang, Ruimao Zhang, Xiaobao Guo, Wei Liu, Wangmeng Zuo, Ping Luo

Second, a clothes warping module warps clothes image according to the generated semantic layout, where a second-order difference constraint is introduced to stabilize the warping process during training. Third, an inpainting module for content fusion integrates all information (e. g. reference image, semantic layout, warped clothes) to adaptively produce each semantic part of human body.

Semantic Segmentation Virtual Try-on

CPR-GCN: Conditional Partial-Residual Graph Convolutional Network in Automated Anatomical Labeling of Coronary Arteries

no code implementations CVPR 2020 Han Yang, Xingjian Zhen, Ying Chi, Lei Zhang, Xian-Sheng Hua

On the technical side, the Partial-Residual GCN takes the position features of the branches, with the 3D spatial image features as conditions, to predict the label for each branches.

Anatomy Graph Neural Network +1

Towards Photo-Realistic Virtual Try-On by Adaptively Generating$\leftrightarrow$Preserving Image Content

3 code implementations12 Mar 2020 Han Yang, Ruimao Zhang, Xiaobao Guo, Wei Liu, WangMeng Zuo, Ping Luo

First, a semantic layout generation module utilizes semantic segmentation of the reference image to progressively predict the desired semantic layout after try-on.

Ranked #4 on Virtual Try-on on VITON (IS metric)

Semantic Segmentation Virtual Try-on

Self-Enhanced GNN: Improving Graph Neural Networks Using Model Outputs

1 code implementation18 Feb 2020 Han Yang, Xiao Yan, Xinyan Dai, Yongqiang Chen, James Cheng

In this paper, we propose self-enhanced GNN (SEG), which improves the quality of the input data using the outputs of existing GNN models for better performance on semi-supervised node classification.

General Classification Node Classification

Convolutional Embedding for Edit Distance

2 code implementations31 Jan 2020 Xinyan Dai, Xiao Yan, Kaiwen Zhou, Yuxuan Wang, Han Yang, James Cheng

Edit-distance-based string similarity search has many applications such as spell correction, data de-duplication, and sequence alignment.

Hyper-Sphere Quantization: Communication-Efficient SGD for Federated Learning

1 code implementation12 Nov 2019 Xinyan Dai, Xiao Yan, Kaiwen Zhou, Han Yang, Kelvin K. W. Ng, James Cheng, Yu Fan

In particular, at the high compression ratio end, HSQ provides a low per-iteration communication cost of $O(\log d)$, which is favorable for federated learning.

Federated Learning Quantization

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