Search Results for author: Ye Zhu

Found 39 papers, 19 papers with code

What is Dataset Distillation Learning?

1 code implementation6 Jun 2024 William Yang, Ye Zhu, Zhiwei Deng, Olga Russakovsky

We reveal distilled data cannot serve as a substitute for real data during training outside the standard evaluation setting for dataset distillation.

Dataset Distillation

A Survey on the Real Power of ChatGPT

1 code implementation22 Apr 2024 Ming Liu, Ran Liu, Ye Zhu, Hua Wang, Youyang Qu, Rongsheng Li, Yongpan Sheng, Wray Buntine

ChatGPT has changed the AI community and an active research line is the performance evaluation of ChatGPT.

Anomaly Detection Based on Isolation Mechanisms: A Survey

no code implementations16 Mar 2024 Yang Cao, Haolong Xiang, Hang Zhang, Ye Zhu, Kai Ming Ting

Anomaly detection is a longstanding and active research area that has many applications in domains such as finance, security, and manufacturing.

Unsupervised Anomaly Detection

DETER: Detecting Edited Regions for Deterring Generative Manipulations

no code implementations16 Dec 2023 Sai Wang, Ye Zhu, Ruoyu Wang, Amaya Dharmasiri, Olga Russakovsky, Yu Wu

While face swapping and attribute editing are performed on similar face regions such as eyes and nose, the inpainting operation can be performed on random image regions, removing the spurious correlations of previous datasets.

Attribute Face Swapping +1

Discovery and Expansion of New Domains within Diffusion Models

no code implementations13 Oct 2023 Ye Zhu, Yu Wu, Duo Xu, Zhiwei Deng, Yan Yan, Olga Russakovsky

In this work, we study the generalization properties of diffusion models in a few-shot setup, introduce a novel tuning-free paradigm to synthesize the target out-of-domain (OOD) data, and demonstrate its advantages compared to existing methods in data-sparse scenarios with large domain gaps.

Denoising Image Generation

Distribution-Based Trajectory Clustering

no code implementations8 Oct 2023 Zi Jing Wang, Ye Zhu, Kai Ming Ting

Independent of the distance measure employed, existing clustering algorithms have another challenge: either effectiveness issues or high time complexity.

Clustering Trajectory Clustering

Diffusion in Diffusion: Cyclic One-Way Diffusion for Text-Vision-Conditioned Generation

1 code implementation14 Jun 2023 Ruoyu Wang, Yongqi Yang, Zhihao Qian, Ye Zhu, Yu Wu

In this work, we investigate the diffusion (physics) in diffusion (machine learning) properties and propose our Cyclic One-Way Diffusion (COW) method to control the direction of diffusion phenomenon given a pre-trained frozen diffusion model for versatile customization application scenarios, where the low-level pixel information from the conditioning needs to be preserved.

Denoising Image Generation

Ripple Knowledge Graph Convolutional Networks For Recommendation Systems

no code implementations2 May 2023 Chen Li, Yang Cao, Ye Zhu, Debo Cheng, Chengyuan Li, Yasuhiko Morimoto

Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model's interpretability and accuracy.

Knowledge Graphs Recommendation Systems

Denoising Diffusion Probabilistic Models to Predict the Density of Molecular Clouds

1 code implementation4 Apr 2023 Duo Xu, Jonathan C. Tan, Chia-Jung Hsu, Ye Zhu

We introduce the state-of-the-art deep learning Denoising Diffusion Probabilistic Model (DDPM) as a method to infer the volume or number density of giant molecular clouds (GMCs) from projected mass surface density maps.

Denoising

Inherent Consistent Learning for Accurate Semi-supervised Medical Image Segmentation

2 code implementations24 Mar 2023 Ye Zhu, Jie Yang, Si-Qi Liu, Ruimao Zhang

Semi-supervised medical image segmentation has attracted much attention in recent years because of the high cost of medical image annotations.

Image Segmentation Segmentation +2

Detecting Change Intervals with Isolation Distributional Kernel

2 code implementations30 Dec 2022 Yang Cao, Ye Zhu, Kai Ming Ting, Flora D. Salim, Hong Xian Li, Luxing Yang, Gang Li

Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis.

Change Point Detection

Domain generalization Person Re-identification on Attention-aware multi-operation strategery

no code implementations19 Oct 2022 Yingchun Guo, Huan He, Ye Zhu, Yang Yu

Domain generalization person re-identification (DG Re-ID) aims to directly deploy a model trained on the source domain to the unseen target domain with good generalization, which is a challenging problem and has practical value in a real-world deployment.

Domain Generalization Person Re-Identification

Robust Human Matting via Semantic Guidance

1 code implementation11 Oct 2022 Xiangguang Chen, Ye Zhu, Yu Li, Bingtao Fu, Lei Sun, Ying Shan, Shan Liu

Unlike previous works, our framework is data efficient, which requires a small amount of matting ground-truth to learn to estimate high quality object mattes.

Image Matting Segmentation

Vision+X: A Survey on Multimodal Learning in the Light of Data

no code implementations5 Oct 2022 Ye Zhu, Yu Wu, Nicu Sebe, Yan Yan

We are perceiving and communicating with the world in a multisensory manner, where different information sources are sophisticatedly processed and interpreted by separate parts of the human brain to constitute a complex, yet harmonious and unified sensing system.

Representation Learning

Toward Clinically Assisted Colorectal Polyp Recognition via Structured Cross-modal Representation Consistency

1 code implementation23 Jun 2022 Weijie Ma, Ye Zhu, Ruimao Zhang, Jie Yang, Yiwen Hu, Zhen Li, Li Xiang

By aligning the class tokens and spatial attention maps of paired NBI and WL images at different levels, the Transformer achieves the ability to keep both global and local representation consistency for the above two modalities.

Classification Image Classification

AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation

1 code implementation16 Jun 2022 Yuanfeng Ji, Haotian Bai, Jie Yang, Chongjian Ge, Ye Zhu, Ruimao Zhang, Zhen Li, Lingyan Zhang, Wanling Ma, Xiang Wan, Ping Luo

Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods.

Image Segmentation Medical Image Segmentation +3

Discrete Contrastive Diffusion for Cross-Modal Music and Image Generation

1 code implementation15 Jun 2022 Ye Zhu, Yu Wu, Kyle Olszewski, Jian Ren, Sergey Tulyakov, Yan Yan

Diffusion probabilistic models (DPMs) have become a popular approach to conditional generation, due to their promising results and support for cross-modal synthesis.

Contrastive Learning Denoising +2

Supplementing Missing Visions via Dialog for Scene Graph Generations

1 code implementation23 Apr 2022 Zhenghao Zhao, Ye Zhu, Xiaoguang Zhu, Yuzhang Shang, Yan Yan

Most current AI systems rely on the premise that the input visual data are sufficient to achieve competitive performance in various computer vision tasks.

Graph Generation Scene Graph Generation

Quantized GAN for Complex Music Generation from Dance Videos

1 code implementation1 Apr 2022 Ye Zhu, Kyle Olszewski, Yu Wu, Panos Achlioptas, Menglei Chai, Yan Yan, Sergey Tulyakov

We present Dance2Music-GAN (D2M-GAN), a novel adversarial multi-modal framework that generates complex musical samples conditioned on dance videos.

Music Generation

Skeleton Sequence and RGB Frame Based Multi-Modality Feature Fusion Network for Action Recognition

no code implementations23 Feb 2022 Xiaoguang Zhu, Ye Zhu, Haoyu Wang, Honglin Wen, Yan Yan, Peilin Liu

To solve the problem, we propose a multi-modality feature fusion network to combine the modalities of the skeleton sequence and RGB frame instead of the RGB video, as the key information contained by the combination of skeleton sequence and RGB frame is close to that of the skeleton sequence and RGB video.

Action Recognition

Saying the Unseen: Video Descriptions via Dialog Agents

1 code implementation26 Jun 2021 Ye Zhu, Yu Wu, Yi Yang, Yan Yan

Current vision and language tasks usually take complete visual data (e. g., raw images or videos) as input, however, practical scenarios may often consist the situations where part of the visual information becomes inaccessible due to various reasons e. g., restricted view with fixed camera or intentional vision block for security concerns.

Transfer Learning

Real Time Video based Heart and Respiration Rate Monitoring

no code implementations4 Jun 2021 Jafar Pourbemany, Almabrok Essa, Ye Zhu

The proposed method is based on measuring fluctuations in the Hue, and can therefore extract both HR and RR from the video of a user's face.

The Impact of Isolation Kernel on Agglomerative Hierarchical Clustering Algorithms

no code implementations12 Oct 2020 Xin Han, Ye Zhu, Kai Ming Ting, Gang Li

In this paper, we identify the root cause of this issue and show that the use of a data-dependent kernel (instead of distance or existing kernel) provides an effective means to address it.

Clustering

Hierarchical HMM for Eye Movement Classification

no code implementations18 Aug 2020 Ye Zhu, Yan Yan, Oleg Komogortsev

In this work, we tackle the problem of ternary eye movement classification, which aims to separate fixations, saccades and smooth pursuits from the raw eye positional data.

Classification General Classification

Describing Unseen Videos via Multi-Modal Cooperative Dialog Agents

1 code implementation ECCV 2020 Ye Zhu, Yu Wu, Yi Yang, Yan Yan

With the arising concerns for the AI systems provided with direct access to abundant sensitive information, researchers seek to develop more reliable AI with implicit information sources.

Video Description

Point-Set Kernel Clustering

1 code implementation14 Feb 2020 Kai Ming Ting, Jonathan R. Wells, Ye Zhu

This paper introduces a new similarity measure called point-set kernel which computes the similarity between an object and a set of objects.

Clustering Semantic Segmentation

Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel

1 code implementation24 Jun 2019 Ye Zhu, Kai Ming Ting

This paper presents a new insight into improving the performance of Stochastic Neighbour Embedding (t-SNE) by using Isolation kernel instead of Gaussian kernel.

Strain engineering of epitaxial oxide heterostructures beyond substrate limitations

no code implementations3 May 2019 Xiong Deng, Chao Chen, Deyang Chen, Xiangbin Cai, Xiaozhe Yin, Chao Xu, Fei Sun, Caiwen Li, Yan Li, Han Xu, Mao Ye, Guo Tian, Zhen Fan, Zhipeng Hou, Minghui Qin, Yu Chen, Zhenlin Luo, Xubing Lu, Guofu Zhou, Lang Chen, Ning Wang, Ye Zhu, Xingsen Gao, Jun-Ming Liu

The limitation of commercially available single-crystal substrates and the lack of continuous strain tunability preclude the ability to take full advantage of strain engineering for further exploring novel properties and exhaustively studying fundamental physics in complex oxides.

Materials Science

A Technical Survey on Statistical Modelling and Design Methods for Crowdsourcing Quality Control

no code implementations5 Dec 2018 Yuan Jin, Mark Carman, Ye Zhu, Yong Xiang

Our survey is the first to bridge the two branches by providing technical details on how they work together under frameworks that systematically unify crowdsourcing aspects modelled by both of them to determine the response quality.

BIG-bench Machine Learning

Multiview Based 3D Scene Understanding On Partial Point Sets

no code implementations30 Nov 2018 Ye Zhu, Sven Ewan Shepstone, Pablo Martínez-Nuevo, Miklas Strøm Kristoffersen, Fabien Moutarde, Zhuang Fu

Deep learning within the context of point clouds has gained much research interest in recent years mostly due to the promising results that have been achieved on a number of challenging benchmarks, such as 3D shape recognition and scene semantic segmentation.

3D Part Segmentation 3D Shape Recognition +2

Hierarchical clustering that takes advantage of both density-peak and density-connectivity

1 code implementation8 Oct 2018 Ye Zhu, Kai Ming Ting, Yuan Jin, Maia Angelova

This paper focuses on density-based clustering, particularly the Density Peak (DP) algorithm and the one based on density-connectivity DBSCAN; and proposes a new method which takes advantage of the individual strengths of these two methods to yield a density-based hierarchical clustering algorithm.

Clustering

CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities

1 code implementation5 Oct 2018 Ye Zhu, Kai Ming Ting, Mark Carman, Maia Angelova

To match the implicit assumption, we propose to transform a given dataset such that the transformed clusters have approximately the same density while all regions of locally low density become globally low density -- homogenising cluster density while preserving the cluster structure of the dataset.

Anomaly Detection Clustering +1

Distinguishing Question Subjectivity from Difficulty for Improved Crowdsourcing

no code implementations12 Feb 2018 Yuan Jin, Mark Carman, Ye Zhu, Wray Buntine

Experiments show that our model(1) improves the performance of both quality control for crowd-sourced answers and next answer prediction for crowd-workers, and (2) can potentially provide coherent rankings of questions in terms of their difficulty and subjectivity, so that task providers can refine their designs of the crowdsourcing tasks, e. g. by removing highly subjective questions or inappropriately difficult questions.

Clustering

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