Search Results for author: Cheng Jin

Found 26 papers, 10 papers with code

EPA: Neural Collapse Inspired Robust Out-of-Distribution Detector

no code implementations3 Jan 2024 Jiawei Zhang, Yufan Chen, Cheng Jin, Lei Zhu, Yuantao Gu

Out-of-distribution (OOD) detection plays a crucial role in ensuring the security of neural networks.

Out of Distribution (OOD) Detection

Point Cloud Part Editing: Segmentation, Generation, Assembly, and Selection

1 code implementation19 Dec 2023 Kaiyi Zhang, Yang Chen, Ximing Yang, Weizhong Zhang, Cheng Jin

Based on this process, we introduce SGAS, a model for part editing that employs two strategies: feature disentanglement and constraint.

Disentanglement Point Cloud Generation

Shapley Values-enabled Progressive Pseudo Bag Augmentation for Whole Slide Image Classification

no code implementations9 Dec 2023 Renao Yan, Qiehe Sun, Cheng Jin, Yiqing Liu, Yonghong He, Tian Guan, Hao Chen

While most of the conventional MIL methods use attention scores to estimate instance importance scores (IIS) which contribute to the prediction of the slide labels, these often lead to skewed attention distributions and inaccuracies in identifying crucial instances.

Image Classification Multiple Instance Learning

UMAAF: Unveiling Aesthetics via Multifarious Attributes of Images

no code implementations19 Nov 2023 Weijie Li, Yitian Wan, Xingjiao Wu, Junjie Xu, Cheng Jin, Liang He

Then, to better utilize image attributes in aesthetic assessment, we propose the Unified Multi-attribute Aesthetic Assessment Framework (UMAAF) to model both absolute and relative attributes of images.

Attribute

High-fidelity Person-centric Subject-to-Image Synthesis

1 code implementation17 Nov 2023 Yibin Wang, Weizhong Zhang, Jianwei Zheng, Cheng Jin

Specifically, we first develop two specialized pre-trained diffusion models, i. e., Text-driven Diffusion Model (TDM) and Subject-augmented Diffusion Model (SDM), for scene and person generation, respectively.

Image Generation Scene Generation

DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding

1 code implementation29 Oct 2023 Anran Wu, Luwei Xiao, Xingjiao Wu, Shuwen Yang, Junjie Xu, Zisong Zhuang, Nian Xie, Cheng Jin, Liang He

Our DCQA dataset is expected to foster research on understanding visualizations in documents, especially for scenarios that require complex reasoning for charts in the visually-rich document.

Answer Generation Chart Question Answering +5

Progressive Evidence Refinement for Open-domain Multimodal Retrieval Question Answering

no code implementations15 Oct 2023 Shuwen Yang, Anran Wu, Xingjiao Wu, Luwei Xiao, Tianlong Ma, Cheng Jin, Liang He

Firstly, utilizing compressed evidence features as input to the model results in the loss of fine-grained information within the evidence.

Contrastive Learning Logical Sequence +2

ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction

no code implementations22 Sep 2023 Zhilei Hu, Zixuan Li, Daozhu Xu, Long Bai, Cheng Jin, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng

To comprehensively understand their intrinsic semantics, in this paper, we obtain prototype representations for each type of event relation and propose a Prototype-Enhanced Matching (ProtoEM) framework for the joint extraction of multiple kinds of event relations.

Event Relation Extraction Relation +1

Linear Speedup of Incremental Aggregated Gradient Methods on Streaming Data

no code implementations10 Sep 2023 Xiaolu Wang, Cheng Jin, Hoi-To Wai, Yuantao Gu

This paper considers a type of incremental aggregated gradient (IAG) method for large-scale distributed optimization.

Distributed Optimization

WYTIWYR: A User Intent-Aware Framework with Multi-modal Inputs for Visualization Retrieval

1 code implementation14 Apr 2023 Shishi Xiao, Yihan Hou, Cheng Jin, Wei Zeng

Retrieving charts from a large corpus is a fundamental task that can benefit numerous applications such as visualization recommendations. The retrieved results are expected to conform to both explicit visual attributes (e. g., chart type, colormap) and implicit user intents (e. g., design style, context information) that vary upon application scenarios.

Retrieval Zero-Shot Learning

DDT: Dual-branch Deformable Transformer for Image Denoising

1 code implementation13 Apr 2023 Kangliang Liu, Xiangcheng Du, Sijie Liu, Yingbin Zheng, Xingjiao Wu, Cheng Jin

Transformer is beneficial for image denoising tasks since it can model long-range dependencies to overcome the limitations presented by inductive convolutional biases.

Image Denoising

Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions

no code implementations22 Mar 2023 Cheng Jin, Zhengrui Guo, Yi Lin, Luyang Luo, Hao Chen

Thus, label-efficient deep learning methods are developed to make comprehensive use of the labeled data as well as the abundance of unlabeled and weak-labeled data.

Aggregated Text Transformer for Scene Text Detection

no code implementations25 Nov 2022 Zhao Zhou, Xiangcheng Du, Yingbin Zheng, Cheng Jin

We present the Aggregated Text TRansformer(ATTR), which is designed to represent texts in scene images with a multi-scale self-attention mechanism.

Scene Text Detection Text Detection

Progressive Scene Text Erasing with Self-Supervision

no code implementations23 Jul 2022 Xiangcheng Du, Zhao Zhou, Yingbin Zheng, Xingjiao Wu, Tianlong Ma, Cheng Jin

Scene text erasing seeks to erase text contents from scene images and current state-of-the-art text erasing models are trained on large-scale synthetic data.

SIT: A Bionic and Non-Linear Neuron for Spiking Neural Network

no code implementations30 Mar 2022 Cheng Jin, Rui-Jie Zhu, Xiao Wu, Liang-Jian Deng

Spiking Neural Networks (SNNs) have piqued researchers' interest because of their capacity to process temporal information and low power consumption.

Image Classification

SRPCN: Structure Retrieval based Point Completion Network

no code implementations6 Feb 2022 Kaiyi Zhang, Ximing Yang, Yuan Wu, Cheng Jin

Besides, the missing patterns are diverse in reality, but existing methods can only handle fixed ones, which means a poor generalization ability.

Point Cloud Completion Retrieval

Generate Point Clouds with Multiscale Details from Graph-Represented Structures

no code implementations13 Dec 2021 Ximing Yang, Zhengfu He, Cheng Jin

As details are missing in most representations of structures, the lack of controllability to details is one of the major weaknesses in structure-based controllable point cloud generation.

Miscellaneous Point Cloud Generation

Attention-based Transformation from Latent Features to Point Clouds

1 code implementation10 Dec 2021 Kaiyi Zhang, Ximing Yang, Yuan Wu, Cheng Jin

The points generated by AXform do not have the strong 2-manifold constraint, which improves the generation of non-smooth surfaces.

Point Cloud Completion Unsupervised Semantic Segmentation

Safe Distillation Box

1 code implementation5 Dec 2021 Jingwen Ye, Yining Mao, Jie Song, Xinchao Wang, Cheng Jin, Mingli Song

In other words, all users may employ a model in SDB for inference, but only authorized users get access to KD from the model.

Knowledge Distillation

Document Layout Analysis with Aesthetic-Guided Image Augmentation

no code implementations27 Nov 2021 Tianlong Ma, Xingjiao Wu, Xin Li, Xiangcheng Du, Zhao Zhou, Liang Xue, Cheng Jin

To measure the proposed image layer modeling method, we propose a manually-labeled non-Manhattan layout fine-grained segmentation dataset named FPD.

Document Layout Analysis document understanding +2

Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging

1 code implementation4 Dec 2020 Zachary J. Lee, George Lee, Ted Lee, Cheng Jin, Rand Lee, Zhi Low, Daniel Chang, Christine Ortega, Steven H. Low

We describe the architecture and algorithms of the Adaptive Charging Network (ACN), which was first deployed on the Caltech campus in early 2016 and is currently operating at over 100 other sites in the United States.

Model Predictive Control Scheduling

One-sample Guided Object Representation Disassembling

no code implementations NeurIPS 2020 Zunlei Feng, Yongming He, Xinchao Wang, Xin Gao, Jie Lei, Cheng Jin, Mingli Song

In this paper, we introduce the One-sample Guided Object Representation Disassembling (One-GORD) method, which only requires one annotated sample for each object category to learn disassembled object representation from unannotated images.

Data Augmentation Image Classification +1

GASNet: Weakly-supervised Framework for COVID-19 Lesion Segmentation

no code implementations19 Oct 2020 Zhanwei Xu, Yukun Cao, Cheng Jin, Guozhu Shao, Xiaoqing Liu, Jie zhou, Heshui Shi, Jianjiang Feng

Segmentation of infected areas in chest CT volumes is of great significance for further diagnosis and treatment of COVID-19 patients.

Image Segmentation Lesion Segmentation +2

TripletGAN: Training Generative Model with Triplet Loss

no code implementations14 Nov 2017 Gongze Cao, Yezhou Yang, Jie Lei, Cheng Jin, Yang Liu, Mingli Song

As an effective way of metric learning, triplet loss has been widely used in many deep learning tasks, including face recognition and person-ReID, leading to many states of the arts.

Face Recognition General Classification +1

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