Search Results for author: Yue Gao

Found 35 papers, 11 papers with code

Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model

1 code implementation28 Mar 2022 Yu Du, Fangyun Wei, Zihe Zhang, Miaojing Shi, Yue Gao, Guoqi Li

In this paper, we introduce a novel method, detection prompt (DetPro), to learn continuous prompt representations for open-vocabulary object detection based on the pre-trained vision-language model.

Image Classification Language Modelling +3

Towards Language-guided Visual Recognition via Dynamic Convolutions

no code implementations17 Oct 2021 Gen Luo, Yiyi Zhou, Xiaoshuai Sun, Xinghao Ding, Yongjian Wu, Feiyue Huang, Yue Gao, Rongrong Ji

Based on the LaConv module, we further build the first fully language-driven convolution network, termed as LaConvNet, which can unify the visual recognition and multi-modal reasoning in one forward structure.

Question Answering Referring Expression +3

Rethinking Supervised Pre-training for Better Downstream Transferring

no code implementations ICLR 2022 Yutong Feng, Jianwen Jiang, Mingqian Tang, Rong Jin, Yue Gao

Though for most cases, the pre-training stage is conducted based on supervised methods, recent works on self-supervised pre-training have shown powerful transferability and even outperform supervised pre-training on multiple downstream tasks.

How Frequency Effect Graph Neural Networks

no code implementations29 Sep 2021 Xueqi Ma, Yubo Zhang, Weifeng Liu, Yue Gao

Based on the frequency principle on GNNs, we present a novel powerful GNNs framework, Multi-Scale Frequency Enhanced Graph Neural Networks (MSF-GNNs) which considers multi-scale representations from wavelet decomposition.

Node Classification

FEATURE-AUGMENTED HYPERGRAPH NEURAL NETWORKS

no code implementations29 Sep 2021 Xueqi Ma, Pan Li, Qiong Cao, James Bailey, Yue Gao

In FAHGNN, we explore the influence of node features for the expressive power of GNNs and augment features by introducing common features and personal features to model information.

Node Classification Representation Learning

Robust Risk-Sensitive Reinforcement Learning Agents for Trading Markets

no code implementations16 Jul 2021 Yue Gao, Kry Yik Chau Lui, Pablo Hernandez-Leal

Trading markets represent a real-world financial application to deploy reinforcement learning agents, however, they carry hard fundamental challenges such as high variance and costly exploration.

reinforcement-learning

Improved Prediction and Network Estimation Using the Monotone Single Index Multi-variate Autoregressive Model

no code implementations28 Jun 2021 Yue Gao, Garvesh Raskutti

Network estimation from multi-variate point process or time series data is a problem of fundamental importance.

Time Series

Graph-MLP: Node Classification without Message Passing in Graph

1 code implementation8 Jun 2021 Yang Hu, Haoxuan You, Zhecan Wang, Zhicheng Wang, Erjin Zhou, Yue Gao

Graph Neural Network (GNN) has been demonstrated its effectiveness in dealing with non-Euclidean structural data.

Classification Node Classification

Aligning Pretraining for Detection via Object-Level Contrastive Learning

1 code implementation NeurIPS 2021 Fangyun Wei, Yue Gao, Zhirong Wu, Han Hu, Stephen Lin

Image-level contrastive representation learning has proven to be highly effective as a generic model for transfer learning.

Contrastive Learning Object Detection +3

Leveraging Non-uniformity in First-order Non-convex Optimization

no code implementations13 May 2021 Jincheng Mei, Yue Gao, Bo Dai, Csaba Szepesvari, Dale Schuurmans

Classical global convergence results for first-order methods rely on uniform smoothness and the \L{}ojasiewicz inequality.

Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems

no code implementations18 Apr 2021 Yue Gao, Ilia Shumailov, Kassem Fawaz

As real-world images come in varying sizes, the machine learning model is part of a larger system that includes an upstream image scaling algorithm.

High-Fidelity and Arbitrary Face Editing

no code implementations CVPR 2021 Yue Gao, Fangyun Wei, Jianmin Bao, Shuyang Gu, Dong Chen, Fang Wen, Zhouhui Lian

However, we observe that the generator tends to find a tricky way to hide information from the original image to satisfy the constraint of cycle consistency, making it impossible to maintain the rich details (e. g., wrinkles and moles) of non-editing areas.

Incremental Learning on Growing Graphs

no code implementations1 Jan 2021 Yutong Feng, Jianwen Jiang, Yue Gao

To tackle this problem, we introduce incremental graph learning (IGL), a general framework to formulate the learning on growing graphs in an incremental manner, where traditional graph learning method could be deployed as a basic model.

Graph Learning Incremental Learning +2

Event Stream Super-Resolution via Spatiotemporal Constraint Learning

no code implementations ICCV 2021 Siqi Li, Yutong Feng, Yipeng Li, Yu Jiang, Changqing Zou, Yue Gao

Therefore, it is imperative to explore the algorithm of event stream super-resolution, which is a non-trivial task due to the sparsity and strong spatio-temporal correlation of the events from an event camera.

Image Reconstruction Super-Resolution

Improving Image Captioning by Leveraging Intra- and Inter-layer Global Representation in Transformer Network

no code implementations13 Dec 2020 Jiayi Ji, Yunpeng Luo, Xiaoshuai Sun, Fuhai Chen, Gen Luo, Yongjian Wu, Yue Gao, Rongrong Ji

The latter contains a Global Adaptive Controller that can adaptively fuse the global information into the decoder to guide the caption generation.

Image Captioning

3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection

2 code implementations CVPR 2021 He Wang, Yezhen Cong, Or Litany, Yue Gao, Leonidas J. Guibas

On KITTI, we are the first to demonstrate semi-supervised 3D object detection and our method surpasses a fully supervised baseline from 1. 8% to 7. 6% under different label ratios and categories.

3D Object Detection Autonomous Driving

Self-Supervised Time Series Representation Learning by Inter-Intra Relational Reasoning

1 code implementation27 Nov 2020 Haoyi Fan, Fengbin Zhang, Yue Gao

In this paper, we present SelfTime: a general self-supervised time series representation learning framework, by exploring the inter-sample relation and intra-temporal relation of time series to learn the underlying structure feature on the unlabeled time series.

Relational Reasoning Representation Learning +3

Attribute2Font: Creating Fonts You Want From Attributes

2 code implementations16 May 2020 Yizhi Wang, Yue Gao, Zhouhui Lian

To the best of our knowledge, our model is the first one in the literature which is capable of generating glyph images in new font styles, instead of retrieving existing fonts, according to given values of specified font attributes.

Font Style Transfer

Hypergraph Learning for Identification of COVID-19 with CT Imaging

no code implementations7 May 2020 Donglin Di, Feng Shi, Fuhua Yan, Liming Xia, Zhanhao Mo, Zhongxiang Ding, Fei Shan, Shengrui Li, Ying WEI, Ying Shao, Miaofei Han, Yaozong Gao, He Sui, Yue Gao, Dinggang Shen

The main challenge in early screening is how to model the confusing cases in the COVID-19 and CAP groups, with very similar clinical manifestations and imaging features.

Multi-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computing

no code implementations5 Apr 2020 Xiaolan Liu, Jiadong Yu, Yue Gao

To support popular Internet of Things (IoT) applications such as virtual reality, mobile games and wearable devices, edge computing provides a front-end distributed computing archetype of centralized cloud computing with low latency.

Distributed Computing Edge-computing +3

Attention-based Multi-modal Fusion Network for Semantic Scene Completion

no code implementations31 Mar 2020 Siqi Li, Changqing Zou, Yipeng Li, Xibin Zhao, Yue Gao

This paper presents an end-to-end 3D convolutional network named attention-based multi-modal fusion network (AMFNet) for the semantic scene completion (SSC) task of inferring the occupancy and semantic labels of a volumetric 3D scene from single-view RGB-D images.

2D Semantic Segmentation Semantic Segmentation

Analyzing Accuracy Loss in Randomized Smoothing Defenses

no code implementations3 Mar 2020 Yue Gao, Harrison Rosenberg, Kassem Fawaz, Somesh Jha, Justin Hsu

In test-time attacks an adversary crafts adversarial examples, which are specially crafted perturbations imperceptible to humans which, when added to an input example, force a machine learning model to misclassify the given input example.

Autonomous Driving Speech Recognition

Deep Multi-View Enhancement Hashing for Image Retrieval

no code implementations1 Feb 2020 Chenggang Yan, Biao Gong, Yuxuan Wei, Yue Gao

Therefore, we try to introduce the multi-view deep neural network into the hash learning field, and design an efficient and innovative retrieval model, which has achieved a significant improvement in retrieval performance.

Image Retrieval

Artistic Glyph Image Synthesis via One-Stage Few-Shot Learning

3 code implementations11 Oct 2019 Yue Gao, Yuan Guo, Zhouhui Lian, Yingmin Tang, Jianguo Xiao

Extensive experiments on both English and Chinese artistic glyph image datasets demonstrate the superiority of our model in generating high-quality stylized glyph images against other state-of-the-art methods.

Few-Shot Learning Image Generation

DHGNN: Dynamic Hypergraph Neural Networks

1 code implementation1 Jul 2019 Jianwen Jiang, Yuxuan Wei, Yifan Feng, Jingxuan Cao, Yue Gao

Then hypergraph convolution is introduced to encode high-order data relations in a hypergraph structure.

PVRNet: Point-View Relation Neural Network for 3D Shape Recognition

no code implementations2 Dec 2018 Haoxuan You, Yifan Feng, Xibin Zhao, Changqing Zou, Rongrong Ji, Yue Gao

More specifically, based on the relation score module, the point-single-view fusion feature is first extracted by fusing the point cloud feature and each single view feature with point-singe-view relation, then the point-multi-view fusion feature is extracted by fusing the point cloud feature and the features of different number of views with point-multi-view relation.

3D Shape Classification 3D Shape Recognition

MeshNet: Mesh Neural Network for 3D Shape Representation

2 code implementations28 Nov 2018 Yutong Feng, Yifan Feng, Haoxuan You, Xibin Zhao, Yue Gao

However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data.

3D Shape Classification 3D Shape Representation +1

Hypergraph Neural Networks

1 code implementation25 Sep 2018 Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, Yue Gao

In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure.

Object Recognition Representation Learning

PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition

no code implementations23 Aug 2018 Haoxuan You, Yifan Feng, Rongrong Ji, Yue Gao

With the recent proliferation of deep learning, various deep models with different representations have achieved the state-of-the-art performance.

3D Object Recognition 3D Shape Classification +2

GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition

no code implementations CVPR 2018 Yifan Feng, Zizhao Zhang, Xibin Zhao, Rongrong Ji, Yue Gao

The proposed GVCNN framework is composed of a hierarchical view-group-shape architecture, i. e., from the view level, the group level and the shape level, which are organized using a grouping strategy.

3D Shape Classification 3D Shape Recognition +1

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