Search Results for author: Xinge You

Found 39 papers, 14 papers with code

SocialCircle: Learning the Angle-based Social Interaction Representation for Pedestrian Trajectory Prediction

1 code implementation9 Oct 2023 Conghao Wong, Beihao Xia, Xinge You

Analyzing and forecasting trajectories of agents like pedestrians and cars in complex scenes has become more and more significant in many intelligent systems and applications.

Pedestrian Trajectory Prediction Trajectory Prediction

Detail Reinforcement Diffusion Model: Augmentation Fine-Grained Visual Categorization in Few-Shot Conditions

no code implementations15 Sep 2023 Tianxu Wu, Shuo Ye, Shuhuang Chen, Qinmu Peng, Xinge You

To address this issue, we propose a novel approach termed the detail reinforcement diffusion model~(DRDM), which leverages the rich knowledge of large models for fine-grained data augmentation and comprises two key components including discriminative semantic recombination (DSR) and spatial knowledge reference~(SKR).

Data Augmentation Fine-Grained Visual Categorization +1

ECEA: Extensible Co-Existing Attention for Few-Shot Object Detection

no code implementations15 Sep 2023 Zhimeng Xin, Tianxu Wu, Shiming Chen, Yixiong Zou, Ling Shao, Xinge You

Extensive experiments on the PASCAL VOC and COCO datasets show that our ECEA module can assist the few-shot detector to completely predict the object despite some regions failing to appear in the training samples and achieve the new state of the art compared with existing FSOD methods.

Few-Shot Object Detection Object +1

Towards Unsupervised Graph Completion Learning on Graphs with Features and Structure Missing

no code implementations6 Sep 2023 Sichao Fu, Qinmu Peng, Yang He, Baokun Du, Xinge You

In recent years, graph neural networks (GNN) have achieved significant developments in a variety of graph analytical tasks.

Node Classification Self-Supervised Learning

EGANS: Evolutionary Generative Adversarial Network Search for Zero-Shot Learning

no code implementations19 Aug 2023 Shiming Chen, Shihuang Chen, Wenjin Hou, Weiping Ding, Xinge You

However, existing GAN-based generative ZSL methods are based on hand-crafted models, which cannot adapt to various datasets/scenarios and fails to model instability.

Generative Adversarial Network Neural Architecture Search +1

Evolving Semantic Prototype Improves Generative Zero-Shot Learning

no code implementations12 Jun 2023 Shiming Chen, Wenjin Hou, Ziming Hong, Xiaohan Ding, Yibing Song, Xinge You, Tongliang Liu, Kun Zhang

After alignment, synthesized sample features from unseen classes are closer to the real sample features and benefit DSP to improve existing generative ZSL methods by 8. 5\%, 8. 0\%, and 9. 7\% on the standard CUB, SUN AWA2 datasets, the significant performance improvement indicates that evolving semantic prototype explores a virgin field in ZSL.

Zero-Shot Learning

CDLT: A Dataset with Concept Drift and Long-Tailed Distribution for Fine-Grained Visual Categorization

no code implementations4 Jun 2023 Shuo Ye, Yufeng Shi, Ruxin Wang, Yu Wang, Jiamiao Xu, Chuanwu Yang, Xinge You

Data is the foundation for the development of computer vision, and the establishment of datasets plays an important role in advancing the techniques of fine-grained visual categorization~(FGVC).

Fine-Grained Visual Categorization

Another Vertical View: A Hierarchical Network for Heterogeneous Trajectory Prediction via Spectrums

1 code implementation11 Apr 2023 Conghao Wong, Beihao Xia, Qinmu Peng, Xinge You

In this paper, we bring a new ``view'' for trajectory prediction to model and forecast trajectories hierarchically according to different frequency portions from the spectral domain to learn to forecast trajectories by considering their frequency responses.

Trajectory Prediction

Self-supervised Guided Hypergraph Feature Propagation for Semi-supervised Classification with Missing Node Features

no code implementations16 Feb 2023 Chengxiang Lei, Sichao Fu, Yuetian Wang, Wenhao Qiu, Yachen Hu, Qinmu Peng, Xinge You

Some recent methods have been proposed to reconstruct the missing node features by the information propagation among nodes with known and unknown attributes.

Pseudo Label

Deep Manifold Hashing: A Divide-and-Conquer Approach for Semi-Paired Unsupervised Cross-Modal Retrieval

no code implementations26 Sep 2022 Yufeng Shi, Xinge You, Jiamiao Xu, Feng Zheng, Qinmu Peng, Weihua Ou

Hashing that projects data into binary codes has shown extraordinary talents in cross-modal retrieval due to its low storage usage and high query speed.

Cross-Modal Retrieval Retrieval

Information-Theoretic Hashing for Zero-Shot Cross-Modal Retrieval

no code implementations26 Sep 2022 Yufeng Shi, Shujian Yu, Duanquan Xu, Xinge You

In this paper, instead of using an extra NLP model to define a common space beforehand, we consider a totally different way to construct (or learn) a common hamming space from an information-theoretic perspective.

Cross-Modal Retrieval Retrieval +1

Leachable Component Clustering

no code implementations28 Aug 2022 Miao Cheng, Xinge You

Clustering attempts to partition data instances into several distinctive groups, while the similarities among data belonging to the common partition can be principally reserved.

Clustering Imputation

Deep Supervised Information Bottleneck Hashing for Cross-modal Retrieval based Computer-aided Diagnosis

no code implementations6 May 2022 Yufeng Shi, Shuhuang Chen, Xinge You, Qinmu Peng, Weihua Ou, Yue Zhao

Mapping X-ray images, radiology reports, and other medical data as binary codes in the common space, which can assist clinicians to retrieve pathology-related data from heterogeneous modalities (i. e., hashing-based cross-modal medical data retrieval), provides a new view to promot computeraided diagnosis.

Cross-Modal Retrieval Retrieval

R2-Trans:Fine-Grained Visual Categorization with Redundancy Reduction

no code implementations21 Apr 2022 Yu Wang, Shuo Ye, Shujian Yu, Xinge You

In this paper, we present a novel approach for FGVC, which can simultaneously make use of partial yet sufficient discriminative information in environmental cues and also compress the redundant information in class-token with respect to the target.

Fine-Grained Visual Categorization

MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning

2 code implementations CVPR 2022 Shiming Chen, Ziming Hong, Guo-Sen Xie, Wenhan Yang, Qinmu Peng, Kai Wang, Jian Zhao, Xinge You

Prior works either simply align the global features of an image with its associated class semantic vector or utilize unidirectional attention to learn the limited latent semantic representations, which could not effectively discover the intrinsic semantic knowledge e. g., attribute semantics) between visual and attribute features.

Attribute Transfer Learning +1

CSCNet: Contextual Semantic Consistency Network for Trajectory Prediction in Crowded Spaces

no code implementations17 Feb 2022 Beihao Xia, Conghao Wong, Qinmu Peng, Wei Yuan, Xinge You

The current methods are dedicated to studying the agents' future trajectories under the social interaction and the sceneries' physical constraints.

Autonomous Driving Trajectory Prediction

TransZero++: Cross Attribute-Guided Transformer for Zero-Shot Learning

1 code implementation16 Dec 2021 Shiming Chen, Ziming Hong, Wenjin Hou, Guo-Sen Xie, Yibing Song, Jian Zhao, Xinge You, Shuicheng Yan, Ling Shao

Analogously, VAT uses the similar feature augmentation encoder to refine the visual features, which are further applied in visual$\rightarrow$attribute decoder to learn visual-based attribute features.

Attribute Zero-Shot Learning

TransZero: Attribute-guided Transformer for Zero-Shot Learning

1 code implementation3 Dec 2021 Shiming Chen, Ziming Hong, Yang Liu, Guo-Sen Xie, Baigui Sun, Hao Li, Qinmu Peng, Ke Lu, Xinge You

Although some attention-based models have attempted to learn such region features in a single image, the transferability and discriminative attribute localization of visual features are typically neglected.

Attribute Zero-Shot Learning

HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning

2 code implementations NeurIPS 2021 Shiming Chen, Guo-Sen Xie, Yang Liu, Qinmu Peng, Baigui Sun, Hao Li, Xinge You, Ling Shao

Specifically, HSVA aligns the semantic and visual domains by adopting a hierarchical two-step adaptation, i. e., structure adaptation and distribution adaptation.

Transfer Learning Zero-Shot Learning

FREE: Feature Refinement for Generalized Zero-Shot Learning

1 code implementation ICCV 2021 Shiming Chen, Wenjie Wang, Beihao Xia, Qinmu Peng, Xinge You, Feng Zheng, Ling Shao

FREE employs a feature refinement (FR) module that incorporates \textit{semantic$\rightarrow$visual} mapping into a unified generative model to refine the visual features of seen and unseen class samples.

Generalized Zero-Shot Learning

MSN: Multi-Style Network for Trajectory Prediction

1 code implementation2 Jul 2021 Conghao Wong, Beihao Xia, Qinmu Peng, Wei Yuan, Xinge You

Then, we assume that the target agents may plan their future behaviors according to each of these categorized styles, thus utilizing different style channels to make predictions with significant style differences in parallel.

Robot Navigation Self-Driving Cars +1

Adaptive Matching of Kernel Means

no code implementations16 Nov 2020 Miao Cheng, Xinge You

As a promising step, the performance of data analysis and feature learning are able to be improved if certain pattern matching mechanism is available.

Novelty Detection

BGM: Building a Dynamic Guidance Map without Visual Images for Trajectory Prediction

no code implementations8 Oct 2020 Beihao Xia, Conghao Wong, Heng Li, Shiming Chen, Qinmu Peng, Xinge You

Visual images usually contain the informative context of the environment, thereby helping to predict agents' behaviors.

Trajectory Prediction

CDE-GAN: Cooperative Dual Evolution Based Generative Adversarial Network

1 code implementation21 Aug 2020 Shiming Chen, Wenjie Wang, Beihao Xia, Xinge You, Zehong Cao, Weiping Ding

In essence, CDE-GAN incorporates dual evolution with respect to the generator(s) and discriminators into a unified evolutionary adversarial framework to conduct effective adversarial multi-objective optimization.

GAN image forensics Generative Adversarial Network +1

Modal Regression based Structured Low-rank Matrix Recovery for Multi-view Learning

no code implementations22 Mar 2020 Jiamiao Xu, Fangzhao Wang, Qinmu Peng, Xinge You, Shuo Wang, Xiao-Yuan Jing, C. L. Philip Chen

Furthermore, recent low-rank modeling provides a satisfactory solution to address data contaminated by predefined assumptions of noise distribution, such as Gaussian or Laplacian distribution.


A Spatial-Temporal Attentive Network with Spatial Continuity for Trajectory Prediction

no code implementations13 Mar 2020 Beihao Xia, Conghao Wang, Qinmu Peng, Xinge You, DaCheng Tao

It remains challenging to automatically predict the multi-agent trajectory due to multiple interactions including agent to agent interaction and scene to agent interaction.

Trajectory Prediction

Kernelized Similarity Learning and Embedding for Dynamic Texture Synthesis

1 code implementation11 Nov 2019 Shiming Chen, Peng Zhang, Guo-Sen Xie, Qinmu Peng, Zehong Cao, Wei Yuan, Xinge You

Dynamic texture (DT) exhibits statistical stationarity in the spatial domain and stochastic repetitiveness in the temporal dimension, indicating that different frames of DT possess a high similarity correlation that is critical prior knowledge.

Texture Synthesis

Closed-Loop Adaptation for Weakly-Supervised Semantic Segmentation

no code implementations29 May 2019 Zhengqiang Zhang, Shujian Yu, Shi Yin, Qinmu Peng, Xinge You

Weakly-supervised semantic segmentation aims to assign each pixel a semantic category under weak supervisions, such as image-level tags.

Segmentation Superpixels +2

Fast and accurate reconstruction of HARDI using a 1D encoder-decoder convolutional network

no code implementations21 Mar 2019 Shi Yin, Zhengqiang Zhang, Qinmu Peng, Xinge You

High angular resolution diffusion imaging (HARDI) demands a lager amount of data measurements compared to diffusion tensor imaging, restricting its use in practice.

Fully-automatic segmentation of kidneys in clinical ultrasound images using a boundary distance regression network

no code implementations5 Jan 2019 Shi Yin, Zhengqiang Zhang, Hongming Li, Qinmu Peng, Xinge You, Susan L. Furth, Gregory E. Tasian, Yong Fan

It remains challenging to automatically segment kidneys in clinical ultrasound images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance.

Classification Distance regression +2

Robust Visual Tracking using Multi-Frame Multi-Feature Joint Modeling

1 code implementation19 Nov 2018 Peng Zhang, Shujian Yu, Jiamiao Xu, Xinge You, Xiubao Jiang, Xiao-Yuan Jing, DaCheng Tao

It remains a huge challenge to design effective and efficient trackers under complex scenarios, including occlusions, illumination changes and pose variations.

Multi-Task Learning MULTI-VIEW LEARNING +3

Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks

no code implementations12 Nov 2018 Shi Yin, Qinmu Peng, Hongming Li, Zhengqiang Zhang, Xinge You, Susan L. Furth, Gregory E. Tasian, Yong Fan

It remains challenging to automatically segment kidneys in clinical ultrasound (US) images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance.

Classification Data Augmentation +3

Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition

2 code implementations ECCV 2018 Chaojian Yu, Xinyi Zhao, Qi Zheng, Peng Zhang, Xinge You

Fine-grained visual recognition is challenging because it highly relies on the modeling of various semantic parts and fine-grained feature learning.

Fine-Grained Visual Recognition

Coarse-to-Fine Salient Object Detection with Low-Rank Matrix Recovery

no code implementations21 May 2018 Qi Zheng, Shujian Yu, Xinge You, Qinmu Peng

Low-Rank Matrix Recovery (LRMR) has recently been applied to saliency detection by decomposing image features into a low-rank component associated with background and a sparse component associated with visual salient regions.

object-detection RGB Salient Object Detection +2

Multi-view Common Component Discriminant Analysis for Cross-view Classification

no code implementations14 May 2018 Xinge You, Jiamiao Xu, Wei Yuan, Xiao-Yuan Jing, DaCheng Tao, Taiping Zhang

Cross-view classification that means to classify samples from heterogeneous views is a significant yet challenging problem in computer vision.

General Classification

Multi-view Hybrid Embedding: A Divide-and-Conquer Approach

no code implementations19 Apr 2018 Jiamiao Xu, Shujian Yu, Xinge You, Mengjun Leng, Xiao-Yuan Jing, C. L. Philip Chen

We present a novel cross-view classification algorithm where the gallery and probe data come from different views.

Classification General Classification

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