Search Results for author: Qinmu Peng

Found 23 papers, 8 papers with code

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

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

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

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

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: 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

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

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

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

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

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