Search Results for author: Geng Chen

Found 27 papers, 15 papers with code

Learning Planning Abstractions from Language

no code implementations6 May 2024 Weiyu Liu, Geng Chen, Joy Hsu, Jiayuan Mao, Jiajun Wu

This paper presents a framework for learning state and action abstractions in sequential decision-making domains.

Decision Making

Metadata-Driven Federated Learning of Connectional Brain Templates in Non-IID Multi-Domain Scenarios

no code implementations14 Mar 2024 Geng Chen, Qingyue Wang, Islem Rekik

However, existing methods overlook the non-independent and identically distributed (non-IDD) issue stemming from multidomain brain connectivity heterogeneity, in which data domains are drawn from different hospitals and imaging modalities.

Federated Learning Privacy Preserving

Edge-aware Feature Aggregation Network for Polyp Segmentation

no code implementations19 Sep 2023 Tao Zhou, Yizhe Zhang, Geng Chen, Yi Zhou, Ye Wu, Deng-Ping Fan

Besides, a Scale-aware Convolution Module (SCM) is proposed to learn scale-aware features by using dilated convolutions with different ratios, in order to effectively deal with scale variation.

Decoder Segmentation

Tissue Segmentation of Thick-Slice Fetal Brain MR Scans with Guidance from High-Quality Isotropic Volumes

no code implementations13 Aug 2023 Shijie Huang, Xukun Zhang, Zhiming Cui, He Zhang, Geng Chen, Dinggang Shen

Accurate tissue segmentation of thick-slice fetal brain magnetic resonance (MR) scans is crucial for both reconstruction of isotropic brain MR volumes and the quantification of fetal brain development.

Domain Adaptation Segmentation +1

Classification of lung cancer subtypes on CT images with synthetic pathological priors

no code implementations9 Aug 2023 Wentao Zhu, Yuan Jin, Gege Ma, Geng Chen, Jan Egger, Shaoting Zhang, Dimitris N. Metaxas

The accurate diagnosis on pathological subtypes for lung cancer is of significant importance for the follow-up treatments and prognosis managements.

Computed Tomography (CT)

Pre-train, Adapt and Detect: Multi-Task Adapter Tuning for Camouflaged Object Detection

no code implementations20 Jul 2023 Yinghui Xing, Dexuan Kong, Shizhou Zhang, Geng Chen, Lingyan Ran, Peng Wang, Yanning Zhang

Camouflaged object detection (COD), aiming to segment camouflaged objects which exhibit similar patterns with the background, is a challenging task.

Multi-Task Learning object-detection +1

Predictive Experience Replay for Continual Visual Control and Forecasting

2 code implementations12 Mar 2023 Wendong Zhang, Geng Chen, Xiangming Zhu, Siyu Gao, Yunbo Wang, Xiaokang Yang

In this paper, we present a new continual learning approach for visual dynamics modeling and explore its efficacy in visual control and forecasting.

Continual Learning Model-based Reinforcement Learning +2

Camouflaged Object Detection via Context-aware Cross-level Fusion

2 code implementations27 Jul 2022 Geng Chen, Si-Jie Liu, Yu-Jia Sun, Ge-Peng Ji, Ya-Feng Wu, Tao Zhou

To address these challenges, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net), which fuses context-aware cross-level features for accurately identifying camouflaged objects.

object-detection Object Detection

Continual Predictive Learning from Videos

1 code implementation CVPR 2022 Geng Chen, Wendong Zhang, Han Lu, Siyu Gao, Yunbo Wang, Mingsheng Long, Xiaokang Yang

Can we develop predictive learning algorithms that can deal with more realistic, non-stationary physical environments?

Continual Learning Test-time Adaptation +1

Specificity-preserving RGB-D Saliency Detection

3 code implementations ICCV 2021 Tao Zhou, Deng-Ping Fan, Geng Chen, Yi Zhou, Huazhu Fu

To effectively fuse cross-modal features in the shared learning network, we propose a cross-enhanced integration module (CIM) and then propagate the fused feature to the next layer for integrating cross-level information.

Decoder object-detection +5

Multi-Modal Transformer for Accelerated MR Imaging

1 code implementation27 Jun 2021 Chun-Mei Feng, Yunlu Yan, Geng Chen, Yong Xu, Ling Shao, Huazhu Fu

To this end, we propose a multi-modal transformer (MTrans), which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging.

Image Reconstruction Super-Resolution

Context-aware Cross-level Fusion Network for Camouflaged Object Detection

2 code implementations26 May 2021 Yujia Sun, Geng Chen, Tao Zhou, Yi Zhang, Nian Liu

Camouflaged object detection (COD) is a challenging task due to the low boundary contrast between the object and its surroundings.

Object object-detection +1

Learning Synergistic Attention for Light Field Salient Object Detection

1 code implementation28 Apr 2021 Yi Zhang, Geng Chen, Qian Chen, Yujia Sun, Yong Xia, Olivier Deforges, Wassim Hamidouche, Lu Zhang

We propose a novel Synergistic Attention Network (SA-Net) to address the light field salient object detection by establishing a synergistic effect between multi-modal features with advanced attention mechanisms.

Object object-detection +2

Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction

1 code implementation12 Apr 2021 Chun-Mei Feng, Zhanyuan Yang, Geng Chen, Yong Xu, Ling Shao

We evaluate the performance of the proposed model on the acceleration of multi-coil MR image reconstruction.

Image Reconstruction

Towards Accurate RGB-D Saliency Detection with Complementary Attention and Adaptive Integration

no code implementations8 Feb 2021 Hong-Bo Bi, Zi-Qi Liu, Kang Wang, Bo Dong, Geng Chen, Ji-Quan Ma

In this paper, we propose Complementary Attention and Adaptive Integration Network (CAAI-Net), a novel RGB-D saliency detection model that integrates complementary attention based feature concentration and adaptive cross-modal feature fusion into a unified framework for accurate saliency detection.

Saliency Detection

Accurate Camouflaged Object Detection via Mixture Convolution and Interactive Fusion

no code implementations14 Jan 2021 Bo Dong, Mingchen Zhuge, Yongxiong Wang, Hongbo Bi, Geng Chen

Our method detects camouflaged objects with an effective fusion strategy, which aggregates the rich context information from a large receptive field.

object-detection Object Detection

Enhanced Information Fusion Network for Crowd Counting

no code implementations12 Jan 2021 Geng Chen, Peirong Guo

We introduce the Information Fusion Module (IFM) which provides a channel for information flow to help different columns to obtain significant information from another column.

Crowd Counting

EF-Net: A novel enhancement and fusion network for RGB-D saliency detection

1 code implementation4 Nov 2020 Qian Chen, Keren Fu, Ze Liu, Geng Chen, Hongwei Du, Bensheng Qiu, LingShao

Finally, we propose an effective layer-wise aggregation module to fuse the features extracted from the enhanced depth maps and RGB images for the accurate detection of salient objects.

object-detection Object Detection +2

Uniqueness and weak-BV stability for $2\times 2$ conservation laws

no code implementations9 Oct 2020 Geng Chen, Sam G. Krupa, Alexis F. Vasseur

As a consequence of our result, the Tame Oscillation Condition, and the Bounded Variation Condition on space-like curves are not necessary for the uniqueness of solutions in the $BV$ theory, in the case of systems with 2 unknowns.

Analysis of PDEs 35L65 (Primary) 76N15, 35L45, 35B35 (Secondary)

Multifold Acceleration of Diffusion MRI via Slice-Interleaved Diffusion Encoding (SIDE)

no code implementations25 Feb 2020 Yoonmi Hong, Wei-Tang Chang, Geng Chen, Ye Wu, Weili Lin, Dinggang Shen, Pew-Thian Yap

Diffusion MRI (dMRI) is a unique imaging technique for in vivo characterization of tissue microstructure and white matter pathways.

Hi-Net: Hybrid-fusion Network for Multi-modal MR Image Synthesis

2 code implementations11 Feb 2020 Tao Zhou, Huazhu Fu, Geng Chen, Jianbing Shen, Ling Shao

Medical image synthesis has been proposed as an effective solution to this, where any missing modalities are synthesized from the existing ones.

Image Generation

DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks

no code implementations7 Jun 2019 Feihong Liu, Jun Feng, Geng Chen, Ye Wu, Yoonmi Hong, Pew-Thian Yap, Dinggang Shen

GCNNs are capable of extracting the geometric features of each fiber tract and harnessing the resulting features for accurate fiber parcellation and ultimately avoiding the use of atlases and any registration method.

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