Search Results for author: Yan Shen

Found 21 papers, 1 papers with code

Continual Domain Adversarial Adaptation via Double-Head Discriminators

no code implementations5 Feb 2024 Yan Shen, Zhanghexuan Ji, Chunwei Ma, Mingchen Gao

We prove that with the introduction of a pre-trained source-only domain discriminator, the empirical estimation error of $\gH$-divergence related adversarial loss is reduced from the source domain side.

Continual Learning Domain Adaptation

ManipLLM: Embodied Multimodal Large Language Model for Object-Centric Robotic Manipulation

no code implementations24 Dec 2023 Xiaoqi Li, Mingxu Zhang, Yiran Geng, Haoran Geng, Yuxing Long, Yan Shen, Renrui Zhang, Jiaming Liu, Hao Dong

By fine-tuning the injected adapters, we preserve the inherent common sense and reasoning ability of the MLLMs while equipping them with the ability for manipulation.

Common Sense Reasoning Language Modelling +3

Learning Part Motion of Articulated Objects Using Spatially Continuous Neural Implicit Representations

no code implementations21 Nov 2023 Yushi Du, Ruihai Wu, Yan Shen, Hao Dong

More importantly, while many methods could only model a certain kind of joint motion (such as the revolution in the clockwise order), our proposed framework is generic to different kinds of joint motions in that transformation matrix can model diverse kinds of joint motions in the space.

ImageManip: Image-based Robotic Manipulation with Affordance-guided Next View Selection

no code implementations13 Oct 2023 Xiaoqi Li, Yanzi Wang, Yan Shen, Ponomarenko Iaroslav, Haoran Lu, Qianxu Wang, Boshi An, Jiaming Liu, Hao Dong

This framework is designed to capture multiple perspectives of the target object and infer depth information to complement its geometry.

Object Robot Manipulation

CLIP-Lung: Textual Knowledge-Guided Lung Nodule Malignancy Prediction

no code implementations17 Apr 2023 Yiming Lei, Zilong Li, Yan Shen, Junping Zhang, Hongming Shan

Drawing on the capability of the contrastive language-image pre-training (CLIP) model to learn generalized visual representations from text annotations, in this paper, we propose CLIP-Lung, a textual knowledge-guided framework for lung nodule malignancy prediction.

Attribute Contrastive Learning

LSDM: Long-Short Diffeomorphic Motion for Weakly-Supervised Ultrasound Landmark Tracking

no code implementations11 Jan 2023 Zhihua Liu, Bin Yang, Yan Shen, Xuejun Ni, Huiyu Zhou

In this paper, we propose a long-short diffeomorphic motion network, which is a multi-task framework with a learnable deformation prior to search for the plausible deformation of landmark.

Landmark Tracking

Progressive Voronoi Diagram Subdivision: Towards A Holistic Geometric Framework for Exemplar-free Class-Incremental Learning

no code implementations28 Jul 2022 Chunwei Ma, Zhanghexuan Ji, Ziyun Huang, Yan Shen, Mingchen Gao, Jinhui Xu

Exemplar-free Class-incremental Learning (CIL) is a challenging problem because rehearsing data from previous phases is strictly prohibited, causing catastrophic forgetting of Deep Neural Networks (DNNs).

Class Incremental Learning Incremental Learning +1

A Bayesian Detect to Track System for Robust Visual Object Tracking and Semi-Supervised Model Learning

no code implementations5 May 2022 Yan Shen, Zhanghexuan Ji, Chunwei Ma, Mingchen Gao

Based on our particle filter inference algorithm, a semi-supervised learn-ing algorithm is utilized for learning tracking network on intermittent labeled frames by variational inference.

Variational Inference Visual Object Tracking

Learning Individual Interactions from Population Dynamics with Discrete-Event Simulation Model

no code implementations4 May 2022 Yan Shen, Fan Yang, Mingchen Gao, Wen Dong

Traditional machine learning approaches capture complex system dynamics either with dynamic Bayesian networks and state space models, which is hard to scale because it is non-trivial to prescribe the dynamics with a sparse graph or a system of differential equations; or a deep neural networks, where the distributed representation of the learned dynamics is hard to interpret.

Confidence Intervals of Treatment Effects in Panel Data Models with Interactive Fixed Effects

no code implementations24 Feb 2022 Xingyu Li, Yan Shen, Qiankun Zhou

We consider the construction of confidence intervals for treatment effects estimated using panel models with interactive fixed effects.

Matrix Completion

FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptation

1 code implementation16 Oct 2021 Yan Shen, Jian Du, Han Zhao, Benyu Zhang, Zhanghexuan Ji, Mingchen Gao

Federated adversary domain adaptation is a unique distributed minimax training task due to the prevalence of label imbalance among clients, with each client only seeing a subset of the classes of labels required to train a global model.

Domain Adaptation

LAViTeR: Learning Aligned Visual and Textual Representations Assisted by Image and Caption Generation

no code implementations4 Sep 2021 Mohammad Abuzar Shaikh, Zhanghexuan Ji, Dana Moukheiber, Yan Shen, Sargur Srihari, Mingchen Gao

Pre-training visual and textual representations from large-scale image-text pairs is becoming a standard approach for many downstream vision-language tasks.

Image Captioning Image Generation +2

An End-to-End learnable Flow Regularized Model for Brain Tumor Segmentation

no code implementations1 Sep 2021 Yan Shen, Zhanghexuan Ji, Mingchen Gao

Many segmentation tasks for biomedical images can be modeled as the minimization of an energy function and solved by a class of max-flow and min-cut optimization algorithms.

Brain Tumor Segmentation Segmentation +1

Strongly Connected Topology Model and Confirmation-based Propagation Method for Cross-chain Interaction

no code implementations18 Feb 2021 Hong Su, Bing Guo, Yan Shen, Tao Li

Meanwhile, different from legacy networks, the propagation method is required to keep the data validity.

Distributed, Parallel, and Cluster Computing

A Stochastic Gradient Langevin Dynamics Algorithm For Noise Intrinsic Federated Learning

no code implementations1 Jan 2021 Yan Shen, Jian Du, Chunwei Ma, Mingchen Gao, Benyu Zhang

Our introduced SGLD oracle would lower generalization errors in local node's parameter learning and provide local node DP protections.

Federated Learning

Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation

no code implementations5 Nov 2019 Zhanghexuan Ji, Yan Shen, Chunwei Ma, Mingchen Gao

In this paper, we use only two kinds of weak labels, i. e., scribbles on whole tumor and healthy brain tissue, and global labels for the presence of each substructure, to train a deep learning model to segment all the sub-regions.

Brain Tumor Segmentation Clustering +4

Brain Tumor Segmentation on MRI with Missing Modalities

no code implementations15 Apr 2019 Yan Shen, Mingchen Gao

We design a brain tumor segmentation algorithm that is robust to the absence of any modality.

Brain Tumor Segmentation Domain Adaptation +2

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