no code implementations • 13 Mar 2024 • ran Xu, Yan Shen, Xiaoqi Li, Ruihai Wu, Hao Dong
To address these challenges, we introduce a comprehensive benchmark, NrVLM, comprising 15 distinct manipulation tasks, containing over 4500 episodes meticulously annotated with fine-grained language instructions.
no code implementations • 5 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.
no code implementations • CVPR 2024 • 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.
no code implementations • 21 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.
no code implementations • 30 Oct 2023 • Dang Minh Nguyen, Chenfei Wang, Yan Shen, Yifan Zeng
Graph Neural Network (GNN) is the trending solution for item retrieval in recommendation problems.
no code implementations • 13 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.
no code implementations • 17 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.
no code implementations • 11 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.
no code implementations • 28 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).
no code implementations • 5 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.
no code implementations • 4 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.
no code implementations • 24 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.
1 code implementation • 16 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.
no code implementations • 4 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.
no code implementations • 1 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.
no code implementations • Sensors 2021 • Xiaotao Shao, Qing Wang, Wei Yang, Yun Chen, Yi Xie, Yan Shen, Zhongli Wang
MFPN includes two modules, namely double feature pyramid network (FPN) integrated with ResNet (DFR) and repulsion loss of minimum (RLM).
no code implementations • 18 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
no code implementations • 1 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.
no code implementations • 5 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.
no code implementations • 25 Sep 2019 • Taojiannan Yang, Sijie Zhu, Yan Shen, Mi Zhang, Andrew Willis, Chen Chen
We propose a framework to mutually learn from different input resolutions and network widths.
no code implementations • 15 Apr 2019 • Yan Shen, Mingchen Gao
We design a brain tumor segmentation algorithm that is robust to the absence of any modality.
no code implementations • 17 Aug 2018 • Yan Shen, Mingchen Gao
We present and evaluate a new deep neural network architecture for automatic thoracic disease detection on chest X-rays.