Search Results for author: Dimitris N. Metaxas

Found 60 papers, 34 papers with code

Learning Trailer Moments in Full-Length Movies with Co-Contrastive Attention

no code implementations ECCV 2020 Lezi Wang, Dong Liu, Rohit Puri, Dimitris N. Metaxas

We introduce a novel ranking network that utilizes the Co-Attention between movies and trailers as guidance to generate the training pairs, where the moments highly corrected with trailers are expected to be scored higher than the uncorrelated moments.

Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI Reconstruction

1 code implementation25 Sep 2023 Bingyu Xin, Meng Ye, Leon Axel, Dimitris N. Metaxas

Then, we extend the baseline model to a prompt-based learning approach, PromptMR, for all-in-one MRI reconstruction from different views, contrasts, adjacent types, and acceleration factors.

Denoising MRI Reconstruction +1

Deep Deformable Models: Learning 3D Shape Abstractions with Part Consistency

no code implementations2 Sep 2023 Di Liu, Long Zhao, Qilong Zhangli, Yunhe Gao, Ting Liu, Dimitris N. Metaxas

The task of shape abstraction with semantic part consistency is challenging due to the complex geometries of natural objects.

DMCVR: Morphology-Guided Diffusion Model for 3D Cardiac Volume Reconstruction

1 code implementation18 Aug 2023 Xiaoxiao He, Chaowei Tan, Ligong Han, Bo Liu, Leon Axel, Kang Li, Dimitris N. Metaxas

However, current cardiac MRI-based reconstruction technology used in clinical settings is 2D with limited through-plane resolution, resulting in low-quality reconstructed cardiac volumes.

3D Reconstruction

Improving Pseudo Labels for Open-Vocabulary Object Detection

no code implementations11 Aug 2023 Shiyu Zhao, Samuel Schulter, Long Zhao, Zhixing Zhang, Vijay Kumar B. G, Yumin Suh, Manmohan Chandraker, Dimitris N. Metaxas

Second, a split-and-fusion (SAF) head is designed to remove the noise in localization of PLs, which is usually ignored in existing methods.

object-detection Open Vocabulary Object Detection

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)

Pathology-and-genomics Multimodal Transformer for Survival Outcome Prediction

1 code implementation22 Jul 2023 Kexin Ding, Mu Zhou, Dimitris N. Metaxas, Shaoting Zhang

Survival outcome assessment is challenging and inherently associated with multiple clinical factors (e. g., imaging and genomics biomarkers) in cancer.

Survival Prediction whole slide images

Training Like a Medical Resident: Universal Medical Image Segmentation via Context Prior Learning

2 code implementations4 Jun 2023 Yunhe Gao, Zhuowei Li, Di Liu, Mu Zhou, Shaoting Zhang, Dimitris N. Metaxas

Inspired by the training of medical residents, we explore universal medical image segmentation, whose goal is to learn from diverse medical imaging sources covering a range of clinical targets, body regions, and image modalities.

Image Segmentation Incremental Learning +3

Revisiting Multimodal Representation in Contrastive Learning: From Patch and Token Embeddings to Finite Discrete Tokens

1 code implementation CVPR 2023 Yuxiao Chen, Jianbo Yuan, Yu Tian, Shijie Geng, Xinyu Li, Ding Zhou, Dimitris N. Metaxas, Hongxia Yang

However, direct aligning cross-modal information using such representations is challenging, as visual patches and text tokens differ in semantic levels and granularities.

Contrastive Learning

Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge Distillation

1 code implementation25 Mar 2023 Xiaoxiao He, Chaowei Tan, Bo Liu, Liping Si, Weiwu Yao, Liang Zhao, Di Liu, Qilong Zhangli, Qi Chang, Kang Li, Dimitris N. Metaxas

The supervised learning of the proposed method extracts features from limited labeled data in each client, while the unsupervised data is used to distill both feature and response-based knowledge from a national data repository to further improve the accuracy of the collaborative model and reduce the communication cost.

Federated Learning Few-Shot Learning +1

Steering Prototype with Prompt-tuning for Rehearsal-free Continual Learning

no code implementations16 Mar 2023 Zhuowei Li, Long Zhao, Zizhao Zhang, Han Zhang, Di Liu, Ting Liu, Dimitris N. Metaxas

Prototype, as a representation of class embeddings, has been explored to reduce memory footprint or mitigate forgetting for continual learning scenarios.

class-incremental learning Class Incremental Learning +2

3D Tooth Mesh Segmentation with Simplified Mesh Cell Representation

1 code implementation25 Jan 2023 Ananya Jana, Hrebesh Molly Subhash, Dimitris N. Metaxas

Summarizing of the mesh cell/triangle in this manner imposes an implicit structural constraint and makes it difficult to work with multiple resolutions which is done in many point cloud based deep learning algorithms.

CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation

1 code implementation22 Nov 2022 Ran Gu, Guotai Wang, Jiangshan Lu, Jingyang Zhang, Wenhui Lei, Yinan Chen, Wenjun Liao, Shichuan Zhang, Kang Li, Dimitris N. Metaxas, Shaoting Zhang

First, a disentangle network is proposed to decompose an image into a domain-invariant anatomical representation and a domain-specific style code, where the former is sent to a segmentation model that is not affected by the domain shift, and the disentangle network is regularized by a decoder that combines the anatomical and style codes to reconstruct the input image.

Disentanglement Domain Generalization +3

Visual Prompt Tuning for Test-time Domain Adaptation

no code implementations10 Oct 2022 Yunhe Gao, Xingjian Shi, Yi Zhu, Hao Wang, Zhiqiang Tang, Xiong Zhou, Mu Li, Dimitris N. Metaxas

First, DePT plugs visual prompts into the vision Transformer and only tunes these source-initialized prompts during adaptation.

Unsupervised Domain Adaptation

Hierarchically Self-Supervised Transformer for Human Skeleton Representation Learning

1 code implementation20 Jul 2022 Yuxiao Chen, Long Zhao, Jianbo Yuan, Yu Tian, Zhaoyang Xia, Shijie Geng, Ligong Han, Dimitris N. Metaxas

Despite the success of fully-supervised human skeleton sequence modeling, utilizing self-supervised pre-training for skeleton sequence representation learning has been an active field because acquiring task-specific skeleton annotations at large scales is difficult.

Action Detection Action Recognition +3

Towards Self-supervised and Weight-preserving Neural Architecture Search

1 code implementation8 Jun 2022 Zhuowei Li, Yibo Gao, Zhenzhou Zha, Zhiqiang Hu, Qing Xia, Shaoting Zhang, Dimitris N. Metaxas

In this work, we propose the self-supervised and weight-preserving neural architecture search (SSWP-NAS) as an extension of the current NAS framework by allowing the self-supervision and retaining the concomitant weights discovered during the search stage.

Neural Architecture Search

Learning Transferable Reward for Query Object Localization with Policy Adaptation

1 code implementation ICLR 2022 Tingfeng Li, Shaobo Han, Martin Renqiang Min, Dimitris N. Metaxas

We propose a reinforcement learning based approach to query object localization, for which an agent is trained to localize objects of interest specified by a small exemplary set.

Metric Learning Object Localization +2

Learned Half-Quadratic Splitting Network for MR Image Reconstruction

1 code implementation17 Dec 2021 Bingyu Xin, Timothy S. Phan, Leon Axel, Dimitris N. Metaxas

Magnetic Resonance (MR) image reconstruction from highly undersampled $k$-space data is critical in accelerated MR imaging (MRI) techniques.

Image Reconstruction

WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image

3 code implementations3 Nov 2021 Xiangde Luo, Wenjun Liao, Jianghong Xiao, Jieneng Chen, Tao Song, Xiaofan Zhang, Kang Li, Dimitris N. Metaxas, Guotai Wang, Shaoting Zhang

Deep learning-based medical image segmentation has shown the potential to reduce manual delineation efforts, but it still requires a large-scale fine annotated dataset for training, and there is a lack of large-scale datasets covering the whole abdomen region with accurate and detailed annotations for the whole abdominal organ segmentation.

Image Segmentation Medical Image Segmentation +3

Hybrid Supervision Learning for Pathology Whole Slide Image Classification

1 code implementation2 Jul 2021 Jiahui Li, Wen Chen, Xiaodi Huang, Zhiqiang Hu, Qi Duan, Hongsheng Li, Dimitris N. Metaxas, Shaoting Zhang

To handle this problem, we propose a hybrid supervision learning framework for this kind of high resolution images with sufficient image-level coarse annotations and a few pixel-level fine labels.

Classification Image Classification +3

Improved Transformer for High-Resolution GANs

1 code implementation NeurIPS 2021 Long Zhao, Zizhao Zhang, Ting Chen, Dimitris N. Metaxas, Han Zhang

Attention-based models, exemplified by the Transformer, can effectively model long range dependency, but suffer from the quadratic complexity of self-attention operation, making them difficult to be adopted for high-resolution image generation based on Generative Adversarial Networks (GANs).

Ranked #2 on Image Generation on CelebA 256x256 (FID metric)

Image Generation Vocal Bursts Intensity Prediction

More Than Just Attention: Improving Cross-Modal Attentions with Contrastive Constraints for Image-Text Matching

no code implementations20 May 2021 Yuxiao Chen, Jianbo Yuan, Long Zhao, Tianlang Chen, Rui Luo, Larry Davis, Dimitris N. Metaxas

Cross-modal attention mechanisms have been widely applied to the image-text matching task and have achieved remarkable improvements thanks to its capability of learning fine-grained relevance across different modalities.

Contrastive Learning Image Captioning +4

SCPM-Net: An Anchor-free 3D Lung Nodule Detection Network using Sphere Representation and Center Points Matching

1 code implementation12 Apr 2021 Xiangde Luo, Tao Song, Guotai Wang, Jieneng Chen, Yinan Chen, Kang Li, Dimitris N. Metaxas, Shaoting Zhang

To overcome these problems, we propose a 3D sphere representation-based center-points matching detection network that is anchor-free and automatically predicts the position, radius, and offset of nodules without the manual design of nodule/anchor parameters.

Lung Nodule Detection

Deep Animation Video Interpolation in the Wild

1 code implementation CVPR 2021 Li SiYao, Shiyu Zhao, Weijiang Yu, Wenxiu Sun, Dimitris N. Metaxas, Chen Change Loy, Ziwei Liu

In the animation industry, cartoon videos are usually produced at low frame rate since hand drawing of such frames is costly and time-consuming.

Optical Flow Estimation Video Frame Interpolation

Variational Bayesian Sequence-to-Sequence Networks for Memory-Efficient Sign Language Translation

no code implementations11 Feb 2021 Harris Partaourides, Andreas Voskou, Dimitrios Kosmopoulos, Sotirios Chatzis, Dimitris N. Metaxas

Memory-efficient continuous Sign Language Translation is a significant challenge for the development of assisted technologies with real-time applicability for the deaf.

Sign Language Translation Translation

Semantic Aware Data Augmentation for Cell Nuclei Microscopical Images With Artificial Neural Networks

no code implementations ICCV 2021 Alireza Naghizadeh, Hongye Xu, Mohab Mohamed, Dimitris N. Metaxas, Dongfang Liu

The importance of this subject is nested in the amount of training data that artificial neural networks need to accurately identify and segment objects in images and the infeasibility of acquiring a sufficient dataset within the biomedical field.

Data Augmentation object-detection +2

Unity of Opposites: SelfNorm and CrossNorm for Model Robustness

no code implementations1 Jan 2021 Zhiqiang Tang, Yunhe Gao, Yi Zhu, Zhi Zhang, Mu Li, Dimitris N. Metaxas

CrossNorm exchanges styles between feature channels to perform style augmentation, diversifying the content and style mixtures.

Object Recognition Unity

Multi-modal AsynDGAN: Learn From Distributed Medical Image Data without Sharing Private Information

no code implementations15 Dec 2020 Qi Chang, Zhennan Yan, Lohendran Baskaran, Hui Qu, Yikai Zhang, Tong Zhang, Shaoting Zhang, Dimitris N. Metaxas

As deep learning technologies advance, increasingly more data is necessary to generate general and robust models for various tasks.

Learning Trailer Moments in Full-Length Movies

no code implementations19 Aug 2020 Lezi Wang, Dong Liu, Rohit Puri, Dimitris N. Metaxas

A movie's key moments stand out of the screenplay to grab an audience's attention and make movie browsing efficient.

Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images

no code implementations10 Jul 2020 Hui Qu, Pengxiang Wu, Qiaoying Huang, Jingru Yi, Zhennan Yan, Kang Li, Gregory M. Riedlinger, Subhajyoti De, Shaoting Zhang, Dimitris N. Metaxas

To alleviate such tedious and manual effort, in this paper we propose a novel weakly supervised segmentation framework based on partial points annotation, i. e., only a small portion of nuclei locations in each image are labeled.

Weakly supervised segmentation

Knowledge as Priors: Cross-Modal Knowledge Generalization for Datasets without Superior Knowledge

no code implementations CVPR 2020 Long Zhao, Xi Peng, Yuxiao Chen, Mubbasir Kapadia, Dimitris N. Metaxas

Our key idea is to generalize the distilled cross-modal knowledge learned from a Source dataset, which contains paired examples from both modalities, to the Target dataset by modeling knowledge as priors on parameters of the Student.

3D Hand Pose Estimation Knowledge Distillation

Vertebra-Focused Landmark Detection for Scoliosis Assessment

1 code implementation9 Jan 2020 Jingru Yi, Pengxiang Wu, Qiaoying Huang, Hui Qu, Dimitris N. Metaxas

The comparison results demonstrate the merits of our method in both Cobb angle measurement and landmark detection on low-contrast and ambiguous X-ray images.

Object-Guided Instance Segmentation for Biological Images

no code implementations20 Nov 2019 Jingru Yi, Hui Tang, Pengxiang Wu, Bo Liu, Daniel J. Hoeppner, Dimitris N. Metaxas, Lianyi Han, Wei Fan

Along with the instance normalization, the model is able to recover the target object distribution and suppress the distribution of neighboring attached objects.

Clustering Instance Segmentation +4

Collaborative Multi-agent Learning for MR Knee Articular Cartilage Segmentation

no code implementations13 Aug 2019 Chaowei Tan, Zhennan Yan, Shaoting Zhang, Kang Li, Dimitris N. Metaxas

However, effective and efficient delineation of all the knee articular cartilages in large-sized and high-resolution 3D MR knee data is still an open challenge.

Decision Making

Greedy AutoAugment

2 code implementations2 Aug 2019 Alireza Naghizadeh, Mohammadsajad Abavisani, Dimitris N. Metaxas

This is a challenging problem and requires exploration for data augmentation policies to ensure their effectiveness in covering the search space.

Data Augmentation

2nd Place Solution to the GQA Challenge 2019

no code implementations16 Jul 2019 Shijie Geng, Ji Zhang, Hang Zhang, Ahmed Elgammal, Dimitris N. Metaxas

We present a simple method that achieves unexpectedly superior performance for Complex Reasoning involved Visual Question Answering.

Question Answering Visual Question Answering +1

Sharpen Focus: Learning with Attention Separability and Consistency

1 code implementation ICCV 2019 Lezi Wang, Ziyan Wu, Srikrishna Karanam, Kuan-Chuan Peng, Rajat Vikram Singh, Bo Liu, Dimitris N. Metaxas

Recent developments in gradient-based attention modeling have seen attention maps emerge as a powerful tool for interpreting convolutional neural networks.

General Classification Image Classification

CR-GAN: Learning Complete Representations for Multi-view Generation

1 code implementation28 Jun 2018 Yu Tian, Xi Peng, Long Zhao, Shaoting Zhang, Dimitris N. Metaxas

Generating multi-view images from a single-view input is an essential yet challenging problem.

Self-Supervised Learning

Scenarios: A New Representation for Complex Scene Understanding

no code implementations16 Feb 2018 Zachary A. Daniels, Dimitris N. Metaxas

The ability for computational agents to reason about the high-level content of real world scene images is important for many applications.

Image Retrieval Object Recognition +3

RED-Net: A Recurrent Encoder-Decoder Network for Video-based Face Alignment

no code implementations17 Jan 2018 Xi Peng, Rogerio S. Feris, Xiaoyu Wang, Dimitris N. Metaxas

We propose a novel method for real-time face alignment in videos based on a recurrent encoder-decoder network model.

Face Alignment

Cartoonish sketch-based face editing in videos using identity deformation transfer

no code implementations25 Mar 2017 Long Zhao, Fangda Han, Xi Peng, Xun Zhang, Mubbasir Kapadia, Vladimir Pavlovic, Dimitris N. Metaxas

We first recover the facial identity and expressions from the video by fitting a face morphable model for each frame.

Face Model

Multispectral Deep Neural Networks for Pedestrian Detection

1 code implementation8 Nov 2016 Jingjing Liu, Shaoting Zhang, Shu Wang, Dimitris N. Metaxas

Multispectral pedestrian detection is essential for around-the-clock applications, e. g., surveillance and autonomous driving.

Pedestrian Detection

Track Facial Points in Unconstrained Videos

no code implementations9 Sep 2016 Xi Peng, Qiong Hu, Junzhou Huang, Dimitris N. Metaxas

Our approach takes advantage of part-based representation and cascade regression for robust and efficient alignment on each frame.

Incremental Learning

A Recurrent Encoder-Decoder Network for Sequential Face Alignment

no code implementations19 Aug 2016 Xi Peng, Rogerio S. Feris, Xiaoyu Wang, Dimitris N. Metaxas

We propose a novel recurrent encoder-decoder network model for real-time video-based face alignment.

Face Alignment

Visual Tracking via Reliable Memories

no code implementations4 Feb 2016 Shu Wang, Shaoting Zhang, Wei Liu, Dimitris N. Metaxas

In this paper, we propose a novel visual tracking framework that intelligently discovers reliable patterns from a wide range of video to resist drift error for long-term tracking tasks.

Clustering Visual Tracking

PIEFA: Personalized Incremental and Ensemble Face Alignment

no code implementations ICCV 2015 Xi Peng, Shaoting Zhang, Yu Yang, Dimitris N. Metaxas

Face alignment, especially on real-time or large-scale sequential images, is a challenging task with broad applications.

Face Alignment Incremental Learning

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