Search Results for author: Jieneng Chen

Found 22 papers, 15 papers with code

GenEx: Generating an Explorable World

no code implementations12 Dec 2024 Taiming Lu, Tianmin Shu, Junfei Xiao, Luoxin Ye, Jiahao Wang, Cheng Peng, Chen Wei, Daniel Khashabi, Rama Chellappa, Alan Yuille, Jieneng Chen

In this work, we take a step toward this goal by introducing GenEx, a system capable of planning complex embodied world exploration, guided by its generative imagination that forms priors (expectations) about the surrounding environments.

3DSRBench: A Comprehensive 3D Spatial Reasoning Benchmark

no code implementations10 Dec 2024 Wufei Ma, Haoyu Chen, Guofeng Zhang, Celso M de Melo, Alan Yuille, Jieneng Chen

We benchmark a wide range of open-sourced and proprietary LMMs, uncovering their limitations in various aspects of 3D awareness, such as height, orientation, location, and multi-object reasoning, as well as their degraded performance on images with uncommon camera viewpoints.

Autonomous Navigation Spatial Reasoning +1

Generative World Explorer

no code implementations18 Nov 2024 Taiming Lu, Tianmin Shu, Alan Yuille, Daniel Khashabi, Jieneng Chen

Our experimental results demonstrate that (1) $\textit{Genex}$ can generate high-quality and consistent observations during long-horizon exploration of a large virtual physical world and (2) the beliefs updated with the generated observations can inform an existing decision-making model (e. g., an LLM agent) to make better plans.

Efficient Large Multi-modal Models via Visual Context Compression

1 code implementation28 Jun 2024 Jieneng Chen, Luoxin Ye, Ju He, Zhao-Yang Wang, Daniel Khashabi, Alan Yuille

While significant advancements have been made in compressed representations for text embeddings in large language models (LLMs), the compression of visual tokens in multi-modal LLMs (MLLMs) has remained a largely overlooked area.

Question Answering Visual Question Answering

3D-TransUNet for Brain Metastases Segmentation in the BraTS2023 Challenge

1 code implementation23 Mar 2024 Siwei Yang, Xianhang Li, Jieru Mei, Jieneng Chen, Cihang Xie, Yuyin Zhou

We identify that the Decoder-only 3D-TransUNet model should offer enhanced efficacy in the segmentation of brain metastases, as indicated by our 5-fold cross-validation on the training set.

Brain Tumor Segmentation Decoder +2

3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers

3 code implementations11 Oct 2023 Jieneng Chen, Jieru Mei, Xianhang Li, Yongyi Lu, Qihang Yu, Qingyue Wei, Xiangde Luo, Yutong Xie, Ehsan Adeli, Yan Wang, Matthew Lungren, Lei Xing, Le Lu, Alan Yuille, Yuyin Zhou

In this paper, we extend the 2D TransUNet architecture to a 3D network by building upon the state-of-the-art nnU-Net architecture, and fully exploring Transformers' potential in both the encoder and decoder design.

Decoder Image Segmentation +4

Compositor: Bottom-up Clustering and Compositing for Robust Part and Object Segmentation

1 code implementation CVPR 2023 Ju He, Jieneng Chen, Ming-Xian Lin, Qihang Yu, Alan Yuille

Compositor achieves state-of-the-art performance on PartImageNet and Pascal-Part by outperforming previous methods by around 0. 9% and 1. 3% on PartImageNet, 0. 4% and 1. 7% on Pascal-Part in terms of part and object mIoU and demonstrates better robustness against occlusion by around 4. 4% and 7. 1% on part and object respectively.

Clustering Object +2

Label-Free Liver Tumor Segmentation

1 code implementation CVPR 2023 Qixin Hu, Yixiong Chen, Junfei Xiao, Shuwen Sun, Jieneng Chen, Alan Yuille, Zongwei Zhou

We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans.

Segmentation Tumor Segmentation

MT-TransUNet: Mediating Multi-Task Tokens in Transformers for Skin Lesion Segmentation and Classification

1 code implementation3 Dec 2021 Jingye Chen, Jieneng Chen, Zongwei Zhou, Bin Li, Alan Yuille, Yongyi Lu

However, these approaches formulated skin cancer diagnosis as a simple classification task, dismissing the potential benefit from lesion segmentation.

Classification Computational Efficiency +4

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

4 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 +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

Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining

no code implementations9 Mar 2021 Jieneng Chen, Ke Yan, Yu-Dong Zhang, YouBao Tang, Xun Xu, Shuwen Sun, Qiuping Liu, Lingyun Huang, Jing Xiao, Alan L. Yuille, Ya zhang, Le Lu

(2) The sampled deep vertex features with positional embedding are mapped into a sequential space and decoded by a multilayer perceptron (MLP) for semantic classification.

valid

SpecTr: Spectral Transformer for Hyperspectral Pathology Image Segmentation

1 code implementation5 Mar 2021 Boxiang Yun, Yan Wang, Jieneng Chen, Huiyu Wang, Wei Shen, Qingli Li

Hyperspectral imaging (HSI) unlocks the huge potential to a wide variety of applications relied on high-precision pathology image segmentation, such as computational pathology and precision medicine.

Image Segmentation Segmentation +1

TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

22 code implementations8 Feb 2021 Jieneng Chen, Yongyi Lu, Qihang Yu, Xiangde Luo, Ehsan Adeli, Yan Wang, Le Lu, Alan L. Yuille, Yuyin Zhou

Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning.

Cardiac Segmentation Decoder +4

Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency

2 code implementations13 Dec 2020 Xiangde Luo, Wenjun Liao, Jieneng Chen, Tao Song, Yinan Chen, Shichuan Zhang, Nianyong Chen, Guotai Wang, Shaoting Zhang

In this paper, we propose a novel framework with Uncertainty Rectified Pyramid Consistency (URPC) regularization for semi-supervised NPC GTV segmentation.

Segmentation

Semi-supervised Medical Image Segmentation through Dual-task Consistency

1 code implementation9 Sep 2020 Xiangde Luo, Jieneng Chen, Tao Song, Yinan Chen, Guotai Wang, Shaoting Zhang

Concretely, we use a dual-task deep network that jointly predicts a pixel-wise segmentation map and a geometry-aware level set representation of the target.

Image Segmentation Segmentation +2

Deep Distance Transform for Tubular Structure Segmentation in CT Scans

no code implementations CVPR 2020 Yan Wang, Xu Wei, Fengze Liu, Jieneng Chen, Yuyin Zhou, Wei Shen, Elliot K. Fishman, Alan L. Yuille

Tubular structure segmentation in medical images, e. g., segmenting vessels in CT scans, serves as a vital step in the use of computers to aid in screening early stages of related diseases.

Segmentation

Towards Brain-inspired System: Deep Recurrent Reinforcement Learning for Simulated Self-driving Agent

no code implementations29 Mar 2019 Jieneng Chen, Jingye Chen, Ruiming Zhang, Xiaobin Hu

Because of the tremendous research that focuses on human brains and reinforcement learning, scientists have investigated how robots can autonomously tackle complex tasks in the form of a self-driving agent control in a human-like way.

Decision Making OpenAI Gym +3

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