no code implementations • 14 Apr 2024 • Weimin WANG, Min Gao, Mingxuan Xiao, Xu Yan, Yufeng Li
To address the issues of limited samples, time-consuming feature design, and low accuracy in detection and classification of breast cancer pathological images, a breast cancer image classification model algorithm combining deep learning and transfer learning is proposed.
no code implementations • 12 Apr 2024 • Mingxuan Xiao, Yufeng Li, Xu Yan, Min Gao, Weimin WANG
To address the challenges of dependence on pathologists expertise and the time-consuming nature of achieving accurate breast pathological image classification, this paper introduces an approach utilizing convolutional neural networks (CNNs) for the rapid categorization of pathological images, aiming to enhance the efficiency of breast pathological image detection.
no code implementations • 11 Apr 2024 • Xu Yan, Weimin WANG, Mingxuan Xiao, Yufeng Li, Min Gao
This study introduces a pioneering approach to enhance survival prediction models for gastric and Colon adenocarcinoma patients.
1 code implementation • 13 Mar 2024 • Zezeng Li, Weimin WANG, Ziliang Wang, Na lei
This paper presents a novel point cloud compression method COT-PCC by formulating the task as a constrained optimal transport (COT) problem.
no code implementations • 9 Jan 2024 • Weimin WANG, Jiawei Liu, Zhijie Lin, Jiangqiao Yan, Shuo Chen, Chetwin Low, Tuyen Hoang, Jie Wu, Jun Hao Liew, Hanshu Yan, Daquan Zhou, Jiashi Feng
The growing demand for high-fidelity video generation from textual descriptions has catalyzed significant research in this field.
1 code implementation • IEEE ROBOTICS AND AUTOMATION LETTERS 2023 • Weimin WANG, Ting Yang, Yu Du, Yu Liu
The proposed approach first constructs the CRF based on k-nearest neighbors with the snow confidence derived from the physical priors of snow, such as intensity and distribution.
1 code implementation • 4 Aug 2023 • Haoyi Xiu, Xin Liu, Weimin WANG, Kyoung-Sook Kim, Masashi Matsuoka
MSECNet consists of a backbone network and a multi-scale edge conditioning (MSEC) stream.
no code implementations • 1 Dec 2022 • Qiong Chang, Aolong Zha, Weimin WANG, Xin Liu, Masaki Onishi, Lei Lei, Meng Joo Er, Tsutomu Maruyama
By combining this technique with the domain transformation (DT) algorithm, our system show real-time processing speed of 32 fps, on a Jetson Tx2 GPU for 1, 280x384 pixel images with a maximum disparity of 128.
no code implementations • 1 Dec 2022 • Yutaka Momma, Weimin WANG, Edgar Simo-Serra, Satoshi Iizuka, Ryosuke Nakamura, Hiroshi Ishikawa
To remedy this problem, we propose to explicitly train a network to refine these results predicted by an existing segmentation method.
no code implementations • 20 Nov 2022 • Daquan Zhou, Weimin WANG, Hanshu Yan, Weiwei Lv, Yizhe Zhu, Jiashi Feng
In specific, unlike existing works that directly train video models in the RGB space, we use a pre-trained VAE to map video clips into a low-dimensional latent space and learn the distribution of videos' latent codes via a diffusion model.
Ranked #10 on Text-to-Video Generation on MSR-VTT
1 code implementation • 20 Sep 2022 • Haoyi Xiu, Xin Liu, Weimin WANG, Kyoung-Sook Kim, Takayuki Shinohara, Qiong Chang, Masashi Matsuoka
Second, we experimentally observe and verify the edge enhancement and suppression behavior.
Ranked #3 on 3D Part Segmentation on ShapeNet-Part
no code implementations • 4 Aug 2022 • Zhenqiang Li, Lin Gu, Weimin WANG, Ryosuke Nakamura, Yoichi Sato
Automated video-based assessment of surgical skills is a promising task in assisting young surgical trainees, especially in poor-resource areas.
no code implementations • 4 Jul 2022 • Haoyi Xiu, Xin Liu, Weimin WANG, Kyoung-Sook Kim, Takayuki Shinohara, Qiong Chang, Masashi Matsuoka
Modeling the local surface geometry is challenging in 3D point cloud understanding due to the lack of connectivity information.
no code implementations • 4 Jul 2022 • Haoyi Xiu, Xin Liu, Weimin WANG, Kyoung-Sook Kim, Takayuki Shinohara, Qiong Chang, Masashi Matsuoka
Learning point clouds is challenging due to the lack of connectivity information, i. e., edges.
no code implementations • 1 Mar 2022 • Haoyi Xiu, Xin Liu, Weimin WANG, Kyoung-Sook Kim, Takayuki Shinohara, Qiong Chang, Masashi Matsuoka
We present a simple but effective attention named the unary-pairwise attention (UPA) for modeling the relationship between 3D point clouds.
no code implementations • 28 Jan 2022 • Ali Caglayan, Nevrez Imamoglu, Oguzhan Guclu, Ali Osman Serhatoglu, Weimin WANG, Ahmet Burak Can, Ryosuke Nakamura
This can be very useful for visual tasks such as simultaneous localization and mapping (SLAM) where CNN representations of spatially attentive object locations may lead to improved performance.
no code implementations • 6 Oct 2021 • Cheng Xu, Weimin WANG, Shuai Liu, Yong Wang, Yuxiang Tang, Tianling Bian, Yanyu Yan, Qi She, Cheng Yang
In this paper, we show our solution to the Google Landmark Recognition 2021 Competition.
1 code implementation • 1 Sep 2021 • Zhenqiang Li, Weimin WANG, Zuoyue Li, Yifei HUANG, Yoichi Sato
The attribution method provides a direction for interpreting opaque neural networks in a visual way by identifying and visualizing the input regions/pixels that dominate the output of a network.
2 code implementations • 1 May 2020 • Zhenqiang Li, Weimin WANG, Zuoyue Li, Yifei HUANG, Yoichi Sato
''Making black box models explainable'' is a vital problem that accompanies the development of deep learning networks.
no code implementations • 21 Apr 2020 • Xuanyu YIN, Yoko SASAKI, Weimin WANG, Kentaro SHIMIZU
In our research, Camera can capture the image to make the Real-time 2D Object Detection by using YOLO, I transfer the bounding box to node whose function is making 3d object detection on point cloud data from Lidar.
no code implementations • 21 Apr 2020 • Xuanyu YIN, Yoko SASAKI, Weimin WANG, Kentaro SHIMIZU
In our research, camera can capture the image to make the Real-time 2D object detection by using YOLO, we transfer the bounding box to node whose function is making 3d object detection on point cloud data from Lidar.
no code implementations • 9 Mar 2020 • Weimin Wang, Shohei Nobuhara, Ryosuke Nakamura, Ken Sakurada
This paper presents a novel semantic-based online extrinsic calibration approach, SOIC (so, I see), for Light Detection and Ranging (LiDAR) and camera sensors.
10 code implementations • 29 Oct 2019 • Roman Solovyev, Weimin WANG, Tatiana Gabruseva
In this work, we present a novel method for combining predictions of object detection models: weighted boxes fusion.
1 code implementation • 16 Sep 2019 • Weimin Wang, Weiran Wang, Ming Sun, Chao Wang
Acoustic Scene Classification (ASC) is a challenging task, as a single scene may involve multiple events that contain complex sound patterns.
no code implementations • 14 Mar 2019 • Xu Cao, Weimin WANG, Katashi Nagao
How can we edit or transform the geometric or color property of a point cloud?
1 code implementation • 29 Nov 2018 • Ken Sakurada, Mikiya Shibuya, Weimin WANG
A straightforward approach for this task is to train a semantic change detection network directly from a large-scale dataset in an end-to-end manner.
no code implementations • 8 Dec 2017 • Ken Sakurada, Weimin WANG, Nobuo Kawaguchi, Ryosuke Nakamura
This paper presents a novel method for detecting scene changes from a pair of images with a difference of camera viewpoints using a dense optical flow based change detection network.
no code implementations • 13 Oct 2017 • Kenji Enomoto, Ken Sakurada, Weimin WANG, Hiroshi Fukui, Masashi Matsuoka, Ryosuke Nakamura, Nobuo Kawaguchi
The networks are trained to output images that are close to the ground truth using the images synthesized with clouds over the ground truth as inputs.
Ranked #8 on Cloud Removal on SEN12MS-CR
1 code implementation • 18 Aug 2017 • Weimin Wang, Ken Sakurada, Nobuo Kawaguchi
Once the corners of the chessboard in the 3D point cloud are estimated, the extrinsic calibration of the two sensors is converted to a 3D-2D matching problem.