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Found 29 papers, 10 papers with code
Breast Cancer Image Classification Method Based on Deep Transfer Learning
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.
Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example
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.
Survival Prediction Across Diverse Cancer Types Using Neural Networks
This study introduces a pioneering approach to enhance survival prediction models for gastric and Colon adenocarcinoma patients.
Point Cloud Compression via Constrained Optimal Transport
This paper presents a novel point cloud compression method COT-PCC by formulating the task as a constrained optimal transport (COT) problem.
MagicVideo-V2: Multi-Stage High-Aesthetic Video Generation
The growing demand for high-fidelity video generation from textual descriptions has catalyzed significant research in this field.
Snow Removal for LiDAR Point Clouds with Spatio-temporal Conditional Random Fields
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.
MSECNet: Accurate and Robust Normal Estimation for 3D Point Clouds by Multi-Scale Edge Conditioning
MSECNet consists of a backbone network and a multi-scale edge conditioning (MSEC) stream.
Efficient stereo matching on embedded GPUs with zero-means cross correlation
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.
P2Net: A Post-Processing Network for Refining Semantic Segmentation of LiDAR Point Cloud based on Consistency of Consecutive Frames
To remedy this problem, we propose to explicitly train a network to refine these results predicted by an existing segmentation method.
MagicVideo: Efficient Video Generation With Latent Diffusion Models
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
Diffusion Unit: Interpretable Edge Enhancement and Suppression Learning for 3D Point Cloud Segmentation
Second, we experimentally observe and verify the edge enhancement and suppression behavior.
Ranked #3 on
3D Part Segmentation
on ShapeNet-Part
Surgical Skill Assessment via Video Semantic Aggregation
Automated video-based assessment of surgical skills is a promising task in assisting young surgical trainees, especially in poor-resource areas.
Enhancing Local Feature Learning Using Diffusion for 3D Point Cloud Understanding
Learning point clouds is challenging due to the lack of connectivity information, i. e., edges.
Enhancing Local Geometry Learning for 3D Point Cloud via Decoupling Convolution
Modeling the local surface geometry is challenging in 3D point cloud understanding due to the lack of connectivity information.
Enhancing Local Feature Learning for 3D Point Cloud Processing using Unary-Pairwise Attention
We present a simple but effective attention named the unary-pairwise attention (UPA) for modeling the relationship between 3D point clouds.
Exploring Object-Aware Attention Guided Frame Association for RGB-D SLAM
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.
3rd Place Solution to Google Landmark Recognition Competition 2021
In this paper, we show our solution to the Google Landmark Recognition 2021 Competition.
Spatio-Temporal Perturbations for Video Attribution
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.
Towards Visually Explaining Video Understanding Networks with Perturbation
''Making black box models explainable'' is a vital problem that accompanies the development of deep learning networks.
YOLO and K-Means Based 3D Object Detection Method on Image and Point Cloud
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.
3D Object Detection Method Based on YOLO and K-Means for Image and Point Clouds
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.
SOIC: Semantic Online Initialization and Calibration for LiDAR and Camera
This paper presents a novel semantic-based online extrinsic calibration approach, SOIC (so, I see), for Light Detection and Ranging (LiDAR) and camera sensors.
Weighted boxes fusion: Ensembling boxes from different object detection models
In this work, we present a novel method for combining predictions of object detection models: weighted boxes fusion.
Acoustic scene analysis with multi-head attention networks
Acoustic Scene Classification (ASC) is a challenging task, as a single scene may involve multiple events that contain complex sound patterns.
Neural Style Transfer for Point Clouds
How can we edit or transform the geometric or color property of a point cloud?
Weakly Supervised Silhouette-based Semantic Scene Change Detection
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.
Dense Optical Flow based Change Detection Network Robust to Difference of Camera Viewpoints
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.
Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets
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
Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard
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.
