Search Results for author:
Found 14 papers, 7 papers with code
UDiffText: A Unified Framework for High-quality Text Synthesis in Arbitrary Images via Character-aware Diffusion Models
Text-to-Image (T2I) generation methods based on diffusion model have garnered significant attention in the last few years.
Segment Anything Model-guided Collaborative Learning Network for Scribble-supervised Polyp Segmentation
Specifically, we first propose a Cross-level Enhancement and Aggregation Network (CEA-Net) for weakly-supervised polyp segmentation.
Human from Blur: Human Pose Tracking from Blurry Images
The key idea is to tackle the inverse problem of image deblurring by modeling the forward problem with a 3D human model, a texture map, and a sequence of poses to describe human motion.
VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantum
Therefore, based on VQNet 1. 0, we further propose VQNet 2. 0, a new generation of unified classical and quantum machine learning framework that supports hybrid optimization.
A Near Sensor Edge Computing System for Point Cloud Semantic Segmentation
In this system, a FPGA-based deep learning accelerator core (DPU) is placed next to the LiDAR sensor, to perform point cloud pre-processing and segmentation neural network.
Automatic Expert Selection for Multi-Scenario and Multi-Task Search
At each multi-scenario/multi-task layer, a novel expert selection algorithm is proposed to automatically identify scenario-/task-specific and shared experts for each input.
A Divide-and-Merge Point Cloud Clustering Algorithm for LiDAR Panoptic Segmentation
The proposed algorithm is implemented with C++ and wrapped as a python function.
FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation Decoding
In this paper, we propose a new projection-based LiDAR semantic segmentation pipeline that consists of a novel network structure and an efficient post-processing step.
LIDAR Semantic Segmentation
Robust 3D Semantic Segmentation
+1
A Technical Survey and Evaluation of Traditional Point Cloud Clustering Methods for LiDAR Panoptic Segmentation
To our best knowledge, we are the first to attempt the point cloud panoptic segmentation with clustering algorithms.
Enabling 3D Object Detection with a Low-Resolution LiDAR
Light Detection And Ranging (LiDAR) has been widely used in autonomous vehicles for perception and localization.
Deep Lucas-Kanade Homography for Multimodal Image Alignment
With those properties, directly updating the Lucas-Kanade algorithm on our feature maps will precisely align image pairs with large appearance changes.
A Surface Geometry Model for LiDAR Depth Completion
Therefore, it is assumed those pixels share the same surface with the nearest LiDAR point, and their respective depth can be estimated as the nearest LiDAR depth value plus a residual error.
DepthNet: Real-Time LiDAR Point Cloud Depth Completion for Autonomous Vehicles
In this paper, a light-weight network is proposed for the task of LiDAR point cloud depth completion.
A CNN Accelerator on FPGA Using Depthwise Separable Convolution
The state-of-the-art CNNs, such as MobileNetV2 and Xception, adopt depthwise separable convolution to replace the standard convolution for embedded platforms.
