Search Results for author:
Found 11 papers, 5 papers with code
Dual-disentangled Deep Multiple Clustering
In the E-step, the disentanglement learning module employs coarse-grained and fine-grained disentangled representations to obtain a more diverse set of latent factors from the data.
Building Lane-Level Maps from Aerial Images
In this paper, we introduce for the first time a large-scale aerial image dataset built for lane detection, with high-quality polyline lane annotations on high-resolution images of around 80 kilometers of road.
DepthSSC: Depth-Spatial Alignment and Dynamic Voxel Resolution for Monocular 3D Semantic Scene Completion
The task of 3D semantic scene completion with monocular cameras is gaining increasing attention in the field of autonomous driving.
Geometry-Guided Ray Augmentation for Neural Surface Reconstruction with Sparse Views
In this paper, we propose a novel method for 3D scene and object reconstruction from sparse multi-view images.
DynamicBEV: Leveraging Dynamic Queries and Temporal Context for 3D Object Detection
3D object detection is crucial for applications like autonomous driving and robotics.
NDC-Scene: Boost Monocular 3D Semantic Scene Completion in Normalized Device Coordinates Space
Monocular 3D Semantic Scene Completion (SSC) has garnered significant attention in recent years due to its potential to predict complex semantics and geometry shapes from a single image, requiring no 3D inputs.
3D Semantic Scene Completion from a single 2D image
3D Semantic Scene Completion from a single RGB image
Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNN
To explore the differences between Transformers and CNNs, we employ a sparse pixel approach to contrastively analyze the distinctions between the two.
AugDMC: Data Augmentation Guided Deep Multiple Clustering
Thereafter, multiple clusterings based on different aspects of the data can be obtained.
Context-Aware Feature Learning for Noise Robust Person Search
For human-introduced noise, we propose a noise-discovery and noise-suppression training process for mislabeling robust person search.
Inverse Moment Methods for Sufficient Forecasting using High-Dimensional Predictors
Assuming that the predictors affect the response through the latent factors, we propose to first conduct factor analysis and then apply sufficient dimension reduction on the estimated factors, to derive the reduced data for subsequent forecasting.
Sufficient Forecasting Using Factor Models
Our method and theory allow the number of predictors to be larger than the number of observations.
