Search Results for author:
Found 13 papers, 10 papers with code
DVIS-DAQ: Improving Video Segmentation via Dynamic Anchor Queries
Modern video segmentation methods adopt object queries to perform inter-frame association and demonstrate satisfactory performance in tracking continuously appearing objects despite large-scale motion and transient occlusion.
Point Cloud Mamba: Point Cloud Learning via State Space Model
To enable more effective processing of 3-D point cloud data by Mamba, we propose a novel Consistent Traverse Serialization to convert point clouds into 1-D point sequences while ensuring that neighboring points in the sequence are also spatially adjacent.
DVIS++: Improved Decoupled Framework for Universal Video Segmentation
We present the \textbf{D}ecoupled \textbf{VI}deo \textbf{S}egmentation (DVIS) framework, a novel approach for the challenging task of universal video segmentation, including video instance segmentation (VIS), video semantic segmentation (VSS), and video panoptic segmentation (VPS).
Ranked #1 on
Video Semantic Segmentation
on VSPW
Long-Range Correlation Supervision for Land-Cover Classification from Remote Sensing Images
In this paper, we propose a novel supervised long-range correlation method for land-cover classification, called the supervised long-range correlation network (SLCNet), which is shown to be superior to the currently used unsupervised strategies.
1st Place Solution for the 5th LSVOS Challenge: Video Instance Segmentation
Video instance segmentation is a challenging task that serves as the cornerstone of numerous downstream applications, including video editing and autonomous driving.
1st Place Solution for PVUW Challenge 2023: Video Panoptic Segmentation
In this report, we successfully validated the effectiveness of the decoupling strategy in video panoptic segmentation.
DVIS: Decoupled Video Instance Segmentation Framework
The efficacy of the decoupling strategy relies on two crucial elements: 1) attaining precise long-term alignment outcomes via frame-by-frame association during tracking, and 2) the effective utilization of temporal information predicated on the aforementioned accurate alignment outcomes during refinement.
Ranked #3 on
Video Panoptic Segmentation
on VIPSeg
BuildMapper: A Fully Learnable Framework for Vectorized Building Contour Extraction
Deep learning based methods have significantly boosted the study of automatic building extraction from remote sensing images.
A diverse large-scale building dataset and a novel plug-and-play domain generalization method for building extraction
In addition, to further improve the generalization ability of a building extraction model, we propose a domain generalization method named batch style mixing (BSM), which can be embedded as an efficient plug-and-play module in the frond-end of a building extraction model, providing the model with a progressively larger data distribution to learn data-invariant knowledge.
E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation
In this paper, we introduce a novel contour-based method, named E2EC, for high-quality instance segmentation.
Ranked #97 on
Instance Segmentation
on COCO test-dev
Rational Polynomial Camera Model Warping for Deep Learning Based Satellite Multi-View Stereo Matching
Based on the RPC warping, we propose the deep learning based satellite MVS (SatMVS) framework for large-scale and wide depth range Earth surface reconstruction.
Scribble-based Weakly Supervised Deep Learning for Road Surface Extraction from Remote Sensing Images
Road surface extraction from remote sensing images using deep learning methods has achieved good performance, while most of the existing methods are based on fully supervised learning, which requires a large amount of training data with laborious per-pixel annotation.
A Novel Recurrent Encoder-Decoder Structure for Large-Scale Multi-view Stereo Reconstruction from An Open Aerial Dataset
In this paper, we present a synthetic aerial dataset, called the WHU dataset, we created for MVS tasks, which, to our knowledge, is the first large-scale multi-view aerial dataset.
