Search Results for author:
Found 10 papers, 1 papers with code
PP-MobileSeg: Explore the Fast and Accurate Semantic Segmentation Model on Mobile Devices
To address this issue, we propose PP-MobileSeg, a semantic segmentation model that achieves state-of-the-art performance on mobile devices.
LVIS Challenge Track Technical Report 1st Place Solution: Distribution Balanced and Boundary Refinement for Large Vocabulary Instance Segmentation
Our method focuses on the problem in following two aspects: the long-tail distribution and the segmentation quality of mask and boundary.
Ranked #3 on
Instance Segmentation
on LVIS v1.0 val
Multi-Target Domain Adaptation via Unsupervised Domain Classification for Weather Invariant Object Detection
We propose a novel unsupervised domain classification method which can be used to generalize single-target domain adaptation methods to multi-target domains, and design a weather-invariant object detector training framework based on it.
Application of Pre-training Models in Named Entity Recognition
Then, we apply these pre-training models to a NER task by fine-tuning, and compare the effects of the different model architecture and pre-training tasks on the NER task.
Movable-Object-Aware Visual SLAM via Weakly Supervised Semantic Segmentation
Moving objects can greatly jeopardize the performance of a visual simultaneous localization and mapping (vSLAM) system which relies on the static-world assumption.
Fully Using Classifiers for Weakly Supervised Semantic Segmentation with Modified Cues
This paper proposes a novel weakly-supervised semantic segmentation method using image-level label only.
Hallucinating very low-resolution and obscured face images
The inpainting network restores the low-resolution(LR) obscured face images.
Semi-Semantic Line-Cluster Assisted Monocular SLAM for Indoor Environments
In the back end, we optimize the map imposing the constraint that the line segments of the same cluster should be the same.
Point-cloud-based place recognition using CNN feature extraction
This paper proposes a novel point-cloud-based place recognition system that adopts a deep learning approach for feature extraction.
Fine-Grained Categorization via CNN-Based Automatic Extraction and Integration of Object-Level and Part-Level Features
Fine-grained categorization can benefit from part-based features which reveal subtle visual differences between object categories.
