Search Results for author:
Found 10 papers, 2 papers with code
Masked Cross-image Encoding for Few-shot Segmentation
Few-shot segmentation (FSS) is a dense prediction task that aims to infer the pixel-wise labels of unseen classes using only a limited number of annotated images.
Ranked #24 on
Few-Shot Semantic Segmentation
on COCO-20i (5-shot)
Spatial-temporal Analysis for Automated Concrete Workability Estimation
Concrete workability measure is mostly determined based on subjective assessment of a certified assessor with visual inspections.
Self-attention on Multi-Shifted Windows for Scene Segmentation
Scene segmentation in images is a fundamental yet challenging problem in visual content understanding, which is to learn a model to assign every image pixel to a categorical label.
Horizontal and Vertical Attention in Transformers
Transformers are built upon multi-head scaled dot-product attention and positional encoding, which aim to learn the feature representations and token dependencies.
Unsupervised Learning on 3D Point Clouds by Clustering and Contrasting
This paper proposes a general unsupervised approach, named \textbf{ConClu}, to perform the learning of point-wise and global features by jointly leveraging point-level clustering and instance-level contrasting.
COTReg:Coupled Optimal Transport based Point Cloud Registration
Generating a set of high-quality correspondences or matches is one of the most critical steps in point cloud registration.
Multi-layer Feature Aggregation for Deep Scene Parsing Models
The proposed module can auto-select the intermediate visual features to correlate the spatial and semantic information.
Ranked #47 on
Semantic Segmentation
on NYU Depth v2
Distribution-aware Margin Calibration for Medical Image Segmentation
In this paper, we present a novel data-distribution-aware margin calibration method for a better generalization of the mIoU over the whole data-distribution, underpinned by a rigid lower bound.
Parameter Efficient Deep Neural Networks with Bilinear Projections
Recent research on deep neural networks (DNNs) has primarily focused on improving the model accuracy.
Dual Attention on Pyramid Feature Maps for Image Captioning
In this paper, we propose to apply dual attention on pyramid image feature maps to fully explore the visual-semantic correlations and improve the quality of generated sentences.
