Search Results for author:
Found 7 papers, 6 papers with code
Virtual Classification: Modulating Domain-Specific Knowledge for Multidomain Crowd Counting
Multidomain crowd counting aims to learn a general model for multiple diverse datasets.
Regressor-Segmenter Mutual Prompt Learning for Crowd Counting
In this study, we propose mutual prompt learning (mPrompt), which leverages a regressor and a segmenter as guidance for each other, solving bias and inaccuracy caused by annotation variance while distinguishing foreground from background.
An Improved Normed-Deformable Convolution for Crowd Counting
However, the heads are not uniformly covered by the sampling points in the deformable convolution, resulting in loss of head information.
Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting
In crowd counting, due to the problem of laborious labelling, it is perceived intractability of collecting a new large-scale dataset which has plentiful images with large diversity in density, scene, etc.
Crowd Counting via Perspective-Guided Fractional-Dilation Convolution
One of the main issues in this task is how to handle the dramatic scale variations of pedestrians caused by the perspective effect.
Perspective-Guided Convolution Networks for Crowd Counting
In this paper, we propose a novel perspective-guided convolution (PGC) for convolutional neural network (CNN) based crowd counting (i. e. PGCNet), which aims to overcome the dramatic intra-scene scale variations of people due to the perspective effect.
Shift-Net: Image Inpainting via Deep Feature Rearrangement
To this end, the encoder feature of the known region is shifted to serve as an estimation of the missing parts.
