Progressive Multi-resolution Loss for Crowd Counting

• URL: http://arxiv.org/abs/2212.04127v1
• Date: Thu, 8 Dec 2022 07:55:13 GMT
• Title: Progressive Multi-resolution Loss for Crowd Counting
• Authors: Ziheng Yan, Yuankai Qi, Guorong Li, Xinyan Liu, Weigang Zhang, Qingming Huang, Ming-Hsuan Yang
• Abstract summary: We propose to predict the density map at one resolution but measure the density map at multiple resolutions. We mathematically prove it is superior to a single-resolution L2 loss.
• Score: 126.01887803981619
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Crowd counting is usually handled in a density map regression fashion, which is supervised via a L2 loss between the predicted density map and ground truth. To effectively regulate models, various improved L2 loss functions have been proposed to find a better correspondence between predicted density and annotation positions. In this paper, we propose to predict the density map at one resolution but measure the density map at multiple resolutions. By maximizing the posterior probability in such a setting, we obtain a log-formed multi-resolution L2-difference loss, where the traditional single-resolution L2 loss is its particular case. We mathematically prove it is superior to a single-resolution L2 loss. Without bells and whistles, the proposed loss substantially improves several baselines and performs favorably compared to state-of-the-art methods on four crowd counting datasets, ShanghaiTech A & B, UCF-QNRF, and JHU-Crowd++.

Related papers

• HDNet: A Hierarchically Decoupled Network for Crowd Counting [11.530565995318696]
  We propose a Hierarchically Decoupled Network (HDNet) to solve the above two problems within a unified framework. HDNet achieves state-of-the-art performance on several popular counting benchmarks.
  arXiv  Detail & Related papers  (2022-12-12T06:01:26Z)
• Redesigning Multi-Scale Neural Network for Crowd Counting [68.674652984003]
  We introduce a hierarchical mixture of density experts, which hierarchically merges multi-scale density maps for crowd counting. Within the hierarchical structure, an expert competition and collaboration scheme is presented to encourage contributions from all scales. Experiments show that our method achieves the state-of-the-art performance on five public datasets.
  arXiv  Detail & Related papers  (2022-08-04T21:49:29Z)
• Rethinking Spatial Invariance of Convolutional Networks for Object Counting [119.83017534355842]
  We try to use locally connected Gaussian kernels to replace the original convolution filter to estimate the spatial position in the density map. Inspired by previous work, we propose a low-rank approximation accompanied with translation invariance to favorably implement the approximation of massive Gaussian convolution. Our methods significantly outperform other state-of-the-art methods and achieve promising learning of the spatial position of objects.
  arXiv  Detail & Related papers  (2022-06-10T17:51:25Z)
• Unpaired Image Super-Resolution with Optimal Transport Maps [128.1189695209663]
  Real-world image super-resolution (SR) tasks often do not have paired datasets limiting the application of supervised techniques. We propose an algorithm for unpaired SR which learns an unbiased OT map for the perceptual transport cost. Our algorithm provides nearly state-of-the-art performance on the large-scale unpaired AIM-19 dataset.
  arXiv  Detail & Related papers  (2022-02-02T16:21:20Z)
• Gradient Variance Loss for Structure-Enhanced Image Super-Resolution [16.971608518924597]
  We introduce a structure-enhancing loss function, coined Gradient Variance (GV) loss, and generate textures with perceptual-pleasant details. Experimental results show that the GV loss can significantly improve both Structure Similarity (SSIM) and peak signal-to-noise ratio (PSNR) performance of existing image super-resolution (SR) deep learning models.
  arXiv  Detail & Related papers  (2022-02-02T12:31:05Z)
• Cascaded Residual Density Network for Crowd Counting [63.714719914701014]
  We propose a novel Cascaded Residual Density Network (CRDNet) in a coarse-to-fine approach to generate the high-quality density map for crowd counting more accurately. A novel additional local count loss is presented to refine the accuracy of crowd counting.
  arXiv  Detail & Related papers  (2021-07-29T03:07:11Z)
• Direct Measure Matching for Crowd Counting [59.66286603624411]
  We propose a new measure-based counting approach to regress the predicted density maps to the scattered point-annotated ground truth directly. In this paper, we derive a semi-balanced form of Sinkhorn divergence, based on which a Sinkhorn counting loss is designed for measure matching.
  arXiv  Detail & Related papers  (2021-07-04T06:37:33Z)
• Dual-Resolution Correspondence Networks [20.004691262722265]
  We introduce Dual-Resolution Correspondence Networks (DualRC-Net), to obtain pixel-wise correspondences in a coarse-to-fine manner. We evaluate our method on large-scale public benchmarks including HPatches, InLoc, and Aachen Day-Night.
  arXiv  Detail & Related papers  (2020-06-16T00:42:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.