An Improved Normed-Deformable Convolution for Crowd Counting

• URL: http://arxiv.org/abs/2206.08084v1
• Date: Thu, 16 Jun 2022 10:56:26 GMT
• Title: An Improved Normed-Deformable Convolution for Crowd Counting
• Authors: Xin Zhong, Zhaoyi Yan, Jing Qin, Wangmeng Zuo and Weigang Lu
• Abstract summary: Deformable convolution is proposed to exploit the scale-adaptive capabilities for CNN features in the heads. An improved Normed-Deformable Convolution (textiti.e.,NDConv) is proposed in this paper. Our method outperforms state-of-the-art methods on ShanghaiTech A, ShanghaiTech B, UCF_QNRF, and UCF_CC_50 dataset.
• Score: 70.02434289611566
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, crowd counting has become an important issue in computer vision. In most methods, the density maps are generated by convolving with a Gaussian kernel from the ground-truth dot maps which are marked around the center of human heads. Due to the fixed geometric structures in CNNs and indistinct head-scale information, the head features are obtained incompletely. Deformable convolution is proposed to exploit the scale-adaptive capabilities for CNN features in the heads. By learning the coordinate offsets of the sampling points, it is tractable to improve the ability to adjust the receptive field. However, the heads are not uniformly covered by the sampling points in the deformable convolution, resulting in loss of head information. To handle the non-uniformed sampling, an improved Normed-Deformable Convolution (\textit{i.e.,}NDConv) implemented by Normed-Deformable loss (\textit{i.e.,}NDloss) is proposed in this paper. The offsets of the sampling points which are constrained by NDloss tend to be more even. Then, the features in the heads are obtained more completely, leading to better performance. Especially, the proposed NDConv is a light-weight module which shares similar computation burden with Deformable Convolution. In the extensive experiments, our method outperforms state-of-the-art methods on ShanghaiTech A, ShanghaiTech B, UCF\_QNRF, and UCF\_CC\_50 dataset, achieving 61.4, 7.8, 91.2, and 167.2 MAE, respectively. The code is available at https://github.com/bingshuangzhuzi/NDConv

Related papers

• Scalable Graph Compressed Convolutions [68.85227170390864]
  We propose a differentiable method that applies permutations to calibrate input graphs for Euclidean convolution. Based on the graph calibration, we propose the Compressed Convolution Network (CoCN) for hierarchical graph representation learning.
  arXiv  Detail & Related papers  (2024-07-26T03:14:13Z)
• LDConv: Linear deformable convolution for improving convolutional neural networks [18.814748446649627]
  Linear Deformable Convolution (LDConv) is a plug-and-play convolutional operation that can replace the convolutional operation to improve network performance. LDConv corrects the growth trend of the number of parameters for standard convolution and Deformable Conv to a linear growth.
  arXiv  Detail & Related papers  (2023-11-20T07:54:54Z)
• Revisiting Deformable Convolution for Depth Completion [40.45231083385708]
  Depth completion aims to generate high-quality dense depth maps from sparse depth maps. Previous work usually employs RGB images as guidance, and introduces iterative spatial propagation to refine estimated coarse depth maps. We propose an effective architecture that leverages deformable kernel convolution as a single-pass refinement module.
  arXiv  Detail & Related papers  (2023-08-03T17:59:06Z)
• Adaptive Self-supervision Algorithms for Physics-informed Neural Networks [59.822151945132525]
  Physics-informed neural networks (PINNs) incorporate physical knowledge from the problem domain as a soft constraint on the loss function. We study the impact of the location of the collocation points on the trainability of these models. We propose a novel adaptive collocation scheme which progressively allocates more collocation points to areas where the model is making higher errors.
  arXiv  Detail & Related papers  (2022-07-08T18:17:06Z)
• Focal Sparse Convolutional Networks for 3D Object Detection [121.45950754511021]
  We introduce two new modules to enhance the capability of Sparse CNNs. They are focal sparse convolution (Focals Conv) and its multi-modal variant of focal sparse convolution with fusion. For the first time, we show that spatially learnable sparsity in sparse convolution is essential for sophisticated 3D object detection.
  arXiv  Detail & Related papers  (2022-04-26T17:34:10Z)
• Dilated convolution with learnable spacings [6.6389732792316005]
  CNNs need receptive fields (RF) to compete with visual transformers. RFs can simply be enlarged by increasing the convolution kernel sizes. The number of trainable parameters, which scales quadratically with the kernel's size in the 2D case, rapidly becomes prohibitive. This paper presents a new method to increase the RF size without increasing the number of parameters.
  arXiv  Detail & Related papers  (2021-12-07T14:54:24Z)
• Adaptive Graph Convolution for Point Cloud Analysis [25.175406613705274]
  We propose Adaptive Graph Convolution (AdaptConv) which generates adaptive kernels for points according to their dynamically learned features. Our method outperforms state-of-the-art point cloud classification and segmentation approaches on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-08-18T08:38:52Z)
• Content-Aware Convolutional Neural Networks [98.97634685964819]
  Convolutional Neural Networks (CNNs) have achieved great success due to the powerful feature learning ability of convolution layers. We propose a Content-aware Convolution (CAC) that automatically detects the smooth windows and applies a 1x1 convolutional kernel to replace the original large kernel.
  arXiv  Detail & Related papers  (2021-06-30T03:54:35Z)
• Decoupled Dynamic Filter Networks [85.38058820176047]
  We propose the Decoupled Dynamic Filter (DDF) that can simultaneously tackle both of these shortcomings. Inspired by recent advances in attention, DDF decouples a depth-wise dynamic filter into spatial and channel dynamic filters. We observe a significant boost in performance when replacing standard convolution with DDF in classification networks.
  arXiv  Detail & Related papers  (2021-04-29T04:55:33Z)

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.