Heatmap Regression via Randomized Rounding

• URL: http://arxiv.org/abs/2009.00225v2
• Date: Thu, 26 Aug 2021 09:34:41 GMT
• Title: Heatmap Regression via Randomized Rounding
• Authors: Baosheng Yu, Dacheng Tao
• Abstract summary: We propose a simple yet effective quantization system to address the sub-pixel localization problem. The proposed system encodes the fractional part of numerical coordinates into the ground truth heatmap using a probabilistic approach during training.
• Score: 105.75014893647538
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Heatmap regression has become the mainstream methodology for deep learning-based semantic landmark localization, including in facial landmark localization and human pose estimation. Though heatmap regression is robust to large variations in pose, illumination, and occlusion in unconstrained settings, it usually suffers from a sub-pixel localization problem. Specifically, considering that the activation point indices in heatmaps are always integers, quantization error thus appears when using heatmaps as the representation of numerical coordinates. Previous methods to overcome the sub-pixel localization problem usually rely on high-resolution heatmaps. As a result, there is always a trade-off between achieving localization accuracy and computational cost, where the computational complexity of heatmap regression depends on the heatmap resolution in a quadratic manner. In this paper, we formally analyze the quantization error of vanilla heatmap regression and propose a simple yet effective quantization system to address the sub-pixel localization problem. The proposed quantization system induced by the randomized rounding operation 1) encodes the fractional part of numerical coordinates into the ground truth heatmap using a probabilistic approach during training; and 2) decodes the predicted numerical coordinates from a set of activation points during testing. We prove that the proposed quantization system for heatmap regression is unbiased and lossless. Experimental results on popular facial landmark localization datasets (WFLW, 300W, COFW, and AFLW) and human pose estimation datasets (MPII and COCO) demonstrate the effectiveness of the proposed method for efficient and accurate semantic landmark localization. Code is available at http://github.com/baoshengyu/H3R.

Related papers

• Subpixel Heatmap Regression for Facial Landmark Localization [65.41270740933656]
  Heatmap regression approaches suffer from discretization-induced errors related to both the heatmap encoding and decoding process. We propose a new approach for the heatmap encoding and decoding process by leveraging the underlying continuous distribution. Our approach offers noticeable gains across multiple datasets setting a new state-of-the-art result in facial landmark localization.
  arXiv  Detail & Related papers  (2021-11-03T17:21:28Z)
• Is 2D Heatmap Representation Even Necessary for Human Pose Estimation? [44.313782042852246]
  We propose a textbfSimple yet promising textbfDisentangled textbfRepresentation for keypoint coordinate (emphSimDR) In detail, we propose to disentangle the representation of horizontal and vertical coordinates for keypoint location, leading to a more efficient scheme without extra upsampling and refinement.
  arXiv  Detail & Related papers  (2021-07-07T16:20:12Z)
• Graph-PCNN: Two Stage Human Pose Estimation with Graph Pose Refinement [54.29252286561449]
  We propose a two-stage graph-based and model-agnostic framework, called Graph-PCNN. In the first stage, heatmap regression network is applied to obtain a rough localization result, and a set of proposal keypoints, called guided points, are sampled. In the second stage, for each guided point, different visual feature is extracted by the localization. The relationship between guided points is explored by the graph pose refinement module to get more accurate localization results.
  arXiv  Detail & Related papers  (2020-07-21T04:59:15Z)
• Bottom-Up Human Pose Estimation by Ranking Heatmap-Guided Adaptive Keypoint Estimates [76.51095823248104]
  We present several schemes that are rarely or unthoroughly studied before for improving keypoint detection and grouping (keypoint regression) performance. First, we exploit the keypoint heatmaps for pixel-wise keypoint regression instead of separating them for improving keypoint regression. Second, we adopt a pixel-wise spatial transformer network to learn adaptive representations for handling the scale and orientation variance. Third, we present a joint shape and heatvalue scoring scheme to promote the estimated poses that are more likely to be true poses.
  arXiv  Detail & Related papers  (2020-06-28T01:14:59Z)
• A Transfer Learning approach to Heatmap Regression for Action Unit intensity estimation [50.261472059743845]
  Action Units (AUs) are geometrically-based atomic facial muscle movements. We propose a novel AU modelling problem that consists of jointly estimating their localisation and intensity. A Heatmap models whether an AU occurs or not at a given spatial location.
  arXiv  Detail & Related papers  (2020-04-14T16:51:13Z)
• Attentive One-Dimensional Heatmap Regression for Facial Landmark Detection and Tracking [73.35078496883125]
  We propose a novel attentive one-dimensional heatmap regression method for facial landmark localization. First, we predict two groups of 1D heatmaps to represent the marginal distributions of the x and y coordinates. Second, a co-attention mechanism is adopted to model the inherent spatial patterns existing in x and y coordinates. Third, based on the 1D heatmap structures, we propose a facial landmark detector capturing spatial patterns for landmark detection on an image.
  arXiv  Detail & Related papers  (2020-04-05T06:51:22Z)

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.