EfficientHRNet: Efficient Scaling for Lightweight High-Resolution Multi-Person Pose Estimation

• URL: http://arxiv.org/abs/2007.08090v2
• Date: Wed, 30 Dec 2020 17:43:31 GMT
• Title: EfficientHRNet: Efficient Scaling for Lightweight High-Resolution Multi-Person Pose Estimation
• Authors: Christopher Neff, Aneri Sheth, Steven Furgurson, Hamed Tabkhi
• Abstract summary: We present EfficientHRNet, a family of lightweight multi-person human pose estimators that are able to perform in real-time on resource-constrained devices. The largest model is able to come within 4.4% accuracy of the current state-of-the-art, while having 1/3 the model size and 1/6 the power. Compared to the top real-time approach, EfficientHRNet increases accuracy by 22% while achieving similar FPS with 1/3 the power.
• Score: 2.924868086534434
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There is an increasing demand for lightweight multi-person pose estimation for many emerging smart IoT applications. However, the existing algorithms tend to have large model sizes and intense computational requirements, making them ill-suited for real-time applications and deployment on resource-constrained hardware. Lightweight and real-time approaches are exceedingly rare and come at the cost of inferior accuracy. In this paper, we present EfficientHRNet, a family of lightweight multi-person human pose estimators that are able to perform in real-time on resource-constrained devices. By unifying recent advances in model scaling with high-resolution feature representations, EfficientHRNet creates highly accurate models while reducing computation enough to achieve real-time performance. The largest model is able to come within 4.4% accuracy of the current state-of-the-art, while having 1/3 the model size and 1/6 the computation, achieving 23 FPS on Nvidia Jetson Xavier. Compared to the top real-time approach, EfficientHRNet increases accuracy by 22% while achieving similar FPS with 1/3 the power. At every level, EfficientHRNet proves to be more computationally efficient than other bottom-up 2D human pose estimation approaches, while achieving highly competitive accuracy.

Related papers

• Taming 3DGS: High-Quality Radiance Fields with Limited Resources [50.92437599516609]
  3D Gaussian Splatting (3DGS) has transformed novel-view synthesis with its fast, interpretable, and high-fidelity rendering. We tackle the challenges of training and rendering 3DGS models on a budget. We derive faster, numerically equivalent solutions for gradient computation and attribute updates.
  arXiv  Detail & Related papers  (2024-06-21T20:44:23Z)
• Pushing the Limits of Asynchronous Graph-based Object Detection with Event Cameras [62.70541164894224]
  We introduce several architecture choices which allow us to scale the depth and complexity of such models while maintaining low computation. Our method runs 3.7 times faster than a dense graph neural network, taking only 8.4 ms per forward pass.
  arXiv  Detail & Related papers  (2022-11-22T15:14:20Z)
• Lite Pose: Efficient Architecture Design for 2D Human Pose Estimation [35.765304656180355]
  We study efficient architecture design for real-time multi-person pose estimation on edge. Inspired by this finding, we design LitePose, an efficient single-branch architecture for pose estimation. We introduce two simple approaches to enhance the capacity of LitePose, including Fusion Deconv Head and Large Kernel Convs.
  arXiv  Detail & Related papers  (2022-05-03T02:08:04Z)
• HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation [0.0]
  We propose an effective and lightweight model, namely High-Resolution 6D Pose Estimation Network (HRPose) With only 33% of the model size and lower computational costs, our HRPose achieves comparable performance compared with state-of-the-art models. Numerical experiments on the widely-used benchmark LINEMOD demonstrate the superiority of our proposed HRPose against state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-04-20T12:43:39Z)
• FasterPose: A Faster Simple Baseline for Human Pose Estimation [65.8413964785972]
  We propose a design paradigm for cost-effective network with LR representation for efficient pose estimation, named FasterPose. We study the training behavior of FasterPose, and formulate a novel regressive cross-entropy (RCE) loss function for accelerating the convergence. Compared with the previously dominant network of pose estimation, our method reduces 58% of the FLOPs and simultaneously gains 1.3% improvement of accuracy.
  arXiv  Detail & Related papers  (2021-07-07T13:39:08Z)
• Effective Model Sparsification by Scheduled Grow-and-Prune Methods [73.03533268740605]
  We propose a novel scheduled grow-and-prune (GaP) methodology without pre-training the dense models. Experiments have shown that such models can match or beat the quality of highly optimized dense models at 80% sparsity on a variety of tasks.
  arXiv  Detail & Related papers  (2021-06-18T01:03:13Z)
• EfficientPose: Efficient Human Pose Estimation with Neural Architecture Search [47.30243595690131]
  We propose an efficient framework targeted at human pose estimation including two parts, the efficient backbone and the efficient head. Our smallest model has only 0.65 GFLOPs with 88.1% PCKh@0.5 on MPII and our large model has only 2 GFLOPs while its accuracy is competitive with the state-of-the-art large model.
  arXiv  Detail & Related papers  (2020-12-13T15:38:38Z)
• APQ: Joint Search for Network Architecture, Pruning and Quantization Policy [49.3037538647714]
  We present APQ for efficient deep learning inference on resource-constrained hardware. Unlike previous methods that separately search the neural architecture, pruning policy, and quantization policy, we optimize them in a joint manner. With the same accuracy, APQ reduces the latency/energy by 2x/1.3x over MobileNetV2+HAQ.
  arXiv  Detail & Related papers  (2020-06-15T16:09:17Z)
• EfficientPose: Scalable single-person pose estimation [3.325625311163864]
  We propose a novel convolutional neural network architecture, called EfficientPose, for single-person pose estimation. Our top-performing model achieves state-of-the-art accuracy on single-person MPII, with low-complexity ConvNets. Due to its low complexity and efficiency, EfficientPose enables real-world applications on edge devices by limiting the memory footprint and computational cost.
  arXiv  Detail & Related papers  (2020-04-25T16:50:46Z)
• FADNet: A Fast and Accurate Network for Disparity Estimation [18.05392578461659]
  We propose an efficient and accurate deep network for disparity estimation named FADNet. It exploits efficient 2D based correlation layers with stacked blocks to preserve fast computation. It contains multi-scale predictions so as to exploit a multi-scale weight scheduling training technique to improve the accuracy.
  arXiv  Detail & Related papers  (2020-03-24T10:27:11Z)

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.