Proceedings ArticleDOI
Binary Dense Predictors for Human Pose Estimation Based on Dynamic Thresholds and Filtering
Xingrun Xing,Yalong Jiang,Baochang Zhang,Wenrui Ding,Yangguang Li,Hongguang Li,Huan Peng +6 more
- pp 1705-1709
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TLDR
This work proposes two approaches to conduct image-aware and pixel-aware dynamic binarization in a model for human pose estimation and improves 5.2% and 3.6% mAP on the COCO test-dev benchmark for ResNet-18/34 architectures respectively.Abstract:
Binary neural networks (BNNs) contribute a lot to the efficiency of image classification models. However, in dense predication tasks such as human pose estimation, predictions in different locations are coupled and rely on the extraction of features across entire images. As a result, more robust and adaptive binarization is required to bridge the performance gap between binarized and full precision models. We propose two approaches to conduct image-aware and pixel-aware dynamic binarization in a model for human pose estimation. Firstly, a simplified dynamic thresholding is leveraged in the backbone to determine unique binarization thresholds for each image. Secondly, in the decoder, we decouple binarization for each pixel according to the activations surrounding the pixel. Dynamic filtering modules are proposed to determine a different binarization strategy for each pixel. Compared with the strong baselines, the proposed framework improves 5.2% and 3.6% mAP on the COCO test-dev benchmark for ResNet-18/34 architectures respectively.read more
Citations
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Proceedings ArticleDOI
BEBERT: Efficient and robust binary ensemble BERT
TL;DR: Li et al. as mentioned in this paper proposed an efficient and robust binary ensemble BERT (BEBERT) to bridge the accuracy gap, which employs ensemble techniques on binary BERTs, yielding BEBERT, which achieves superior accuracy while retaining computational efficiency.
Proceedings ArticleDOI
Bebert: Efficient And Robust Binary Ensemble Bert
TL;DR: Li et al. as mentioned in this paper proposed an efficient and robust binary ensemble BERT (BEBERT) to bridge the accuracy gap, which employs ensemble techniques on binary BERTs, yielding BEBERT, which achieves superior accuracy while retaining computational efficiency.
References
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Book ChapterDOI
Stacked Hourglass Networks for Human Pose Estimation
TL;DR: This work introduces a novel convolutional network architecture for the task of human pose estimation that is described as a “stacked hourglass” network based on the successive steps of pooling and upsampling that are done to produce a final set of predictions.
Journal ArticleDOI
OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields
TL;DR: OpenPose as mentioned in this paper uses Part Affinity Fields (PAFs) to learn to associate body parts with individuals in the image, which achieves high accuracy and real-time performance.
Proceedings ArticleDOI
2D Human Pose Estimation: New Benchmark and State of the Art Analysis
TL;DR: A novel benchmark "MPII Human Pose" is introduced that makes a significant advance in terms of diversity and difficulty, a contribution that is required for future developments in human body models.
Proceedings ArticleDOI
Towards Accurate Multi-person Pose Estimation in the Wild
George Papandreou,Tyler Zhu,Nori Kanazawa,Alexander Toshev,Jonathan Tompson,Chris Bregler,Kevin Murphy +6 more
TL;DR: In this article, a top-down approach consisting of two stages is proposed for multi-person detection and 2-D pose estimation that achieves state-of-the-art results on the challenging COCO keypoints task.
Book ChapterDOI
Bi-Real Net: Enhancing the Performance of 1-Bit CNNs with Improved Representational Capability and Advanced Training Algorithm
TL;DR: In this paper, the authors proposed a Bi-Real Network (Bi-Net) which connects the real activations (after the 1-bit convolution and/or batchNorm layer, before the sign function) to activations of the consecutive block, through an identity shortcut.