The proposed head pose estimation loss function is defined as follows: Dec 12, 2023 · Real-time multi-person pose estimation presents significant challenges in balancing speed and precision. Additionally, to quantify disparities between the actual and estimated head poses, we employed expected regression. However, in challenging imaging conditions such as on low-resolution images or when the image is corrupted by imaging artifacts, current systems degrade considerably in accuracy. Pose Estimation plays a crucial role in computer vision, encompassing a wide range of important applications. 6m; Classifed into 2D and 3D Pose Estimation 2D Pose Estimation; Estimate a 2D pose (x,y) coordinates for each joint in pixel space from a RGB image; 3D Pose Estimation Sep 1, 2021 · 2D human pose estimation had been widely studied before. 3); later we introduce the balanced loss weighting strategy (Section 3. c * is the ground truth part affinity fields, S. In recent years, many new advances have been made in Mar 9, 2022 · This is the bottleneck of loss computation in training neural networks based on Bingham representation. Contribute a large scale RGB-D video dataset (YCB-Video) for 6D object pose estimation, where we provide 6D pose annotations for 21 YCB objects. Feb 16, 2023 · Action recognition using pose estimation is a computer vision task that involves identifying and classifying human actions based on analyzing the poses of the human body. This paper proposes a deep learning based approach for pose estimation with point clouds of textureless objects. This task is used in many applications, such as sports analysis and surveillance systems. In our method, the output dis-tribution is learnable, which resulting in a learnable loss function. At present, after obtaining the RGB and depth modality information, most methods directly concatenate them without considering information interactions. We showed that our method alleviates the need of re-parameterising regression parameters, which addresses the domain shift problem of deep learning applications. Weighted correspondences are passed through SVD to find fundamental matrix F, which is further decomposed into poses. Mar 14, 2024 · Moreover, joint object detection and pose estimation models are better suited to leverage the co-dependent nature of the tasks for improving the accuracy of both tasks. Our Jul 14, 2022 · As an important task in computer vision, head pose estimation has been widely applied in both academia and industry. We then define our loss based on the diagonal covariance elements, which entails considering the final Nov 13, 2023 · Human Pose Estimation (HPE) is the task that aims to predict the location of human joints from images and videos. both arm up or left leg bent). It captures both short-term and long-term motion information to Feb 7, 2024 · Recently, 6DoF object pose estimation has become increasingly important for a broad range of applications in the fields of virtual reality, augmented reality, autonomous driving, and robotic operations. 4); at last, we demonstrate the elaborate network structure we utilize for better performance Implementation of various human pose estimation models in pytorch on multiple datasets (MPII & COCO) along with pretrained models - Pytorch-Human-Pose-Estimation/losses. A keypoint’s location is modeled as a one Jul 14, 2022 · As an important task in computer vision, head pose estimation has been widely applied in both academia and industry. 1, although this may not be Oct 21, 2020 · The contributions of this paper are as follows: (1) we introduce a multitask network to estimate the 6D object pose, the symmetry axis, and the key points at the same time; (2) we propose a multiscale feature extraction module to fuse the features from the color image and the depth image; (3) we devise an optimization function to refine the Nov 2, 2023 · The loss function of previous self-supervised methods is mainly based on a photometric error, which is indirectly computed from synthesized images using depth and pose estimates. In other words, head pose estimation is vulnerable to changes in the background scene around the target face, as shown in Fig. (3) To examine the influence of the range of angle distributions on the proposed model, a test is conducted on three public benchmark datasets, demonstrating that our In this paper, we address the problem of 3D object instance recognition and pose estimation of localized objects in cluttered environments using convolutional neural networks. We design a new graph convolutional network architecture, U-shaped GCN (UGCN). While quaternion is a common choice for rotation representation of 6D pose, it cannot represent an Oct 30, 2023 · We use multiple loss functions to constrain the depth estimation for non-textured regions. In view of the difficulty of obtaining 3D ground truth labels for a dataset of 3D pose estimation techniques, we take 2D images as the research object in this paper, and propose a self-supervised 3D pose estimation model called Pose dress this, we derive a loss function that exploits the ground truth pose before solving the PnP problem. , images, videos, or signals). Our findings emphasize the This work proposes a fast-computable and easy-to-implement loss function for Bingham distribution and shows not only to examine the parametrization of Bingham Distribution but also an application based on the loss function. May 1, 2024 · After YOLOv8 introduced pose estimation in the framework in the second half of 2023, the framework now supports up to four tasks including classification, object detection, instance segmentation, and pose estimation. To address these issues, a new 6D pose estimation algorithm is proposed based on improvements made to the latest object detection algorithm, Yolov7. While recent research primarily aims at enhancing estimated pose performance, it is important to acknowledge the challenges encountered when evaluating these estimations against ground truth pose data. • Jul 5, 2023 · Head pose estimation is an important technology for analyzing human behavior and has been widely researched and applied in areas such as human–computer interaction and fatigue detection. 1 Introduction 3D human hand pose estimation is a long-standing problem in computer vision, Feb 16, 2024 · The evolution of 3D human pose estimation techniques has seen substantial progress over the past few decades, with notable advancements in accuracy and applications. Before the rise of deep learning, human pose estimation was mainly Head pose estimation has attracted immense research interest recently, as its inherent information significantly improves the performance of face-related applications such as face alignment and face recognition. Take home message Root-relative loss has formed the basis of 3D human pose estimation for many years. To leverage the transitive structure characteristics for human pose estimation, we explore the part descriptor that qualitatively describe the structure consistency on various appearance. To solve this problem, we propose a new method to fuse RGB and Nov 12, 2023 · Calculate the keypoints loss for the model. g. The keypoints loss is based on the difference between the predicted keypoints and ground truth keypoints. As an important task in computer vision, head pose estimation has been widely applied in both academia and industry. In existing 6D pose estimation methods, there is often a high requirement for the precision of 3D models or UV textures of objects. We augmented our proposed deep regression network with a Apr 15, 2022 · Human pose estimation aims at localizing human anatomical keypoints or body parts in the input data (e. This novel multi-pose approach produces multiple weighted pose estimates to avoid getting stuck in local minima. Source: Image by author. Existing learning-based pose estimation systems utilize a voting strategy to estimate the feature points in a Jul 1, 2022 · A THESL-Net (tiered head pose estimation with self-adjustment loss network) model is proposed, gaining a greater freedom during angle estimation and outperforms the state-of-the-art approaches. Euler angles wrap around at 2π radians, having multiple values representing the same angle. As such, we propose a fast-computable and easy-to-implement loss function for Bingham distribution. We have discovered in our experiments that bounding box margin has a large impact on the final accuracy of head pose estimation. e. A wide variety of solutions have been proposed to tackle the problem. • Results: The experimental results show that the representation method, the multi-loss functions, and the chosen backbone contribute to improved accuracy. Introduce ShapeMatch-Loss, a new training loss function for pose estimation of symmetric objects. However, traditional head pose estimation networks suffer from the problem of easily losing spatial structure information, particularly in complex scenarios where occlusions and multiple object detections of a detected face, and the other is choice of loss functions. We propose a new loss function, called motion loss, for supervising models for monocular 3D Human pose estimation from videos. Figure 1. py at master · Naman-ntc/Pytorch-Human-Pose-Estimation for pose estimation, where the PointNet-like network predicts weights for all correspondences. The RCE loss generalizes the ordinary cross-entropy loss from the binary supervision to a continuous range, thus the training of pose estimation network is able to benefit from the sigmoid function. To achieve it, hypergraph-based manifold regularization was applied. Deep learning techniques allow learning feature representations directly Sep 26, 2023 · The 6D pose estimation using RGBD images plays a pivotal role in robotics applications. This function calculates the keypoints loss and keypoints object loss for a given batch. With the progress of technology and the increase of application demanded, 3D human pose estimation had accumulated a lot of related results. 知乎专栏提供一个平台，让用户自由表达观点和分享写作内容。 Jul 30, 2023 · Although the gain from our heatmap weighting loss function is not significant now, it provides a new optimization idea for human pose estimation. PREPRINT VERSION. Today, the majority of self-driving car accidents are caused by “robotic” driving, where the self-driving vehicle conducts an allowed but unexpected stop, and a human driver crashes into the self-driving car. In computing motion loss, a simple yet effective representation for keypoint motion, called pairwise motion encoding, is introduced. However, this point-to-point loss treats every keypoint separately and ignores internal connection information of the human body. [1], we propose a method that introduces the dynamic margin in the manifold learning triplet loss function. Specifically, we linearize the PnP solver around the ground-truth pose and compute the covariance of the resulting pose distribution. Oct 30, 2023 · We use multiple loss functions to constrain the depth estimation for non-textured regions. A common methodology in two-stage approaches to 3D human pose estimation is to create a comprehensive basis of 3D poses. [19] propose histogram loss, which use histogram (i. Nov 22, 2022 · The proposed WPL-based loss function is based on the 3D coordinates produced by Equations (1) and (2) and is combined with other loss functions to train A2J. The key to estimate object poses is matching feature points in the captured image with predefined ones of the 3D model of the object. Because of the non-differentiable nature of common PnP solvers, these methods are supervised via the individual correspondences. Apr 6, 2020 · Mask R-CNN Network Overview & Loss Function 3. This Pose model offers an excellent balance between latency and accuracy. - "An Improved Tiered Head Pose Estimation Network with Self-Adjust Loss Function" er appropriate constraint should be introduced into the loss function. PDF Abstract Root-relative loss has formed the basis of 3D human pose estimation for many years. Traditional head pose estimation methods use face localization and image cropping to reduce the influence of image-independent backgrounds on the detection target, mainly by using face alignment to similarly match the target image to the image sample . However, building an efficient HPE model is difficult; many challenges, like crowded scenes and formulate a novel regressive cross-entropy (RCE) loss function for accelerating the convergence and promoting the accuracy. It works by comparing the motion pattern of the prediction against ground truth key point trajectories. Architecture: Pose V the answer was a resounding "I'd give up depth; don't take away my color!" That's a big change from just a few years ago. We introduce a pose rectified network that only estimates the rotation transformation between two adjacent frames of images for the camera pose problem, and improves the pose estimation results with the pose rectified network loss. heatmap) to represent the output distribution. READ FULL TEXT May 9, 2018 · Modern deep learning systems successfully solve many perception tasks such as object pose estimation when the input image is of high quality. While quaternion is a common choice for rotation representation of 6D pose, it Mar 12, 2023 · The accurate estimation of a 3D human pose is of great importance in many fields, such as human–computer interaction, motion recognition and automatic driving. L2-loss. As in Keypoint Detection, each Ground-Truth keypoint is one-hot-encoded, across all the K channels, in the featuremap of size [K=17, 56, 56], for a single object. In recent years, a deep learning framework has been widely used for object pose estimation. , roll, yaw, and pitch) as separate facets, which disregard To achieve this, we formulated a regression problem for 3D pose estimation based on the angle-axis representation of 3D rotations that form a Special Orthogonal Group SO(3); and used the bi-invariant geodesic distance, which is a natural Riemannian metric on SO(3) , as the loss function. There are two major approaches used to estimate head pose. Related Work Pose Estimation New architectures [22,38,39] and train- values activation function [3] significantly improve robust-ness against adversarial attacks and scale to pose estimation theoretically. Take home message Our network achieves end-to-end 6D pose estimation and is very robust to occlusions between objects. In order to handle the uncertainty, Bingham distribution is one promising solution because this has suitable features, such as a smooth representation over SO(3), in Apr 29, 2020 · We propose a new loss function, called motion loss, for the problem of monocular 3D Human pose estimation from 2D pose. Confronted with the extreme environment of space, existing spacecraft pose estimation methods are predominantly multi-stage networks with complex operations. But by the 2020 version of the Dec 4, 2023 · Crowd pose estimation with multi-instance analysis. Jun 3, 2024 · Loss functions: An . While quaternion is a common choice for rotation representation of 6D pose, it cannot represent an uncertainty of the observation. We hope it will serve as a useful tool to the community for analyzing, diagnosing, and improving pose estimation frameworks. Overview The self-supervised monocular depth estimation framework for indoor scenes de-signed in this paper is shown in Figure1. Ground truth poses (GT poses) are used to compute L2 loss between rotation and translation (pose-loss). Human Pose Estimation (HPE) is the task that aims to predict the location of human joints from images and videos. 1) and provide a overview of our proposed UV R-CNN (Section 3. In addition, the available datasets, different loss functions used in HPE, and Jun 21, 2021 · Loss Function in Keypoint-RCNN. We also show not only to examine the parametrization of Bingham distribution but also an application based on our loss function. Example of frequent models and loss functions for training and testing on the 300W-LP dataset (AP is the average pooling operation, and FC is a fully connected layer; α equals 1 or 2). In this study, we Jan 1, 2022 · Human pose estimation is one of the issues that have gained many benefits from using state-of-the-art deep learning-based models. Pose estimation refers to the acquisition of a rigid transformation of an object relative to its original model coordinate system. Mar 8, 2022 · In recent years, a deep learning framework has been widely used for object pose estimation. For example, before deep learning was mature, 3D pose estimation was mostly based on feature engineering and prediction of joints [11], [12]. To address this, we can define a piecewise loss function and use an absolute difference instead of squared one to avoid large penalties in general Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation ∗ Zhengxiong Luo 1,2,3,4 , Zhicheng Wang 1 , Yan Huang 3,4 , Liang Wang 3,4 , Tieniu Tan 3 , and Erjin Zhou 1 1 Megvii Inc 2 University of Chinese Academy of Sciences (UCAS) Most recent methods formulate the task of human pose estimation as a heatmap estimation problem, and use the overall L2 loss computed from the entire heatmap to optimize the heatmap prediction. Two-Stage Architecture The Mask R-CNN framework can easily be extended to human pose estimation. and keypoint inversion errors in human pose estimation and propose simple solutions to alleviate these issues. By extending the prediction networks and modifying the loss function, as well as performing keypoint interpolation Apr 1, 2022 · 3D human pose estimation research is affected by deep learning significantly in recent years where conventional methods [12], [13] are overtaken by deep learning methods. In this paper, we propose a lightweight network SP-YOLO based on the YOLO-Pose algorithm for real-time human pose estimation. The notion of our heatmap weighting loss can be considered as a kind of adjustment of the handcrafted two-dimensional Gaussian distribution heatmap to make it approximate the actual distribution of Aug 9, 2023 · In this section, we first review the problem formulation of densepose estimation (Section 3. Tasks and loss functions. formulate a novel regressive cross-entropy (RCE) loss function for accelerating the convergence and promoting the accuracy. 1. They work on formulating the pose Estimation problem as a DNN-based regression problem towards body joints. . Sep 26, 2018 · In this work, we have presented a novel loss function to regress poses on the Lie group SE(3), and derived the necessary gradients required for CNN training. Our loss is inspired by the VSD (Visible Surface Discrepancy) metric and relies on a differentiable renderer and CAD models. where . 2); then we introduce the novel dense points regression loss (Section 3. To this end, we propose attention-based temporal fusion for multi-object 6D pose estimation that accumulates information across multiple frames of a video sequence. Keywords: 3D hand pose estimation· graph re nement· prior pose· ad-versarial learning· bone-constrained loss. This task involves extracting the target area from the input data and subsequently determining the position and orientation of the objects. As recently as 2019, in the Benchmark for 6D Object Pose Estimation (a nearly annual competition), geometric pose estimation was still outperforming deep-learning based approaches Hodan20. function is used to calculate the loss between the predicted confidence maps and Part Affinity fields to the ground truth maps and fields. Human Pose Estimation Human posture estimation has been an active research prob-lem in the last decades. Jan 4, 2024 · The architecture of YOLOv8 allows for the adjustment of the number of layers and filters in the neural network, which can be tuned based on the complexity of the pose estimation task at hand. To this end, we reformulate the facial pose estimation as label distribution learning problem and introduce a more intuitive similarity constraint: Gaussian label distribution loss into the training for facial pose estimation to improve the accuracy. Thus, it is suitable for safety-critical embedded AI scenarios in autonomous systems, where computational resources are typically limited and fast execution is often required, but Jun 29, 2020 · What are the different techniques used to estimate head pose? Note: Many approaches in head pose estimation assume face detection as a preliminary step. Imani et al. In this way, the loss of face pose estimation was reduced. This paper introduces RTMO, a one-stage pose estimation framework that seamlessly integrates Jan 25, 2021 · The accurate estimation of three-dimensional (3D) object pose is important in a wide range of applications, such as robotics and augmented reality. First, a face is detected and only then can head pose be estimated. Bingham Loss Function and Its Application to Pose Estimation* Hiroya Sato1,2, Takuya Ikeda1, and Koichi Nishiwaki1 Abstract—In recent years, a deep learning framework has been widely used for object pose estimation. j * is the ground truth part confidence map, and Mar 4, 2022 · The RCE loss generalizes the ordinary cross-entropy loss from the binary supervision to a continuous range, thus the training of pose estimation network is able to benefit from the sigmoid function. We experiment our proposed method on multiple models and our method achieves a consistent model performance improvement. RELATED WORK A. The contribution of this paper can be summarized as follows: (1) A multi-scale local feature aggregation strategy for emphasizing the neighbor region of Oct 18, 2023 · The traditional multi-person human pose estimation method has several problems including low real-time detection effect, low recognition efficiency, and a large number of calculation parameters. By analyzing the loss formulation of the existing dense pose estimation model, we introduce a novel point regression loss function, named Dense Points loss to stable the training progress, and a new balanced loss weighting strategy to handle the multi-task losses. Nov 13, 2020 · Next, we need to define loss functions for each representation and metric to compare models performances. Existing 3D supervised models usually require large-scale 3D annotated datasets, but the amount of existing data is still insufficient to train supervised models to achieve ideal performance, especially for animal pose estimation. In this paper, we conduct an in-depth study of head pose estimation and present a multiregression loss function, an L2 regression loss combined with an ordinal regression loss, to 2D human pose estimation models by replacing their original loss function with our proposed loss function measuring the distribution difference between the predicted heatmap and the dot annotation. By doing so, the output heatmap can be inferred from the LR features without loss of spatial accuracy, while the computational cost and model size Feb 1, 2022 · The performance of multi-person pose estimation is seriously affected by scale variation. However, there remains two challenges in the field of head pose estimation: (1) even given the same task (e. Some works define a superset of loss functions We propose a novel loss function - heatmap weighting loss that explores the information from ground truth heatmaps to improve keypoints localization. For each visible Ground-Truth, channel wise Softmax (instead of sigmoid), from the final featuremap [17, 56, 56], is used to minimize the Cross Entropy Adaptive Loss Function. ACCEPTED APRIL, 2022 1 Homography-Based Loss Function for Camera Pose Regression Clementin Boittiaux´ 1 ;2 3, Ricard Marxer2, Claire Dune , Aur´elien Arnaubec 1 and Vincent Hugel3 Jul 14, 2022 · Furthermore, the reasons for the discontinuity in angle estimation are revealed, including not only labeling the dataset with quaternions or Euler angles, but also the loss function that simply Sep 26, 2022 · This paper presents a novel Convolutional Neural Network (CNN) architecture for 2D human pose estimation from RGB images that balances between high 2D human pose/skeleton estimation accuracy and rapid inference. One approach involves an intermediate step of estimating facial Sep 27, 2021 · We propose a novel multi-pose loss function to train a neural network for 6D pose estimation, using synthetic data and evaluating it on real images. This leads to illegal pose prediction, which humans cannot form in the real world. This leads to the low accuracy of 6D pose estimation in occlusion and illumination changes. the loss function is Mar 9, 2022 · In recent years, a deep learning framework has been widely used for object pose estimation. Deep Learning-based approaches have been and multiple loss functions, in detail. Nov 7, 2023 · Building upon the success of YOLO-NAS, the company has now unveiled YOLO-NAS Pose as its Pose Estimation counterpart. , tiredness detection), the existing algorithms usually consider the estimation of the three angles (i. However, there remains two challenges in the field of head pose estimation: (1) even Aug 20, 2020 · Different from current loss functions using only a single type of features, the descriptive power was improved by combining multiple image features. Additionally, the loss function may be modified to place different emphasis on keypoint detection accuracy versus bounding box localization. In this way, when the preset anchors estimate the position of a joint, the spatial relationship between joints can be considered from different viewpoints. Nov 7, 2022 · easy to collapse compared to other region-based human instance analyzing tasks. Human pose, hand and mesh estimation is a significant problem that has attracted the attention of the computer vision community for the past few decades. This will have a big impact on various fields, for example, autonomous driving, sports, healthcare, and many more. In this paper, we show that in bottom-up human pose estimation where each heatmap often contains multiple body joints, using the overall L2 loss to optimize the heatmap prediction may not be the optimal In this work, we propose a novel indoor depth estimation framework PMIndoor, which mainly consists of three modules: (a) Pose Rectified Network (PRN): we introduce a Pose Rectified Network (PRN) before the pose estimation network to remove the rotational motion between adjacent frames, which can obtain more accurate pose estimation results and Dec 12, 2023 · This function minimized a loss based on the coefficients of a 3D pose dictionary and pose-conditioned joint velocity, effectively transforming the 2D pose into a 3D pose. Extensive works have been devoted to reducing the effect by modifying convolutional network structure or loss function, but little attention has been paid to the problem in the construction of heatmaps. The farther the Most modern image-based 6D object pose estimation methods learn to predict 2D-3D correspondences, from which the pose can be obtained using a PnP solver. 2. Most recent methods formulate the task of human pose estimation as a heatmap estimation problem, and use the overall L2 loss computed from the entire heatmap to opti-mize the heatmap prediction. Jul 1, 2022 · Figure 1. However, head pose estimation in complex environments is a challenging problem. Our framework consists of four components: depth estimation network, pose estimation network, pose rectified network and multiple loss functions. It also suffers from differences in estimation difficulty between keypoints. The ProPnP algorithm transforms pose estimation problem into a predictive positional probability density problem, as the probability density function is derivable and therefore the network can be trained end-to-end. hand pose estimation from a monocular image on ve standard bench-marks. While a loss in performance is unavoidable, we would like our models to Mar 12, 2024 · Spacecraft pose estimation using computer vision has garnered increasing attention in research areas such as automation system theory, control theory, sensors and instruments, robot technology, and automation software. Pose Estimation is the search for a specific pose in space of all articulated poses; Number of keypoints varies with dataset - LSP has 14, MPII has 16, 16 are used in Human3. May 9, 2023 · Humans have an impressive ability to reliably perceive pose with semantic descriptions (e. However, these approaches limit the expres-sive power of networks while pose estimation models rely on rich global and local features to determine the position of keypoints. They present a cascade of DNN-regressors which resulted in high precision pose estimates. The keypoints object loss is a binary classification loss that classifies whether a keypoint is present or not. Dec 18, 2023 · Following the output production by the SoftMax layer, KL divergence is used to assess the similarity between the true and predicted label distributions. II. Firstly, a lightweight ghost spatial pyramid pooling-fast (GSPPF) module is Jun 14, 2020 · During training, the loss function is a weighted average of the heatmap prediction loss and the tag values loss (according to the associative embedding method small distance between tags of the same group leads to lower loss and so does higher distance between tags of different groups). While two-stage top-down methods slow down as the number of people in the image increases, existing one-stage methods often fail to simultaneously deliver high accuracy and real-time performance. Meantime, we utilize the fixed bone constraint to fully exploit structure knowledge IEEE ROBOTICS AND AUTOMATION LETTERS. Recently, several studies have embraced deep learning to enhance the performance of HPE tasks. There have been several works towards adaptive loss functions. Jul 14, 2022 · To remove the inconsistency in loss function, which is the main cause of angle estimation discontinuity problems, an easy-to-use dynamic self-adjusting loss function is developed. L. Jun 23, 2022 · DeepPose was proposed by researchers at Google for Pose Estimation in the 2014 Computer Vision and Pattern Recognition conference. While quaternion is a common choice for rotation representation of 6D pose, it cannot represent an uncertainty of the This paper presents Triangulation Residual loss (TR loss) for multiview 3D pose estimation in a data-efficient manner. Inspired by the descriptor learning approach of Wohlhart et al. Jul 25, 2018 · On this dataset, the SE(3) loss function shows drastic improvement in all image similarity metrics, which suggests that a small geodesic distance is linked to a good pose estimation. As the 2D human pose estimation results are progressively improved, researchers have also started to use detected 2D keypoints as an intermediate for 3D human pose estimation. Such a loss function is designed to map images Dec 24, 2023 · We decouple pose estimation problem into translation estimation and rotation estimation. , tiredness detection), the existing algorithms usually consider the esti … Apr 1, 2024 · Our network was constructed on a multi-loss head pose estimation with continuous representation to predict the angles using RepVGG-b1g4 as a backbone. In this paper, we show that in bottom-up human pose estimation where each heatmap often contains multiple body joints, using the overall L2 . It forms a crucial component in enabling machines to have an insightful understanding of the behaviors of humans, and has become a salient problem in computer vision and related fields. Mar 9, 2022 · This is the bottleneck of loss computation in training neural networks based on Bingham representation. yq xl na un zc br tl jk ej tt