在视线估计任务中使用轻量模型作为骨干网络通常会导致性能显著下降，主要原因是轻量网络的特征通道数量通常较少，使得模型表达能力受限。为提升轻量模型在视线估计任务中的性能，本文提出了名为Multitask-Gaze的网络模型。该模型的主要组成部分包括单向卷积（Unidirectional Convolution，UC）、空间与通道注意力（Spatial and Channel Attention，SCA）、全局卷积模块（Global Convolution Module，GCM）以及多任务回归模块（Multi-task Regression Module，MRM）。UC不仅显著减少了参数量和浮点运算量（FLOPs），还扩展了感受野，提升了模型的长距离建模能力，进而提高模型性能；SCA可突出与视线相关的特征，抑制与视线无关的特征；GCM替代池化层，避免了信息丢失导致的性能下降；MRM提升了单个任务的精度，同时强化了任务间的关联以实现整体性能提升。实验结果表明，与当前最优方法SUGE相比，Multitask-Gaze在MPIIFaceGaze和Gaze360数据集上的性能分别提升了1.71%和2.75%，同时参数量和FLOPs分别显著降低了75.5%和86.88%。
英文摘要：
Using lightweight models as backbone networks in gaze estimation tasks often results in significant performance degradation. The main reason is that the number of feature channels in lightweight networks is usually small, which makes the model expression ability limited. In order to improve the performance of lightweight models in gaze estimation tasks, a network model named Multitask-Gaze is proposed. The main components of Multitask-Gaze include Unidirectional Convolution (UC), Spatial and Channel Attention (SCA), Global Convolution Module (GCM), and Multi-task Regression Module(MRM). UC not only significantly reduces the number of parameters and FLOPs, but also extends the receptive field and improves the long-distance modeling capability of the model, thereby improving the model performance. SCA highlights gaze-related features and suppresses gaze-irrelevant features. The GCM replaces the pooling layer and avoids the performance degradation due to information loss. MRM improves the accuracy of individual tasks and strengthens the connections between tasks for overall performance improvement. The experimental results show that compared with the State-of-the-art method SUGE, the performance of Multitask-Gaze on MPIIFaceGaze and Gaze360 datasets is improved by 1.71% and 2.75%, respectively, while the number of parameters and FLOPs are significantly reduced by 75.5% and 86.88%.
The Hong Kong University of Science and Technology（香港科技大学）
Appearance-based gaze estimation has attracted more and more attention because of its wide range of applications. The use of deep convolutional neural networks has improved the accuracy significantly. In order to improve the estimation accuracy further, we focus on extracting better features from eye images. Relatively large changes in gaze angles may result in relatively small changes in eye appearance. We argue that current architectures for gaze estimation may not be able to capture such small changes, as they apply multiple pooling layers or other downsampling layers so that the spatial re…
Estimation eye gaze direction is useful in various human-computer interaction tasks. Knowledge of gaze direction can give valuable information regarding users point of attention. Certain patterns of eye movements known as eye accessing cues are reported to be related to the cognitive processes in the human brain. We propose a real-time framework for the classification of eye gaze direction and estimation of eye accessing cues. In the first stage, the algorithm detects faces using a modified version of the Viola-Jones algorithm. A rough eye region is obtained using geometric relations and facia…
Estimation of 3D gaze is highly relevant to multiple fields, including but not limited to interactive systems, specialized human-computer interfaces, and behavioral research. Although recently deep learning methods have boosted the accuracy of appearance-based gaze estimation, there is still room for improvement in the network architectures for this particular task. Therefore we propose here a novel network architecture grounded on self-attention augmented convolutions to improve the quality of the learned features during the training of a shallower residual network. The rationale is that self…
University of California, San Diego（加利福尼亚大学圣地亚哥分校）
Driver gaze has been shown to be an excellent surrogate for driver attention in intelligent vehicles. With the recent surge of highly autonomous vehicles, driver gaze can be useful for determining the handoff time to a human driver. While there has been significant improvement in personalized driver gaze zone estimation systems, a generalized system which is invariant to different subjects, perspectives and scales is still lacking. We take a step towards this generalized system using Convolutional Neural Networks (CNNs). We finetune 4 popular CNN architectures for this task, and provide extens…
As a critical cue for understanding human intention, human gaze provides a key signal for Human-Computer Interaction(HCI) applications. Appearance-based gaze estimation, which directly regresses the gaze vector from eye images, has made great progress recently based on Convolutional Neural Networks(ConvNets) architecture and open-source large-scale gaze datasets. However, encoding model-based knowledge into CNN model to further improve the gaze estimation performance remains a topic that needs to be explored. In this paper, we propose HybridGazeNet(HGN), a unified framework that encodes the ge…
The eye fixation patterns of human observers are a fundamental indicator of the aspects of an image to which humans attend. Thus, manipulating fixation patterns to guide human attention is an exciting challenge in digital image processing. Here, we present a new model for manipulating images to change the distribution of human fixations in a controlled fashion. We use the state-of-the-art model for fixation prediction to train a convolutional neural network to transform images so that they satisfy a given fixation distribution. For network training, we carefully design a loss function to achie…
Gwangju Institute of Science and Technology（光州科学技术院）
Human-Computer Interaction(HCI) is a field that studies interactions between human users and computer systems. With the development of HCI, individuals or groups of people can use various digital technologies to achieve the optimal user experience. Human visual attention and visual intelligence are related to cognitive science, psychology, and marketing informatics, and are used in various applications of HCI. Gaze recognition is closely related to the HCI field because it is meaningful in that it can enhance understanding of basic human behavior. We can obtain reliable visual attention by the…
Universidad Peruana Cayetano Heredia（秘鲁卡耶塔诺·埃雷迪亚大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Autism is a developmental disorder that affects social interaction and communication of children. The gold standard diagnostic tools are very difficult to use and time consuming. However, diagnostic could be deduced from child gaze preferences by looking a video with social and abstract scenes. In this work, we propose an algorithm based on convolutional neural networks to predict gaze direction for a fast and effective autism diagnosis. Early results show that our algorithm achieves real-time response and robust high accuracy for prediction of gaze direction.
National University of Sciences and Technology (NUST)（巴基斯坦国立科技大学（NUST））；Riphah International University（里法国际大学）
Unavailability of large training datasets is a bottleneck that needs to be overcome to realize the true potential of deep learning in histopathology applications. Although slide digitization via whole slide imaging scanners has increased the speed of data acquisition, labeling of virtual slides requires a substantial time investment from pathologists. Eye gaze annotations have the potential to speed up the slide labeling process. This work explores the viability and timing comparisons of eye gaze labeling compared to conventional manual labeling for training object detectors. Challenges associ…
Universidad Peruana Cayetano Heredia（秘鲁卡耶塔诺埃雷迪亚大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Early diagnosis of autism spectrum disorder (ASD) is known to improve the quality of life of affected individuals. However, diagnosis is often delayed even in wealthier countries including the US, largely due to the fact that gold standard diagnostic tools such as the Autism Diagnostic Observation Schedule (ADOS) and the Autism Diagnostic Interview-Revised (ADI-R) are time consuming and require expertise to administer. This trend is even more pronounced lower resources settings due to a lack of trained experts. As a result, alternative, less technical methods that leverage the unique ways in w…
National University of Ireland Galway（爱尔兰国立大学戈尔韦分校）；Xperi Corporation（Xperi公司）
Accurate and efficient eye gaze estimation is important for emerging consumer electronic systems such as driver monitoring systems and novel user interfaces. Such systems are required to operate reliably in difficult, unconstrained environments with low power consumption and at minimal cost. In this paper a new hardware friendly, convolutional neural network model with minimal computational requirements is introduced and assessed for efficient appearance-based gaze estimation. The model is tested and compared against existing appearance based CNN approaches, achieving better eye gaze accuracy …
Unconstrained remote gaze tracking using off-the-shelf cameras is a challenging problem. Recently, promising algorithms for appearance-based gaze estimation using convolutional neural networks (CNN) have been proposed. Improving their robustness to various confounding factors including variable head pose, subject identity, illumination and image quality remain open problems. In this work, we study the effect of variable head pose on machine learning regressors trained to estimate gaze direction. We propose a novel branched CNN architecture that improves the robustness of gaze classifiers to va…
The Hong Kong University of Science and Technology（香港科技大学）
Appearance-based gaze estimation provides relatively unconstrained gaze tracking. However, subject-independent models achieve limited accuracy partly due to individual variations. To improve estimation, we propose a novel gaze decomposition method and a single gaze point calibration method, motivated by our finding that the inter-subject squared bias exceeds the intra-subject variance for a subject-independent estimator. We decompose the gaze angle into a subject-dependent bias term and a subject-independent term between the gaze angle and the bias. The subject-independent term is estimated by…
Center for Research in Computer Vision, University of Central Florida（中佛罗里达大学计算机视觉研究中心）
Inspired by the finding that vanishing point (road tangent) guides driver's gaze, in our previous work we showed that vanishing point attracts gaze during free viewing of natural scenes as well as in visual search (Borji et al., Journal of Vision 2016). We have also introduced improved saliency models using vanishing point detectors (Feng et al., WACV 2016). Here, we aim to predict vanishing points in naturalistic environments by training convolutional neural networks in an end-to-end manner over a large set of road images downloaded from Youtube with vanishing points annotated. Results demons…
Making accurate inferences about other individuals' locus of attention is essential for human social interactions and will be important for AI to effectively interact with humans. In this study, we compare how a CNN (convolutional neural network) based model of gaze and humans infer the locus of attention in images of real-world scenes with a number of individuals looking at a common location. We show that compared to the model, humans' estimates of the locus of attention are more influenced by the context of the scene, such as the presence of the attended target and the number of individuals …
This paper presents a framework for recognition of human activity from egocentric video and eye tracking data obtained from a head-mounted eye tracker. Three channels of information such as eye movement, ego-motion, and visual features are combined for the classification of activities. Image features were extracted using a pre-trained convolutional neural network. Eye and ego-motion are quantized, and the windowed histograms are used as the features. The combination of features obtains better accuracy for activity classification as compared to individual features.
With the increase in computation power and the development of new state-of-the-art deep learning algorithms, appearance-based gaze estimation is becoming more and more popular. It is believed to work well with curated laboratory data sets, however it faces several challenges when deployed in real world scenario. One such challenge is to estimate the gaze of a person about which the Deep Learning model trained for gaze estimation has no knowledge about. To analyse the performance in such scenarios we have tried to simulate a calibration mechanism. In this work we use the MPIIGaze data set. We t…
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）
Appearance-based gaze estimation is believed to work well in real-world settings, but existing datasets have been collected under controlled laboratory conditions and methods have been not evaluated across multiple datasets. In this work we study appearance-based gaze estimation in the wild. We present the MPIIGaze dataset that contains 213,659 images we collected from 15 participants during natural everyday laptop use over more than three months. Our dataset is significantly more variable than existing ones with respect to appearance and illumination. We also present a method for in-the-wild …
Recent work has proven the effectiveness of transformers in many computer vision tasks. However, the performance of transformers in gaze estimation is still unexplored. In this paper, we employ transformers and assess their effectiveness for gaze estimation. We consider two forms of vision transformer which are pure transformers and hybrid transformers. We first follow the popular ViT and employ a pure transformer to estimate gaze from images. On the other hand, we preserve the convolutional layers and integrate CNNs as well as transformers. The transformer serves as a component to complement …
Gaze tracking is an important technology in many domains. Techniques such as Convolutional Neural Networks (CNN) has allowed the invention of gaze tracking method that relies only on commodity hardware such as the camera on a personal computer. It has been shown that the full-face region for gaze estimation can provide better performance than from an eye image alone. However, a problem with using the full-face image is the heavy computation due to the larger image size. This study tackles this problem through compression of the input full-face image by removing redundant information using a no…
Universitat de Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）；University of Calgary（卡尔加里大学）；University of Larestan（拉雷斯坦大学）
Gaze behavior is an important non-verbal cue in social signal processing and human-computer interaction. In this paper, we tackle the problem of person- and head pose-independent 3D gaze estimation from remote cameras, using a multi-modal recurrent convolutional neural network (CNN). We propose to combine face, eyes region, and face landmarks as individual streams in a CNN to estimate gaze in still images. Then, we exploit the dynamic nature of gaze by feeding the learned features of all the frames in a sequence to a many-to-one recurrent module that predicts the 3D gaze vector of the last fra…
Hong Kong University of Science and Technology（香港科技大学）
Safe and efficient crowd navigation for mobile robot is a crucial yet challenging task. Previous work has shown the power of deep reinforcement learning frameworks to train efficient policies. However, their performance deteriorates when the crowd size grows. We suggest that this can be addressed by enabling the network to identify and pay attention to the humans in the crowd that are most critical to navigation. We propose a novel network utilizing a graph representation to learn the policy. We first train a graph convolutional network based on human gaze data that accurately predicts human a…
HOIGaze: Gaze Estimation During Hand-Object Interactions in Extended Reality Exploiting Eye-Hand-Head Coordination
University of Stuttgart Germany（斯图加特大学）；University of Tuebingen Germany（图宾根大学）；The Center for Bionic Intelligence Tuebingen Stuttgart Germany（图宾根-斯图加特生物智能中心）
Idiap Research Institute（Idiap研究所）；Eyeware Tech SA（Eyeware科技公司）
Non-invasive gaze estimation methods usually regress gaze directions directly from a single face or eye image. However, due to important variabilities in eye shapes and inner eye structures amongst individuals, universal models obtain limited accuracies and their output usually exhibit high variance as well as biases which are subject dependent. Therefore, increasing accuracy is usually done through calibration, allowing gaze predictions for a subject to be mapped to his/her actual gaze. In this paper, we introduce a novel image differential method for gaze estimation. We propose to directly t…
China University of Mining and Technology（中国矿业大学）；Australian National University（澳大利亚国立大学）
Vision based text entry systems aim to help disabled people achieve text communication using eye movement. Most previous methods have employed an existing eye tracker to predict gaze direction and design an input method based upon that. However, these methods can result in eye tracking quality becoming easily affected by various factors and lengthy amounts of time for calibration. Our paper presents a novel efficient gaze based text input method, which has the advantage of low cost and robustness. Users can type in words by looking at an on-screen keyboard and blinking. Rather than estimate ga…
Monash University（莫纳什大学）；Indian Institute of Technology Ropar（罗帕尔印度理工学院）；Kroop AI；University of Trento（特伦托大学）
Labelling of human behavior analysis data is a complex and time consuming task. In this paper, a fully automatic technique for labelling an image based gaze behavior dataset for driver gaze zone estimation is proposed. Domain knowledge is added to the data recording paradigm and later labels are generated in an automatic manner using Speech To Text conversion (STT). In order to remove the noise in the STT process due to different illumination and ethnicity of subjects in our data, the speech frequency and energy are analysed. The resultant Driver Gaze in the Wild (DGW) dataset contains 586 rec…
Berkeley Institute for Data Science, University of California, Berkeley（加州大学伯克利分校伯克利数据科学研究所）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
Fiber-reinforced ceramic-matrix composites are advanced materials resistant to high temperatures, with application to aerospace engineering. Their analysis depends on the detection of embedded fibers, with semi-supervised techniques usually employed to separate fibers within the fiber beds. Here we present an open computational pipeline to detect fibers in ex-situ X-ray computed tomography fiber beds. To separate the fibers in these samples, we tested four different architectures of fully convolutional neural networks. When comparing our neural network approach to a semi-supervised one, we obt…
In this paper we introduce a novel Depth-Aware Video Saliency approach to predict human focus of attention when viewing RGBD videos on regular 2D screens. We train a generative convolutional neural network which predicts a saliency map for a frame, given the fixation map of the previous frame. Saliency estimation in this scenario is highly important since in the near future 3D video content will be easily acquired and yet hard to display. This can be explained, on the one hand, by the dramatic improvement of 3D-capable acquisition equipment. On the other hand, despite the considerable progress…
Human gaze is a crucial cue used in various applications such as human-robot interaction and virtual reality. Recently, convolution neural network (CNN) approaches have made notable progress in predicting gaze direction. However, estimating gaze in-the-wild is still a challenging problem due to the uniqueness of eye appearance, lightning conditions, and the diversity of head pose and gaze directions. In this paper, we propose a robust CNN-based model for predicting gaze in unconstrained settings. We propose to regress each gaze angle separately to improve the per-angel prediction accuracy, whi…
Hong Kong University of Science and Technology（香港科技大学）
Imitation learning by behavioral cloning is a prevalent method that has achieved some success in vision-based autonomous driving. The basic idea behind behavioral cloning is to have the neural network learn from observing a human expert's behavior. Typically, a convolutional neural network learns to predict the steering commands from raw driver-view images by mimicking the behaviors of human drivers. However, there are other cues, such as gaze behavior, available from human drivers that have yet to be exploited. Previous researches have shown that novice human learners can benefit from observi…
University of Modena and Reggio Emilia（摩德纳和雷焦艾米利亚大学）；University of Udine（乌迪内大学）
Data-driven saliency has recently gained a lot of attention thanks to the use of Convolutional Neural Networks for predicting gaze fixations. In this paper we go beyond standard approaches to saliency prediction, in which gaze maps are computed with a feed-forward network, and present a novel model which can predict accurate saliency maps by incorporating neural attentive mechanisms. The core of our solution is a Convolutional LSTM that focuses on the most salient regions of the input image to iteratively refine the predicted saliency map. Additionally, to tackle the center bias typical of hum…
Is Geometry Enough? An Evaluation of Landmark-Based Gaze Estimation
Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche（工业工程与数学科学系，帕尔米塞大学）；Department of Science and Information Technology, Università Pegaso（科学与信息科技系，佩加索大学）
EyeTheia: A Lightweight and Accessible Eye-Tracking Toolbox
Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition（里尔大学、法国国家医学研究院、里尔大学医院、U1172 - 里尔神经科学与认知中心）；Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL（里尔大学、法国国家科学研究中心、里尔中央理工大学、UMR 9189 CRIStAL）；Centre national de ressources et de résilience (CN2R)（资源与韧性国家研究中心）
The study of human gaze behavior in natural contexts requires algorithms for gaze estimation that are robust to a wide range of imaging conditions. However, algorithms often fail to identify features such as the iris and pupil centroid in the presence of reflective artifacts and occlusions. Previous work has shown that convolutional networks excel at extracting gaze features despite the presence of such artifacts. However, these networks often perform poorly on data unseen during training. This work follows the intuition that jointly training a convolutional network with multiple datasets lear…
GMGaze: MoE-Based Context-Aware Gaze Estimation with CLIP and Multiscale Transformer
School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, China（桂林电子科技大学人工智能学院）；The Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure, Montreal, Canada（图像、视觉与人工智能实验室，蒙特利尔大学高级技术学院）；Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia（计算机与信息科学学院信息科技系，沙特国王大学）；Department of Cybersecurity, College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia（计算机科学与工程学院网络安全系，塔伊卜大学）；Department of Networked Engineering, College of Computer Science and Engineering, Jeddah University, Jeddah, Saudi Arabia（计算机科学与工程学院网络工程系，吉达大学）
The increased availability and accuracy of eye-gaze tracking technology has sparked attention-related research in psychology, neuroscience, and, more recently, computer vision and artificial intelligence. The attention mechanism in artificial neural networks is known to improve learning tasks. However, no previous research has combined the network attention and human attention. This paper describes a gaze-guided class activation mapping (GG-CAM) method to directly regulate the formation of network attention based on expert radiologists' visual attention for the chest X-ray pathology classifica…
University of Kassel（卡塞尔大学）；Interdisciplinary Research Center for Information System Design（信息系统设计跨学科研究中心）
Virtual-reality (VR) and augmented-reality (AR) technology is increasingly combined with eye-tracking. This combination broadens both fields and opens up new areas of application, in which visual perception and related cognitive processes can be studied in interactive but still well controlled settings. However, performing a semantic gaze analysis of eye-tracking data from interactive three-dimensional scenes is a resource-intense task, which so far has been an obstacle to economic use. In this paper we present a novel approach which minimizes time and information necessary to annotate volumes…
Modular Deep Learning Framework for Assistive Perception: Gaze, Affect, and Speaker Identification
School of Computer Science and Engineering (SCOPE)（计算机科学与工程学院）；Vellore Institute of Technology (VIT)（韦洛雷理工学院）；School of Electronics Engineering (SENSE)（电子工程学院）
Evaluating Sensitivity Parameters in Smartphone-Based Gaze Estimation: A Comparative Study of Appearance-Based and Infrared Eye Trackers
School of Computer, Data and Mathematical Sciences, Western Sydney University, Penrith, New South Wales, Australia（计算机、数据与数学科学学院，西悉尼大学，彭里，新南威尔士州，澳大利亚）；The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, NSW, Australia（脑行为与发育研究所，西悉尼大学，彭里，新南威尔士州，澳大利亚）；School of Systems and Computing, University of New South Wales, Canberra, ACT, Australia（系统与计算学院，新南威尔士大学，堪培拉，澳大利亚）
EG-SpikeFormer: Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis
School of Computing, University of Georgia（佐治亚大学计算学院）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；University of Georgia（佐治亚大学）；Department of Physics and Astronomy（物理与天文学系）
Utrecht University（乌得勒支大学）；University of Essex（埃塞克斯大学）；London South Bank University（伦敦南岸大学）
Deep convolutional networks are widely used in video action recognition. 3D convolutions are one prominent approach to deal with the additional time dimension. While 3D convolutions typically lead to higher accuracies, the inner workings of the trained models are more difficult to interpret. We focus on creating human-understandable visual explanations that represent the hierarchical parts of spatio-temporal networks. We introduce Class Feature Pyramids, a method that traverses the entire network structure and incrementally discovers kernels at different network depths that are informative for…
University of Rome La Sapienza（罗马大学）；University of Applied Sciences Western Switzerland (HES-SO Valais)（瑞士西部应用科学与艺术大学）
The instability of myoelectric signals over time complicates their use to control highly articulated prostheses. To address this problem, studies have tried to combine surface electromyography with modalities that are less affected by the amputation and environment, such as accelerometry or gaze information. In the latter case, the hypothesis is that a subject looks at the object he or she intends to manipulate and that knowing this object's affordances allows to constrain the set of possible grasps. In this paper, we develop an automated way to detect stable fixations and show that gaze infor…
In a typical video conferencing setup, it is hard to maintain eye contact during a call since it requires looking into the camera rather than the display. We propose an eye contact correction model that restores the eye contact regardless of the relative position of the camera and display. Unlike previous solutions, our model redirects the gaze from an arbitrary direction to the center without requiring a redirection angle or camera/display/user geometry as inputs. We use a deep convolutional neural network that inputs a monocular image and produces a vector field and a brightness map to corre…
Event-based Gaze Control System for Accurate Real-time Spin Estimation in Professional Ball Games
Sony AI, Tokyo, Japan（索尼AI，东京，日本）；Sony AI, Zürich, Switzerland（索尼AI，苏黎世，瑞士）；Sony Advanced Visual Sensing, Sony Europe Ltd., Zurich, Switzerland（索尼高级视觉感知，索尼欧洲有限公司，苏黎世，瑞士）
Massachusetts Institute of Technology（麻省理工学院）；Toyota Research Institute（丰田研究院）
We introduce a saliency-based distortion layer for convolutional neural networks that helps to improve the spatial sampling of input data for a given task. Our differentiable layer can be added as a preprocessing block to existing task networks and trained altogether in an end-to-end fashion. The effect of the layer is to efficiently estimate how to sample from the original data in order to boost task performance. For example, for an image classification task in which the original data might range in size up to several megapixels, but where the desired input images to the task network are much…
