With the increasing need for facial behavior analysis, semi-supervised AU intensity estimation using only keyframe annotations has emerged as a practical and effective solution to relieve the burden of annotation. However, the lack of annotations makes the spurious correlation problem caused by AU co-occurrences and subject variation much more prominent, leading to non-robust intensity estimation that is entangled among AUs and biased among subjects. We observe that trend information inherent in keyframe annotations could act as extra supervision and raising the awareness of AU-specific facial appearance changing trends during training is the key to learning invariant AU-specific features. To this end, we propose \textbf{T}rend-\textbf{A}ware \textbf{S}upervision (TAS), which pursues three kinds of trend awareness, including intra-trend ranking awareness, intra-trend speed awareness, and inter-trend subject awareness. TAS alleviates the spurious correlation problem by raising trend awareness during training to learn AU-specific features that represent the corresponding facial appearance changes, to achieve intensity estimation invariance. Experiments conducted on two commonly used AU benchmark datasets, BP4D and DISFA, show the effectiveness of each kind of awareness. And under trend-aware supervision, the performance can be improved without extra computational or storage costs during inference.
Zhejiang University（浙江大学）；Westlake University（西lake大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Hunan University（湖南大学）；Zhejiang University Shanghai Innovation Institute（浙江大学上海创新研究院）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
University of Technology Sydney（悉尼科技大学）；Facebook Reality Labs（脸书现实实验室）
In this paper, we present supervision-by-registration, an unsupervised approach to improve the precision of facial landmark detectors on both images and video. Our key observation is that the detections of the same landmark in adjacent frames should be coherent with registration, i.e., optical flow. Interestingly, the coherency of optical flow is a source of supervision that does not require manual labeling, and can be leveraged during detector training. For example, we can enforce in the training loss function that a detected landmark at frame$_{t-1}$ followed by optical flow tracking from fr…
Supervision-by-Hallucination-and-Transfer: A Weakly-Supervised Approach for Robust and Precise Facial Landmark Detection
School of Information Engineering, Zhongnan University of Economics（经济法律大学信息工程学院）；College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；School of Information Science and Engineering, Yunnan University（云南大学信息科学与工程学院）
We propose a deep convolutional neural network (CNN) for face detection leveraging on facial attributes based supervision. We observe a phenomenon that part detectors emerge within CNN trained to classify attributes from uncropped face images, without any explicit part supervision. The observation motivates a new method for finding faces through scoring facial parts responses by their spatial structure and arrangement. The scoring mechanism is data-driven, and carefully formulated considering challenging cases where faces are only partially visible. This consideration allows our network to det…
Due to the high annotation cost of large-scale facial landmark detection tasks in videos, a semi-supervised paradigm that uses self-training for mining high-quality pseudo-labels to participate in training has been proposed by researchers. However, self-training based methods often train with a gradually increasing number of samples, whose performances vary a lot depending on the number of pseudo-labeled samples added. In this paper, we propose a teacher-student asynchronous learning~(TSAL) framework based on the multi-source supervision signal consistency criterion, which implicitly mines pse…
Monash University（莫纳什大学）；Indian Institute of Technology Ropar（印度理工学院罗帕尔分校）；University of Melbourne（墨尔本大学）
Robust gaze estimation is a challenging task, even for deep CNNs, due to the non-availability of large-scale labeled data. Moreover, gaze annotation is a time-consuming process and requires specialized hardware setups. We propose MTGLS: a Multi-Task Gaze estimation framework with Limited Supervision, which leverages abundantly available non-annotated facial image data. MTGLS distills knowledge from off-the-shelf facial image analysis models, and learns strong feature representations of human eyes, guided by three complementary auxiliary signals: (a) the line of sight of the pupil (i.e. pseudo-…
Thapar Institute of Engineering and Technology（塔帕尔工程技术学院）；Indian Institute of Technology Ropar（印度理工学院鲁尔基分校）；Oxford Brookes University（牛津布鲁克斯大学）
Recently, there has been numerous breakthroughs in face hallucination tasks. However, the task remains rather challenging in videos in comparison to the images due to inherent consistency issues. The presence of extra temporal dimension in video face hallucination makes it non-trivial to learn the facial motion through out the sequence. In order to learn these fine spatio-temporal motion details, we propose a novel cross-modal audio-visual Video Face Hallucination Generative Adversarial Network (VFH-GAN). The architecture exploits the semantic correlation of between the movement of the facial …
Lanzhou University（兰州大学）；University of Guelph（圭尔夫大学）；Zhejiang Sci-Tech University（浙江理工大学）；Chongqing University（重庆大学）
The ever-increasing demands for intuitive interactions in Virtual Reality has triggered a boom in the realm of Facial Expression Recognition (FER). To address the limitations in existing approaches (e.g., narrow receptive fields and homogenous supervisory signals) and further cement the capacity of FER tools, a novel multifarious supervision-steering Transformer for FER in the wild is proposed in this paper. Referred as FER-former, our approach features multi-granularity embedding integration, hybrid self-attention scheme, and heterogeneous domain-steering supervision. In specific, to dig deep…
This paper demonstrates a novel approach to improve face-recognition pose-invariance using semantic-segmentation features. The proposed Seg-Distilled-ID network jointly learns identification and semantic-segmentation tasks, where the segmentation task is then "distilled" (MobileNet encoder). Performance is benchmarked against three state-of-the-art encoders on a publicly available data-set emphasizing head-pose variations. Experimental evaluations show the Seg-Distilled-ID network shows notable robustness benefits, achieving 99.9% test-accuracy in comparison to 81.6% on ResNet-101, 96.1% on VG…
Indraprastha Institute of Information Technology, Delhi（德里英德拉普拉斯塔信息技术学院）；Indian Institute of Technology, Roorkee（鲁尔基印度理工学院）；International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）
Retrieving facial images from attributes plays a vital role in various systems such as face recognition and suspect identification. Compared to other image retrieval tasks, facial image retrieval is more challenging due to the high subjectivity involved in describing a person's facial features. Existing methods do so by comparing specific characteristics from the user's mental image against the suggested images via high-level supervision such as using natural language. In contrast, we propose a method that uses a relatively simpler form of binary supervision by utilizing the user's feedback to…
Amortised Invariance Learning for Contrastive Self-Supervision
University of Edinburgh（爱丁堡大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Heidelberg Institute for Theoretical Studies（海德堡理论研究所）；Samsung AI Research Centre（三星人工智能研究中心）
Universitat de Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）；Univ. Autònoma de Barcelona (UAB)（巴塞罗那自治大学）
Facial expressions are combinations of basic components called Action Units (AU). Recognizing AUs is key for developing general facial expression analysis. In recent years, most efforts in automatic AU recognition have been dedicated to learning combinations of local features and to exploiting correlations between Action Units. In this paper, we propose a deep neural architecture that tackles both problems by combining learned local and global features in its initial stages and replicating a message passing algorithm between classes similar to a graphical model inference approach in later stag…
Presence of noise in the labels of large scale facial expression datasets has been a key challenge towards Facial Expression Recognition (FER) in the wild. During early learning stage, deep networks fit on clean data. Then, eventually, they start overfitting on noisy labels due to their memorization ability, which limits FER performance. This work proposes an effective training strategy in the presence of noisy labels, called as Consensual Collaborative Training (CCT) framework. CCT co-trains three networks jointly using a convex combination of supervision loss and consistency loss, without ma…
DisFaceRep: Representation Disentanglement for Co-occurring Facial Components in Weakly Supervised Face Parsing
School of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；School of AI and Advanced Computing, Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学人工智能与先进计算学院）；Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University（香港理工大学电子与电气工程系）；Department of Electrical and Computer Engineering, National University of Singapore（新加坡国立大学电子与计算机工程系）；Computer Vision Institute, School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院计算机视觉研究所）；Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen University（广东省智能信息处理重点实验室，深圳大学）
We propose a novel facial Anchors and Contours Estimation framework, ACE-Net, for fine-level face alignment tasks. ACE-Net predicts facial anchors and contours that are richer than traditional facial landmarks while overcoming ambiguities and inconsistencies in their definitions. We introduce a weakly supervised loss enabling ACE-Net to learn from existing facial landmarks datasets without the need for reannotation. Instead, synthetic data, from which GT contours can be easily obtained, is used during training to bridge the density gap between landmarks and true facial contours. We evaluate th…
One of the most pressing challenges for the detection of face-manipulated videos is generalising to forgery methods not seen during training while remaining effective under common corruptions such as compression. In this paper, we examine whether we can tackle this issue by harnessing videos of real talking faces, which contain rich information on natural facial appearance and behaviour and are readily available in large quantities online. Our method, termed RealForensics, consists of two stages. First, we exploit the natural correspondence between the visual and auditory modalities in real vi…
South China University of Technology（华南理工大学）；Nanyang Technological University（南洋理工大学）；University of Sydney（悉尼大学）
Automatic facial age estimation can be used in a wide range of real-world applications. However, this process is challenging due to the randomness and slowness of the aging process. Accordingly, in this paper, we propose a comprehensive framework aimed at overcoming the challenges associated with facial age estimation. First, we propose a novel age encoding method, referred to as 'Soft-ranking', which encodes two important properties of facial age, i.e., the ordinal property and the correlation between adjacent ages. Therefore, Soft-ranking provides a richer supervision signal for training dee…
High-Fidelity Facial Albedo Estimation via Texture Quantization
University of Technology Sydney（悉尼科技大学）；Shanghai Jiao Tong University（上海交通大学）；DeepGlint（旷视科技（北京））；Insightface；Zhejiang University（浙江大学）；Imperial College London（伦敦帝国学院）
University of Science（科学大学）；Vietnam National University, Ho Chi Minh City（胡志明市越南国家大学）；AIOZ；FPT Software AI Center（FPT软件人工智能中心）；University of Liverpool（利物浦大学）
Despite significant progress over the past few years, ambiguity is still a key challenge in Facial Expression Recognition (FER). It can lead to noisy and inconsistent annotation, which hinders the performance of deep learning models in real-world scenarios. In this paper, we propose a new uncertainty-aware label distribution learning method to improve the robustness of deep models against uncertainty and ambiguity. We leverage neighborhood information in the valence-arousal space to adaptively construct emotion distributions for training samples. We also consider the uncertainty of provided la…
Although current face alignment algorithms have obtained pretty good performances at predicting the location of facial landmarks, huge challenges remain for faces with severe occlusion and large pose variations, etc. On the contrary, semantic location of facial boundary is more likely to be reserved and estimated on these scenes. Therefore, we study a two-stage but end-to-end approach for exploring the relationship between the facial boundary and landmarks to get boundary-aware landmark predictions, which consists of two modules: the self-calibrated boundary estimation (SCBE) module and the bo…
SIVA-RL: Sensitivity-Invariance Visual Alignment for Multimodal Reinforcement Learning
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；Sichuan University（四川大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；University of Macau（澳门大学）
Multi-Task Multi-Modal Self-Supervised Learning for Facial Expression Recognition
Technische Universität Berlin（柏林工业大学）；Science of Intelligence, Research Cluster of Excellence（科学智慧卓越研究集群）；Univ. Grenoble Alpes（格勒诺布尔大学）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）；LJK（让·昆廷诺实验室）；Humboldt-Universität zu Berlin（柏林洪堡大学）
Samsung AI Center – Moscow（三星人工智能中心（莫斯科））；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）
We present a system for real-time RGBD-based estimation of 3D human pose. We use parametric 3D deformable human mesh model (SMPL-X) as a representation and focus on the real-time estimation of parameters for the body pose, hands pose and facial expression from Kinect Azure RGB-D camera. We train estimators of body pose and facial expression parameters. Both estimators use previously published landmark extractors as input and custom annotated datasets for supervision, while hand pose is estimated directly by a previously published method. We combine the predictions of those estimators into a te…
School of Electronic Information and Electrical Engineering（电子信息与电气工程学院）；School of Electronics Engineering and Computer Science（电子工程与计算机科学学院）；Shanghai Jiao Tong University（上海交通大学）；Peking University（北京大学）
Getting pain intensity from face images is an important problem in autonomous nursing systems. However, due to the limitation in data sources and the subjectiveness in pain intensity values, it is hard to adopt modern deep neural networks for this problem without domain-specific auxiliary design. Inspired by human vision priori, we propose a novel approach called saliency supervision, where we directly regularize deep networks to focus on facial area that is discriminative for pain regression. Through alternative training between saliency supervision and global loss, our method can learn spars…
National Technical University of Athens（雅典国立技术大学）；Imperial College London（伦敦帝国理工学院）；Institute for Language and Speech Processing, Athena R.C.（雅典娜研究中心语言与语音处理研究所）；Foundation for Research & Technology - Hellas（希腊研究与技术基金会）；University of Exeter（埃克塞特大学）
The recent state of the art on monocular 3D face reconstruction from image data has made some impressive advancements, thanks to the advent of Deep Learning. However, it has mostly focused on input coming from a single RGB image, overlooking the following important factors: a) Nowadays, the vast majority of facial image data of interest do not originate from single images but rather from videos, which contain rich dynamic information. b) Furthermore, these videos typically capture individuals in some form of verbal communication (public talks, teleconferences, audiovisual human-computer intera…
Researchers have developed excellent feed-forward models that learn to map images to desired outputs, such as to the images' latent factors, or to other images, using supervised learning. Learning such mappings from unlabelled data, or improving upon supervised models by exploiting unlabelled data, remains elusive. We argue that there are two important parts to learning without annotations: (i) matching the predictions to the input observations, and (ii) matching the predictions to known priors. We propose Adversarial Inverse Graphics networks (AIGNs): weakly supervised neural network models t…
Omni-Supervised Motion Editing: Balancing Change and Invariance through Positive-Negative Learning
Shanghai Institute of Artificial Intelligence for Education, East China Normal University, China（上海人工智能教育研究院，华东师范大学，中国）；School of Computer Science and Technology, East China Normal University, China（华东师范大学计算机科学与技术学院，中国）；School of Statistics, East China Normal University, China（华东师范大学统计学院，中国）；The 27th Research Institute of CETC, Zhengzhou, China（中国电子科技集团第27研究所，郑州，中国）；Key Laboratory of Advanced Theory and Application in Statistics and Data Science, MOE, China（教育部统计与数据科学先进理论与应用重点实验室，中国）；Shanghai Key Laboratory of Computer Software Evaluating and Testing, China（上海计算机软件评测测试重点实验室，中国）；School of Computer Science, Shanghai Jiao Tong University, China（上海交通大学计算机科学学院，中国）
Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target Data
LIVIA, Dept. of Systems Engineering, ETS Montreal, Canada（LIVIA，系统工程系，蒙特利尔大学工程学院，加拿大）；LIVIA, Dept. of Software and IT Engineering, ETS Montreal, Canada（LIVIA，软件与信息技术工程系，蒙特利尔大学工程学院，加拿大）；Dept. of Health, Kinesiology & Applied Physiology, Concordia University, Montreal, Canada（健康、运动科学与应用生理学系，康科迪亚大学，蒙特利尔，加拿大）
Attributes Shape the Embedding Space of Face Recognition Models
Department of Mathematical Sciences, Politecnico di Torino, Turin, Italy（波兰理工学院数学科学系）；Department of Computer Science, University of Pisa, Pisa, Italy（比萨大学计算机科学系）；CENTAI Institute, Turin, Italy（CENTAI研究院）
Successful applications of deep learning (DL) requires large amount of annotated data. This often restricts the benefits of employing DL to businesses and individuals with large budgets for data-collection and computation. Summarization offers a possible solution by creating much smaller representative datasets that can allow real-time deep learning and analysis of big data and thus democratize use of DL. In the proposed work, our aim is to explore a novel approach to task-specific image corpus summarization using semantic information and self-supervision. Our method uses a classification-base…
Self supervised learning (SSL) has become a very successful technique to harness the power of unlabeled data, with no annotation effort. A number of developed approaches are evolving with the goal of outperforming supervised alternatives, which have been relatively successful. One main issue in SSL is robustness of the approaches under different settings. In this paper, for the first time, we recognize the fundamental limits of SSL coming from the use of a single-supervisory signal. To address this limitation, we leverage the power of uncertainty representation to devise a robust and general s…
