mtybilly/Instruct2DS huggingface dataset:
Label2Label: A Language Modeling Framework for Multi-Attribute Learning
Authors: Wanhua Li, Zhexuan Cao, Jianjiang Feng, Jie Zhou, Jiwen Lu
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: Objects are usually associated with multiple attributes, and these attributes often exhibit high correlations. Modeling complex relationships between attributes poses a great challenge for multi-attribute learning. This paper proposes a simple yet generic framework named Label2Label to exploit the complex attribute correlations. Label2Label is the first attempt for multi-attribute prediction from the perspective of language modeling. Specifically, it treats each attribute label as a "word" describing the sample. As each sample is annotated with multiple attribute labels, these "words" will naturally form an unordered but meaningful "sentence", which depicts the semantic information of the corresponding sample. Inspired by the remarkable success of pre-training language models in NLP, Label2Label introduces an image-conditioned masked language model, which randomly masks some of the "word" tokens from the label "sentence" and aims to recover them based on the masked "sentence" and the context conveyed by image features. Our intuition is that the instance-wise attribute relations are well grasped if the neural net can infer the missing attributes based on the context and the remaining attribute hints. Label2Label is conceptually simple and empirically powerful. Without incorporating task-specific prior knowledge and highly specialized network designs, our approach achieves state-of-the-art results on three different multi-attribute learning tasks, compared to highly customized domain-specific methods. Code is available at https://github.com/Li-Wanhua/Label2Label.
AgeTransGAN for Facial Age Transformation with Rectified Performance Metrics
Authors: Gee-Sern Hsu, Rui-Cang Xie, Zhi-Ting Chen, Yu-Hong Lin
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "We propose the AgeTransGAN for facial age transformation and the improvements to the metrics for performance evaluation. The AgeTransGAN is composed of an encoder-decoder generator and a conditional multitask discriminator with an age classifier embedded. The generator exploits cycle-generation consistency, age classification and cross-age identity consistency to disentangle the identity and age characteristics during training. The discriminator fuses age features with the target age group label and collaborates with the embedded age classifier to warrant the desired age traits made on the generated images. As many previous work use the Face++ APIs as the metrics for performance evaluation, we reveal via experiments the inappropriateness of using the Face++ as the metrics for the face verification and age estimation of juniors. To rectify the Face++ metrics, we made the Cross-Age Face (CAF) dataset which contains 4000 face images of 520 individuals taken from their childhood to seniorhood. The CAF is one of the very few datasets that offer much more images of the same individuals across large age gaps than the popular FG-Net. We use the CAF to rectify the face verification thresholds of the Face++ APIs across different age gaps. We also use the CAF and the FFHQ-Aging datasets to compare the age estimation performance of the Face++ APIs and an age estimator made by our own, and propose rectified metrics for performance evaluation. We compare the performance of the AgeTransGAN and state-of-the-art approaches by using the existing and rectified metrics."
Hierarchical Contrastive Inconsistency Learning for Deepfake Video Detection
Authors: Zhihao Gu, Taiping Yao, Yang Chen, Shouhong Ding, Lizhuang Ma
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "With the rapid development of Deepfake techniques, the capacity of generating hyper-realistic faces has aroused public concerns in recent years. The temporal inconsistency which derives from the contrast of facial movements between pristine and forged videos can serve as an efficient cue in identifying Deepfakes. However, most existing approaches tend to impose binary supervision to model it, which restricts them to only focusing on the category-level discrepancies. In this paper, we propose a novel Hierarchical Contrastive Inconsistency Learning framework (HCIL) with a two-level contrastive paradigm. Specially, sampling multiply snippets to form the input, HCIL performs contrastive learning from both local and global perspectives to capture more general and intrinsical temporal inconsistency between real and fake videos. Moreover, we also incorporate a region-adaptive module for intra-snippet inconsistency mining and an inter-snippet fusion module for cross-snippet information fusion, which further facilitates the inconsistency learning. Extensive experiments and visualizations demonstrate the effectiveness of our method against SOTA competitors on four Deepfake video datasets, \emph{i.e.,} FaceForensics++, Celeb-DF, DFDC, and Wild-Deepfake."
Rethinking Robust Representation Learning under Fine-Grained Noisy Faces
Authors: Bingqi Ma, Guanglu Song, Boxiao Liu, Yu Liu
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Learning robust feature representation from large-scale noisy faces stands out as one of the key challenges in high-performance face recognition. Recent attempts have been made to cope with this challenge by alleviating the intra-class conflict and inter-class conflict. However, the unconstrained noise type in each conflict still makes it difficult for these algorithms to perform well. To better understand this, we reformulate the noise type of faces in each class with a more fine-grained manner as N-identities|K-clusters|C-conflicts. Different types of noisy faces can be generated by adjusting the values of N, K, and C. Based on this unified formulation, we found that the main barrier behind the noise-robust representation learning is the flexibility of the algorithm under different N, K, and C. For this potential problem, we constructively propose a new method, named Evolving Sub-centers Learning (ESL), to find optimal hyperplanes to accurately describe the latent space of massive noisy faces. More specifically, we initialize M sub-centers for each class and ESL encourages it to be automatically aligned to N-identities|K-clusters|C-conflicts faces via producing, merging, and dropping operations. Images belonging to the same identity in noisy faces can effectively converge to the same sub-center and samples with different identities will be pushed away. We inspect its effectiveness with an elaborate ablation study on synthetic noisy datasets different N, K, and C. Without any bells and whistles, ESL can achieve significant performance gains over state-of-the-art methods in large-scale noisy faces."
Teaching Where to Look: Attention Similarity Knowledge Distillation for Low Resolution Face Recognition
Authors: Sungho Shin, Joosoon Lee, Junseok Lee, Yeonguk Yu, Kyoobin Lee
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Deep learning has achieved outstanding performance for face recognition benchmarks, but performance reduces significantly for low resolution (LR) images. We propose an attention similarity knowledge distillation approach, which transfers attention maps obtained from a high resolution (HR) network as a teacher into an LR network as a student to boost LR recognition performance. Inspired by humans being able to approximate an object’s region from an LR image based on prior knowledge obtained from HR images, we designed the knowledge distillation loss using the cosine similarity to make the student network’s attention resemble the teacher network’s attention. Experiments on various LR face related benchmarks confirmed the proposed method generally improved recognition performances on LR settings, outperforming state-of-the-art results by simply transferring well-constructed attention maps. The code and pretrained models are publicly available in the https://github.com/gist-ailab/teaching-where-to-look."
Teaching with Soft Label Smoothing for Mitigating Noisy Labels in Facial Expressions
Authors: Tohar Lukov, Na Zhao, Gim Hee Lee, Ser-Nam Lim
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Recent studies have highlighted the problem of noisy labels in large scale in-the-wild facial expressions datasets due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, and the subjectiveness of annotators. To solve the problem of noisy labels, we propose Soft Label Smoothing (SLS), which smooths out multiple high-confidence classes in the logits by assigning them a probability based on the corresponding confidence, and at the same time assigning a fixed low probability to the low-confidence classes. Specifically, we introduce what we call the Smooth Operator Framework for Teaching (SOFT), based on a mean-teacher (MT) architecture where SLS is applied over the teacher’s logits. We find that the smoothed teacher’s logit provides a beneficial supervision to the student via a consistency loss -- at 30\% noise rate, SLS leads to 15\% reduction in the error rate compared with MT. Overall, SOFT beats the state of the art at mitigating noisy labels by a significant margin for both symmetric and asymmetric noise. Our code is available at https://github.com/toharl/soft."
Learning Dynamic Facial Radiance Fields for Few-Shot Talking Head Synthesis
Authors: Shuai Shen, Wanhua Li, Zheng Zhu, Yueqi Duan, Jie Zhou, Jiwen Lu
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Talking head synthesis is an emerging technology with wide applications in film dubbing, virtual avatars and online education. Recent NeRF-based methods generate more natural talking videos, as they better capture the 3D structural information of faces. However, a specific model needs to be trained for each identity with a large dataset. In this paper, we propose Dynamic Facial Radiance Fields (DFRF) for few-shot talking head synthesis, which can rapidly generalize to an unseen identity with few training data. Different from the existing NeRF-based methods which directly encode the 3D geometry and appearance of a specific person into the network, our DFRF conditions face radiance field on 2D appearance images to learn the face prior. Thus the facial radiance field can be flexibly adjusted to the new identity with few reference images. Additionally, for better modeling of the facial deformations, we propose a differentiable face warping module conditioned on audio signals to deform all reference images to the query space. Extensive experiments show that with only tens of seconds of training clip available, our proposed DFRF can synthesize natural and high-quality audio-driven talking head videos for novel identities with only 40k iterations. We highly recommend readers view our supplementary video for intuitive comparisons. Code is available in https://sstzal.github.io/DFRF/."
CoupleFace: Relation Matters for Face Recognition Distillation
Authors: Jiaheng Liu, Haoyu Qin, Yichao Wu, Jinyang Guo, Ding Liang, Ke Xu
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Knowledge distillation is an effective method to im- prove the performance of a lightweight neural network (i.e., student model) by transferring the knowledge of a well- performed neural network (i.e., teacher model), which has been widely applied in many computer vision tasks, includ- ing face recognition. Nevertheless, the current face recogni- tion distillation methods usually utilize the Feature Consis- tency Distillation (FCD) (e.g., L 2 distance) on the learned embeddings extracted by the teacher and student models for each sample, which is not able to fully transfer the knowl- edge from the teacher to the student for face recognition. In this work, we observe that mutual relation knowledge between samples is also important to improve the discrim- inative ability of the learned representation of the student model, and propose an effective face recognition distilla- tion method called CoupleFace by additionally introducing the Mutual Relation Distillation (MRD) into existing distil- lation framework. Specifically, in MRD, we first propose to mine the informative mutual relations, and then intro- duce the Relation-Aware Distillation (RAD) loss to trans- fer the mutual relation knowledge of the teacher model to the student model. Extensive experimental results on multi- ple benchmark datasets demonstrate the effectiveness of our proposed CoupleFace for face recognition."
Controllable and Guided Face Synthesis for Unconstrained Face Recognition
Authors: Feng Liu, Minchul Kim, Anil Jain, Xiaoming Liu
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Although significant advances have been made in face recognition (FR), FR in unconstrained environments remains challenging due to the domain gap between the semi-constrained training datasets and unconstrained testing scenarios. To address this problem, we propose a controllable face synthesis model (CFSM) that can mimic the distribution of target datasets in a style latent space. CFSM learns a linear subspace with orthogonal bases in the style latent space with precise control over the diversity and degree of synthesis. Furthermore, the pre-trained synthesis model can be guided by the FR model, making the resulting images more beneficial for FR model training. Besides, target dataset distributions are characterized by the learned orthogonal bases, which can be utilized to measure the distributional similarity among face datasets. Our approach yields significant performance gains on unconstrained benchmarks, such as IJB-B, IJB-C, TinyFace and IJB-S (+5.76% Rank1)."
Towards Robust Face Recognition with Comprehensive Search
Authors: Manyuan Zhang, Guanglu Song, Yu Liu, Hongsheng Li
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Data cleaning, architecture, and loss function design are important factors contributing to high-performance face recognition. Previously, the research community tries to improve the performance of each single aspect but failed to present a unified solution on the joint search of the optimal designs for all three aspects.In this paper, we for the first time identify that these aspects are tightly coupled to each other. Optimizing the design of each aspect actually greatly limits the performance and biases the algorithmic design.Specifically, we find that the optimal model architecture or loss function is closely coupled with the data cleaning. To eliminate the bias of single-aspect research and provide an overall understanding of the face recognition model design, we first carefully design the search space for each aspect, then a comprehensive search method is introduced to jointly search optimal data cleaning, architecture, and loss function design.In our framework, we make the proposed comprehensive search as flexible as possible, by using an innovative reinforcement learning based approach.Extensive experiments on million-level face recognition benchmarks demonstrate the effectiveness of our newly-designed search space for each aspect and the comprehensive search. We outperform expert algorithms developed for each single research track by large margins. More importantly, we analyze the difference between our searched optimal design and the independent design of the single factors. We point out that strong models tend to optimize with more difficult training datasets and loss functions. Our empirical study can provide guidance in future research towards more robust face recognition systems."
Towards Unbiased Label Distribution Learning for Facial Pose Estimation Using Anisotropic Spherical Gaussian
Authors: Zhiwen Cao, Dongfang Liu, Qifan Wang, Yingjie Chen
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Facial pose estimation refers to the task of predicting face orientation from a single RGB image. It is an important research topic with a wide range of applications in computer vision. Label distribution learning (LDL) based methods have been recently proposed for facial pose estimation, which achieve promising results. However, there are two major issues in existing LDL methods. First, the expectations of label distributions are biased, leading to a biased pose estimation. Second, fixed distribution parameters are applied for all learning samples, severely limiting the model capability. In this paper, we propose an Anisotropic Spherical Gaussian (ASG)-based LDL approach for facial pose estimation. In particular, our approach adopts the spherical Gaussian distribution on a unit sphere which constantly generates unbiased expectation. Meanwhile, we introduce a new loss function that allows the network to learn the distribution parameter for each learning sample flexibly. Extensive experimental results show that our method sets new state-of-the-art records on AFLW2000 and BIWI datasets."
AU-Aware 3D Face Reconstruction through Personalized AU-Specific Blendshape Learning
Authors: Chenyi Kuang, Zijun Cui, Jeffrey O. Kephart, Qiang Ji
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "3D face reconstruction and facial action unit (AU) detection have emerged as interesting and challenging tasks in recent years, but are rarely performed in tandem. Image-based 3D face reconstruction, which can represent a dense space of facial motions, is typically accomplished by estimating identity, expression, texture, head pose, and illumination separately via pre-constructed 3D morphable models (3DMMs). Recent 3D reconstruction models can recover high-quality geometric facial details like wrinkles and pores, but are still limited in their ability to recover 3D subtle motions caused by the activation of AUs. We present a multi-stage learning framework that recovers AU-interpretable 3D facial details by learning personalized AU-specific blendshapes from images. Our model explicitly learns 3D expression basis by using AU labels and generic AU relationship prior and then constrains the basis coefficients such that they are semantically mapped to each AU. Our AU-aware 3D reconstruction model generates accurate 3D expressions composed by semantically meaningful AU motion components. Furthermore, the output of the model can be directly applied to generate 3D AU occurrence predictions, which have not been fully explored by prior 3D reconstruction models. We demonstrate the effectiveness of our approach via qualitative and quantitative evaluations."
BézierPalm: A Free Lunch for Palmprint Recognition
Authors: Kai Zhao, Lei Shen, Yingyi Zhang, Chuhan Zhou, Tao Wang, Ruixin Zhang, Shouhong Ding, Wei Jia, Wei Shen
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Palmprints are private and stable information for biometric recognition. In the deep learning era, the development of palmprint recognition is limited by the lack of sufficient training data. In this paper, by observing that palmar creases are the key information to deep-learning-based palmprint recognition, we propose to synthesize training data by manipulating palmar creases. Concretely, we introduce an intuitive geometric model which represents palmar creases with parameterized Bézier curves. By randomly sampling Bézier parameters, we can synthesize massive training samples of diverse identities, which enables us to pretrain large-scale palmprint recognition models Experimental results demonstrate that such synthetically pretrained models have a very strong generalization ability: they can be efficiently transferred to real datasets, leading to significant performance improvements on palm print recognition. For example, under the open-set protocol, our method improves the strong ArcFace baseline by more than 10% in terms of TAR@1e-6. And under the closed-set protocol, our method reduces the equal error rate (EER) by an order of magnitude. The code will be made openly available upon acceptance."
Adaptive Transformers for Robust Few-Shot Cross-Domain Face Anti-Spoofing
Authors: Hsin-Ping Huang, Deqing Sun, Yaojie Liu, Wen-Sheng Chu, Taihong Xiao, Jinwei Yuan, Hartwig Adam, Ming-Hsuan Yang
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "While recent face anti-spoofing methods perform well under the intra-domain setups, an effective approach needs to account for much larger appearance variations of images acquired in complex scenes with different sensors for robust performance. In this paper, we present adaptive vision transformers (ViT) for robust cross-domain face anti-spoofing. Specifically, we adopt ViT as a backbone to exploit its strength to account for long-range dependencies among pixels. We further introduce the ensemble adapters module and feature-wise transformation layers in the ViT to adapt to different domains for robust performance with a few samples. Experiments on several benchmark datasets show that the proposed models achieve both robust and competitive performance against the state-of-the-art methods."
Face2Faceρ: Real-Time High-Resolution One-Shot Face Reenactment
Authors: Kewei Yang, Kang Chen, Daoliang Guo, Song-Hai Zhang, Yuan-Chen Guo, Weidong Zhang
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Existing one-shot face reenactment methods either present obvious artifacts in large pose transformations, or cannot well-preserve the identity information in the source images, or fail to meet the requirements of real-time applications due to the intensive amount of computation involved. In this paper, we introduce Face2Face^Ï, the first Real-time High-resolution and One-shot (RHO, Ï) face reenactment framework. To achieve this goal, we designed a new 3DMM-assisted warping-based face reenactment architecture which consists of two fast and efficient sub-networks, i.e., a u-shaped rendering network to reenact faces driven by head poses and facial motion fields, and a hierarchical coarse-to-fine motion network to predict facial motion fields guided by different scales of landmark images. Compared with existing state-of-the-art works, Face2Face^Ï can produce results of equal or better visual quality, yet with significantly less time and memory overhead. We also demonstrate that Face2Face^Ï can achieve real-time performance for face images of 1440×1440 resolution with a desktop GPU and 256×256 resolution with a mobile CPU."
Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation
Authors: Haiwen Feng, Timo Bolkart, Joachim Tesch, Michael J. Black, Victoria Abrevaya
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Virtual facial avatars will play an increasingly important role in immersive communication, games and the metaverse, and it is therefore critical that they be inclusive. This requires accurate recovery of the albedo, regardless of age, sex, or ethnicity. While significant progress has been made on estimating 3D facial geometry, appearance estimation has received less attention. The task is fundamentally ambiguous because the observed color is a function of albedo and lighting, both of which are unknown. We find that current methods are biased towards light skin tones due to (1) strongly biased priors that prefer lighter pigmentation and (2) algorithmic solutions that disregard the light/albedo ambiguity. To address this, we propose a new evaluation dataset (FAIR) and an algorithm (TRUST) to improve albedo estimation and, hence, fairness. Specifically, we create the first facial albedo evaluation benchmark where subjects are balanced in terms of skin color, and measure accuracy using the Individual Typology Angle (ITA) metric. We then address the light/albedo ambiguity by building on a key observation: the image of the full scene -as opposed to a cropped image of the face- contains important information about lighting that can be used for disambiguation. TRUST regresses facial albedo by conditioning on both the face region and a global illumination signal obtained from the scene image. Our experimental results show significant improvement compared to state-of-the-art methods on albedo estimation, both in terms of accuracy and fairness. The evaluation benchmark and code are available for research purposes at https://trust.is.tue.mpg.de."
BoundaryFace: A Mining Framework with Noise Label Self-Correction for Face Recognition
Authors: Shijie Wu, Xun Gong
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Face recognition has made tremendous progress in recent years due to the advances in loss functions and the explosive growth in training sets size. A properly designed loss is seen as key to extract discriminative features for classification. Several margin-based losses have been proposed as alternatives of softmax loss in face recognition. However, two issues remain to consider: 1) They overlook the importance of hard sample mining for discriminative learning. 2) Label noise ubiquitously exists in large-scale datasets, which can seriously damage the model’s performance. In this paper, starting from the perspective of decision boundary, we propose a novel mining framework that focuses on the relationship between a sample’s ground truth class center and its nearest negative class center. Specifically, a closed-set noise label self-correction module is put forward, making this framework work well on datasets containing a lot of label noise. The proposed method consistently outperforms SOTA methods in various face recognition benchmarks. Training code has been released at https://gitee.com/swjtugx/classmate/tree/master/OurGroup/BoundaryFace."
Pre-training Strategies and Datasets for Facial Representation Learning
Authors: Adrian Bulat, Shiyang Cheng, Jing Yang, Andrew Garbett, Enrique Sanchez, Georgios Tzimiropoulos
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "What is the best way to learn a universal face representation? Recent work on Deep Learning in the area of face analysis has focused on supervised learning for specific tasks of interest (e.g. face recognition, facial landmark localization etc.) but has overlooked the overarching question of how to find a facial representation that can be readily adapted to several facial analysis tasks and datasets. To this end, we make the following 4 contributions: (a) we introduce, for the first time, a comprehensive evaluation benchmark for facial representation learning consisting of 5 important face analysis tasks. (b) We systematically investigate two ways of large-scale representation learning applied to faces: supervised and unsupervised pre-training. Importantly, we focus our evaluations on the case of few-shot facial learning. (c) We investigate important properties of the training datasets including their size and quality (labelled, unlabelled or even uncurated). (d) To draw our conclusions, we conducted a very large number of experiments. Our main two findings are: (1) Unsupervised pre-training on completely in-the-wild, uncurated data provides consistent and, in some cases, significant accuracy improvements for all facial tasks considered. (2) Many existing facial video datasets seem to have a large amount of redundancy. We will release code, pre-trained models and data to facilitate future research."
Look Both Ways: Self-Supervising Driver Gaze Estimation and Road Scene Saliency
Authors: Isaac Kasahara, Simon Stent, Hyun Soo Park
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "We present a new on-road driving dataset, called “Look Both Ways”, which contains synchronized video of both driver faces and the forward road scene, along with ground truth gaze data registered from eye tracking glasses worn by the drivers. Our dataset supports the study of methods for non-intrusively estimating a driver’s focus of attention while driving - an important application area in road safety. A key challenge is that this task requires accurate gaze estimation, but supervised appearance-based gaze estimation methods often do not transfer well to real driving datasets, and in-domain ground truth to supervise them is difficult to gather. We therefore propose a method for self-supervision of driver gaze, by taking advantage of the geometric consistency between the driver’s gaze direction and the saliency of the scene as observed by the driver. We formulate a 3D geometric learning framework to enforce this consistency, allowing the gaze model to supervise the scene saliency model, and vice versa. We implement a prototype of our method and test it with our dataset, to show that compared to a supervised approach it can yield better gaze estimation and scene saliency estimation with no additional labels."
3D Face Reconstruction with Dense Landmarks
Authors: Erroll Wood, Tadas Baltrušaitis, Charlie Hewitt, Matthew Johnson, Jingjing Shen, Nikola Milosavljević, Daniel Wilde, Stephan Garbin, Toby Sharp, Ivan Stojiljković, Tom Cashman, Julien Valentin
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Landmarks often play a key role in face analysis, but many aspects of identity or expression cannot be represented by sparse landmarks alone. Thus, in order to reconstruct faces more accurately, landmarks are often combined with additional signals like depth images or techniques like differentiable rendering. Can we keep things simple by just using more landmarks? In answer, we present the first method that accurately predicts 10x as many landmarks as usual, covering the whole head, including the eyes and teeth. This is accomplished using synthetic training data, which guarantees perfect landmark annotations. By fitting a morphable model to these dense landmarks, we achieve state-of-the-art results for monocular 3D face reconstruction in the wild. We show that dense landmarks are an ideal signal for integrating face shape information across frames by demonstrating accurate and expressive facial performance capture in both monocular and multi-view scenarios. This approach is also highly efficient: we can predict dense landmarks and fit our 3D face model at over 150FPS on a single CPU thread. Please see our website: https://microsoft.github.io/DenseLandmarks/."
Emotion-Aware Multi-View Contrastive Learning for Facial Emotion Recognition
Authors: Daeha Kim, Byung Cheol Song
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "When a person recognizes another’s emotion, he or she recognizes the (facial) features associated with emotional expression. So, for a machine to recognize facial emotion(s), the features related to emotional expression must be represented and described properly. However, prior arts based on label supervision not only failed to explicitly capture features related to emotional expression, but also were not interested in learning emotional representations. This paper proposes a novel approach to generate features related to emotional expression through feature transformation and to use them for emotional representation learning. Specifically, the contrast between the generated features and overall facial features is quantified through contrastive representation learning, and then facial emotions are recognized based on understanding of angle and intensity that describe the emotional representation in the polar coordinate, i.e., the Arousal-Valence space. Experimental results show that the proposed method improves the PCC/CCC performance by more than 10% compared to the runner-up method in the wild datasets and is also qualitatively better in terms of neural activation map."
Order Learning Using Partially Ordered Data via Chainization
Authors: Seon-Ho Lee, Chang-Su Kim
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "We propose the chainization algorithm for effective order learning when only partially ordered data are available. First, we develop a binary comparator to predict missing ordering relations between instances. Then, by extending the Kahn’s algorithm, we form a chain representing a linear ordering of instances. We fine-tune the comparator over pseudo pairs, which are sampled from the chain, and then re-estimate the linear ordering alternately. As a result, we obtain a more reliable comparator and a more meaningful linear ordering. Experimental results show that the proposed algorithm yields excellent rank estimation performances under various weak supervision scenarios, including semi-supervised learning, domain adaptation, and bipartite cases. The source codes are available at https://github.com/seon92/Chainization"
Unsupervised High-Fidelity Facial Texture Generation and Reconstruction
Authors: Ron Slossberg, Ibrahim Jubran, Ron Kimmel
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Many methods have been proposed over the years to tackle the task of facial 3D geometry and texture recovery from a single image. Such methods often fail to provide high-fidelity texture without relying on 3D facial scans during training. In contrast, the complementary task of 3D facial generation has not received as much attention. As opposed to the 2D texture domain, where GANs have proven to produce highly realistic facial images, the more challenging 3D domain has not yet caught up to the same levels of realism and diversity. In this paper, we propose a novel unified pipeline for both tasks, generation of texture with coupled geometry, and reconstruction of high-fidelity texture. Our texture model is learned, in an unsupervised fashion, from natural images as opposed to scanned textures. To our knowledge, this is the first such unified framework independent of scanned textures. Our novel training pipeline incorporates a pre-trained 2D facial generator coupled with a deep feature manipulation methodology. By applying our two-step geometry fitting process, we seamlessly integrate our modeled textures into synthetically generated background images forming a realistic composition of our textured model with background, hair, teeth, and body. This enables us to apply transfer learning from the 2D image domain, thus leveraging the high-quality results obtained in this domain. We provide a comprehensive study on several recent methods comparing our model in generation and reconstruction tasks. As the extensive qualitative, as well as quantitative analysis, demonstrate, we achieve state-of-the-art results for both tasks."
Multi-Domain Learning for Updating Face Anti-Spoofing Models
Authors: Xiao Guo, Yaojie Liu, Anil Jain, Xiaoming Liu
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "In this work, we study multi-domain learning for face anti-spoofing (MD-FAS), where a pre-trained FAS model needs to be updated to perform equally well on both source and target domains while only using target domain data for updating. We present a new model for MD-FAS, which addresses the forgetting issue when learning new domain data, while possessing a high level of adaptability. First, we devise a simple yet effective module, called spoof region estimator (SRE), to identify spoof traces in the spoof image. Such spoof traces reflect the source pre-trained model’s responses that help upgraded models combat catastrophic forgetting during updating. Unlike prior works that estimate spoof traces which generate multiple outputs or a low-resolution binary mask, SRE produces one single, detailed pixel-wise estimate in an unsupervised manner. Secondly, we propose a novel framework, named FAS-wrapper, which transfers knowledge from the pre-trained models and seamlessly integrates with different FAS models. Lastly, to help the community further advance MD-FAS, we construct a new benchmark based on SIW, SIW-Mv2 and Oulu-NPU, and introduce four distinct protocols for evaluation, where source and target domains are different in terms of spoof type, age, ethnicity, and illumination. Our proposed method achieves superior performance on the MD-FAS benchmark than previous methods."
Towards Metrical Reconstruction of Human Faces
Authors: Wojciech Zielonka, Timo Bolkart, Justus Thies
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Face reconstruction and tracking is a building block of numerous applications in AR/VR, human-machine interaction, as well as medical applications. Most of these applications rely on a metrically correct prediction of the shape, especially, when the reconstructed subject is put into a metrical context (i.e., when there is a reference object of known size). A metrical reconstruction is also needed for any application that measures distances and dimensions of the subject (e.g., to virtually fit a glasses frame). State-of-the-art methods for face reconstruction from a single image are trained on large 2D image datasets in a self-supervised fashion. However, due to the nature of a perspective projection they are not able to reconstruct the actual face dimensions, and even predicting the average human face outperforms some of these methods in a metrical sense. To learn the actual shape of a face, we argue for a supervised training scheme. Since there exists no large-scale 3D dataset for this task, we annotated and unified small- and medium-scale databases. The resulting unified dataset is still a medium-scale dataset with more than 2k identities and training purely on it would lead to overfitting. To this end, we take advantage of a face recognition network pretrained on a large-scale 2D image dataset, which provides distinct features for different faces and is robust to expression, illumination, and camera changes. Using these features, we train our face shape estimator in a supervised fashion, inheriting the robustness and generalization of the face recognition network. Our method, which we call \modellong, outperforms the state-of-the-art reconstruction methods by a large margin, both on current non-metric benchmarks as well as on our metric benchmarks (15% and 24% lower average error on NoW, respectively)."
Discover and Mitigate Unknown Biases with Debiasing Alternate Networks
Authors: Zhiheng Li, Anthony Hoogs, Chenliang Xu
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Deep image classifiers have been found to learn biases from datasets. To mitigate the biases, most previous methods require labels of protected attributes (e.g., age, skin tone) as full-supervision, which has two limitations: 1) it is infeasible when the labels are unavailable; 2) they are incapable of mitigating unknown biases---biases that humans do not preconceive. To resolve those problems, we propose Debiasing Alternate Networks (DebiAN), which comprises two networks---a Discoverer and a Classifier. By training in an alternate manner, the discoverer tries to find multiple unknown biases of the classifier without any annotations of biases, and the classifier aims at unlearning the biases identified by the discoverer. While previous works evaluate debiasing results in terms of a single bias, we create Multi-Color MNIST dataset to better benchmark mitigation of multiple biases in a multi-bias setting, which not only reveals the problems in previous methods but also demonstrates the advantage of DebiAN in identifying and mitigating multiple biases simultaneously. We further conduct extensive experiments on real-world datasets, showing that the discoverer in DebiAN can identify unknown biases that may be hard to be found by humans. Regarding debiasing, DebiAN achieves strong bias mitigation performance."
Unsupervised and Semi-Supervised Bias Benchmarking in Face Recognition
Authors: Alexandra Chouldechova, Siqi Deng, Yongxin Wang, Wei Xia, Pietro Perona
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "We introduce Semi-supervised Performance Evaluation for Face Recognition (SPE-FR). SPE-FR is a statistical method for evaluating the performance and algorithmic bias of face verification systems when identity labels are unavailable or incomplete. The method is based on parametric Bayesian modeling of the face embedding similarity scores. SPE-FR produces point estimates, performance curves, and confidence bands that reflect uncertainty in the estimation procedure. Focusing on the unsupervised setting wherein no identity labels are available, we validate our method through experiments on a wide range of face embedding models and two publicly available evaluation datasets. Experiments show that SPE-FR can accurately assess performance on data with no identity labels, and confidently reveal demographic biases in system performance."
Towards Efficient Adversarial Training on Vision Transformers
Authors: Boxi Wu, Jindong Gu, Zhifeng Li, Deng Cai, Xiaofei He, Wei Liu
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Vision Transformer (ViT), as a powerful alternative to Convolutional Neural Network (CNN), has received much attention. Recent work showed that ViTs are also vulnerable to adversarial examples like CNNs. To build robust ViTs, an intuitive way is to apply adversarial training since it has been shown as one of the most effective ways to accomplish robust CNNs. However, one major limitation of adversarial training is its heavy computational cost. The self-attention mechanism adopted by ViTs is a computationally intense operation whose expense increases quadratically with the number of input patches, making adversarial training on ViTs even more time-consuming. In this work, we first comprehensively study fast adversarial training on a variety of vision transformers and illustrate the relationship between the efficiency and robustness. Then, to expediate adversarial training on ViTs, we propose an efficient Attention Guided Adversarial Training mechanism. Specifically, relying on the specialty of self-attention, we actively remove certain patch embeddings of each layer with an attention-guided dropping strategy during adversarial training. The slimmed self-attention modules accelerate the adversarial training on ViTs significantly. With only 65\% of the fast adversarial training time, we match the state-of-the-art results on the challenging ImageNet benchmark."
MIME: Minority Inclusion for Majority Group Enhancement of AI Performance
Authors: Pradyumna Chari, Yunhao Ba, Shreeram Athreya, Achuta Kadambi
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Several papers have rightly included minority groups in artificial intelligence (AI) training data to improve test inference for minority groups and/or society-at-large. A society-at-large consists of both minority and majority stakeholders. A common misconception is that minority inclusion does not increase performance for majority groups alone. In this paper, we make the surprising finding that including minority samples can improve test error for the majority group. In other words, minority group inclusion leads to majority group enhancements (MIME) in performance. A theoretical existence proof of the MIME effect is presented and found to be consistent with experimental results on six different datasets. Project webpage: https://visual.ee.ucla.edu/mime.htm/."
Studying Bias in GANs through the Lens of Race
Authors: Vongani H. Maluleke, Neerja Thakkar, Tim Brooks, Ethan Weber, Trevor Darrell, Alexei A. Efros, Angjoo Kanazawa, Devin Guillory
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "In this work, we study how the performance and evaluation of generative image models are impacted by the racial composition of the datasets upon which these models are trained. By examining and controlling the racial distributions in various training datasets, we are able to observe the impacts of different training distributions on generated image quality and the racial distributions of the generated images. Our results show that the racial compositions of generated images successfully preserve that of the training data. However, we observe that truncation, a technique used to generate higher quality images during inference, exacerbates racial imbalances in the data. Lastly, when examining the relationship between image quality and race, we find that the highest perceived visual quality images of a given race come from a distribution where that race is well-represented, and that annotators consistently prefer generated white faces over Black faces."
"Trust, but Verify: Using Self-Supervised Probing to Improve Trustworthiness"
Authors: Ailin Deng, Shen Li, Miao Xiong, Zhirui Chen, Bryan Hooi
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Trustworthy machine learning is of primary importance to the practical deployment of deep learning models. While state-of-the-art models achieve astonishingly good performance in terms of accuracy, recent literature reveals that their predictive confidence scores unfortunately cannot be trusted: e.g., they are often overconfident when wrong predictions are made, or so even for obvious outliers. In this paper, we introduce a new approach of \emph{self-supervised probing}, which enables us to check and mitigate the overconfidence issue for a trained model, thereby improving its trustworthiness. We provide a simple yet effective framework, which can be flexibly applied to existing trustworthiness-related methods in a plug-and-play manner. Extensive experiments on three trustworthiness-related tasks (misclassification detection, calibration and out-of-distribution detection) across various benchmarks verify the effectiveness of our proposed probing framework."
Learning to Censor by Noisy Sampling
Authors: Ayush Chopra, Abhinav Java, Abhishek Singh, Vivek Sharma, Ramesh Raskar
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Point clouds are an increasingly ubiquitous input modality and the raw signal can be efficiently processed with recent progress in deep learning. This signal may, often inadvertently, capture sensitive information that can leak semantic and geometric properties of the scene which the data owner does not want to share. The goal of this work is to protect sensitive information when learning from point clouds; by censoring signal before the point cloud is released for downstream tasks. Specifically, we focus on preserving utility for perception tasks while mitigating attribute leakage attacks. The key motivating insight is to leverage the localized saliency of perception tasks on point clouds to provide good privacy-utility trade-offs. We realize this through a mechanism called censoring by noisy sampling (CBNS), which is composed of two modules: i) Invariant Sampling: a differentiable point-cloud sampler which learns to remove points invariant to utility and ii) Noise Distortion: which learns to distort sampled points to decouple the sensitive information from utility, and mitigate privacy leakage. We validate the effectiveness of CBNS through extensive comparisons with state-of-the-art baselines and sensitivity analyses of key design choices. Results show that CBNS achieves superior privacy-utility trade-offs."