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Makeup Style Transfer on Low-Quality Images with Weighted Multi-Scale Attention

Daniel Organisciak, Edmond S. L. Ho, Shum Hubert P. H.

Auto-TLDR; Facial Makeup Style Transfer for Low-Resolution Images Using Multi-Scale Spatial Attention

Abstract Slides Poster

Facial makeup style transfer is an extremely challenging sub-field of image-to-image-translation. Due to this difficulty, state-of-the-art results are mostly reliant on the Face Parsing Algorithm, which segments a face into parts in order to easily extract makeup features. However, we find that this algorithm can only work well on high-definition images where facial features can be accurately extracted. Faces in many real-world photos, such as those including a large background or multiple people, are typically of low-resolution, which considerably hinders state-of-the-art algorithms. In this paper, we propose an end-to-end holistic approach to effectively transfer makeup styles between two low-resolution images. The idea is built upon a novel weighted multi-scale spatial attention module, which identifies salient pixel regions on low-resolution images in multiple scales, and uses channel attention to determine the most effective attention map. This design provides two benefits: low-resolution images are usually blurry to different extents, so a multi-scale architecture can select the most effective convolution kernel size to implement spatial attention; makeup is applied on both a macro-level (foundation, fake tan) and a micro-level (eyeliner, lipstick) so different scales can excel in extracting different makeup features. We develop an Augmented CycleGAN network that embeds our attention modules at selected layers to most effectively transfer makeup. We test our system with the FBD data set, which consists of many low-resolution facial images, and demonstrates that it outperforms state-of-the-art methods, particularly in transferring makeup for blurry images and partially occluded images.

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Identity-Preserved Face Beauty Transformation with Conditional Generative Adversarial Networks

Zhitong Huang, Ching Y Suen

Auto-TLDR; Identity-preserved face beauty transformation using conditional GANs

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Identity-preserved face beauty transformation aims to change the beauty scale of a face image while preserving the identity of the original face. In our framework of conditional Generative Adversarial Networks (cGANs), the synthesized face produced by the generator would have the same beauty scale indicated by the input condition. Unlike the discrete class labels used in most cGANs, the condition of target beauty scale in our framework is given by a continuous real-valued beauty score in the range [1 to 5], which makes the work challenging. To tackle the problem, we have implemented a triple structure, in which the conditional discriminator is divided into a normal discriminator and a separate face beauty predictor. We have also developed another new structure called Conditioned Instance Normalization to replace the original concatenation used in cGANs, which makes the combination of the input image and condition more effective. Furthermore, Self-Consistency Loss is introduced as a new parameter to improve the stability of training and quality of the generated image. In the end, the objectives of beauty transformation and identity preservation are evaluated by the pretrained face beauty predictor and state-of-the-art face recognition network. The result is encouraging and it also shows that certain facial features could be synthesized by the generator according to the target beauty scale, while preserving the original identity.

Unsupervised Face Manipulation Via Hallucination

Keerthy Kusumam, Enrique Sanchez, Georgios Tzimiropoulos

Auto-TLDR; Unpaired Face Image Manipulation using Autoencoders

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This paper addresses the problem of manipulatinga face image in terms of changing its pose. To achieve this, wepropose a new method that can be trained under the very general“unpaired” setting. To this end, we firstly propose to modelthe general appearance, layout and background of the inputimage using a low-resolution version of it which is progressivelypassed through a hallucination network to generate featuresat higher resolutions. We show that such a formulation issignificantly simpler than previous approaches for appearancemodelling based on autoencoders. Secondly, we propose a fullylearnable and spatially-aware appearance transfer module whichcan cope with misalignment between the input source image andthe target pose and can effectively combine the features fromthe hallucination network with the features produced by ourgenerator. Thirdly, we introduce an identity preserving methodthat is trained in an unsupervised way, by using an auxiliaryfeature extractor and a contrastive loss between the real andgenerated images. We compare our method against the state-of-the-art reporting significant improvements both quantitatively, interms of FID and IS, and qualitatively.

Cycle-Consistent Adversarial Networks and Fast Adaptive Bi-Dimensional Empirical Mode Decomposition for Style Transfer

Elissavet Batziou, Petros Alvanitopoulos, Konstantinos Ioannidis, Ioannis Patras, Stefanos Vrochidis, Ioannis Kompatsiaris

Auto-TLDR; FABEMD: Fast and Adaptive Bidimensional Empirical Mode Decomposition for Style Transfer on Images

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Recently, research endeavors have shown the potentiality of Cycle-Consistent Adversarial Networks (CycleGAN) in style transfer. In Cycle-Consistent Adversarial Networks, the consistency loss is introduced to measure the difference between the original images and the reconstructed in both directions, forward and backward. In this work, the combination of Cycle-Consistent Adversarial Networks with Fast and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD) is proposed to perform style transfer on images. In the proposed approach the cycle-consistency loss is modified to include the differences between the extracted Intrinsic Mode Functions (BIMFs) images. Instead of an estimation of pixel-to-pixel difference between the produced and input images, the FABEMD is applied and the extracted BIMFs are involved in the computation of the total cycle loss. This method enriches the computation of the total loss in a content-to-content and style-to-style comparison by connecting the spatial information to the frequency components. The experimental results reveal that the proposed method is efficient and produces qualitative results comparable to state-of-the-art methods.

Learning Disentangled Representations for Identity Preserving Surveillance Face Camouflage

Jingzhi Li, Lutong Han, Hua Zhang, Xiaoguang Han, Jingguo Ge, Xiaochu Cao

Auto-TLDR; Individual Face Privacy under Surveillance Scenario with Multi-task Loss Function

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In this paper, we focus on protecting the person face privacy under the surveillance scenarios, whose goal is to change the visual appearances of faces while keep them to be recognizable by current face recognition systems. This is a challenging problem as that we should retain the most important structures of captured facial images, while alter the salient facial regions to protect personal privacy. To address this problem, we introduce a novel individual face protection model, which can camouflage the face appearance from the perspective of human visual perception and preserve the identity features of faces used for face authentication. To that end, we develop an encoder-decoder network architecture that can separately disentangle the person feature representation into an appearance code and an identity code. Specifically, we first randomly divide the face image into two groups, the source set and the target set, where the source set is used to extract the identity code and the target set provides the appearance code. Then, we recombine the identity and appearance codes to synthesize a new face, which has the same identity with the source subject. Finally, the synthesized faces are used to replace the original face to protect the privacy of individual. Furthermore, our model is trained end-to-end with a multi-task loss function, which can better preserve the identity and stabilize the training loss. Experiments conducted on Cross-Age Celebrity dataset demonstrate the effectiveness of our model and validate our superiority in terms of visual quality and scalability.

Galaxy Image Translation with Semi-Supervised Noise-Reconstructed Generative Adversarial Networks

Qiufan Lin, Dominique Fouchez, Jérôme Pasquet

Auto-TLDR; Semi-supervised Image Translation with Generative Adversarial Networks Using Paired and Unpaired Images

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Image-to-image translation with Deep Learning neural networks, particularly with Generative Adversarial Networks (GANs), is one of the most powerful methods for simulating astronomical images. However, current work is limited to utilizing paired images with supervised translation, and there has been rare discussion on reconstructing noise background that encodes instrumental and observational effects. These limitations might be harmful for subsequent scientific applications in astrophysics. Therefore, we aim to develop methods for using unpaired images and preserving noise characteristics in image translation. In this work, we propose a two-way image translation model using GANs that exploits both paired and unpaired images in a semi-supervised manner, and introduce a noise emulating module that is able to learn and reconstruct noise characterized by high-frequency features. By experimenting on multi-band galaxy images from the Sloan Digital Sky Survey (SDSS) and the Canada France Hawaii Telescope Legacy Survey (CFHT), we show that our method recovers global and local properties effectively and outperforms benchmark image translation models. To our best knowledge, this work is the first attempt to apply semi-supervised methods and noise reconstruction techniques in astrophysical studies.

High Resolution Face Age Editing

Xu Yao, Gilles Puy, Alasdair Newson, Yann Gousseau, Pierre Hellier

Auto-TLDR; An Encoder-Decoder Architecture for Face Age editing on High Resolution Images

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Face age editing has become a crucial task in film post-production, and is also becoming popular for general purpose photography. Recently, adversarial training has produced some of the most visually impressive results for image manipulation, including the face aging/de-aging task. In spite of considerable progress, current methods often present visual artifacts and can only deal with low-resolution images. In order to achieve aging/de-aging with the high quality and robustness necessary for wider use, these problems need to be addressed. This is the goal of the present work. We present an encoder-decoder architecture for face age editing. The core idea of our network is to encode a face image to age-invariant features, and learn a modulation vector corresponding to a target age. We then combine these two elements to produce a realistic image of the person with the desired target age. Our architecture is greatly simplified with respect to other approaches, and allows for fine-grained age editing on high resolution images in a single unified model. Source codes are available at https://github.com/InterDigitalInc/HRFAE.

GarmentGAN: Photo-Realistic Adversarial Fashion Transfer

Amir Hossein Raffiee, Michael Sollami

Auto-TLDR; GarmentGAN: A Generative Adversarial Network for Image-Based Garment Transfer

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The garment transfer problem comprises two tasks: learning to separate a person's body (pose, shape, color) from their clothing (garment type, shape, style) and then generating new images of the wearer dressed in arbitrary garments. We present GarmentGAN, a new algorithm that performs image-based garment transfer through generative adversarial methods. The GarmentGAN framework allows users to virtually try-on items before purchase and generalizes to various apparel types. GarmentGAN requires as input only two images, namely, a picture of the target fashion item and an image containing the customer. The output is a synthetic image wherein the customer is wearing the target apparel. In order to make the generated image look photo-realistic, we employ the use of novel generative adversarial techniques. GarmentGAN improves on existing methods in the realism of generated imagery and solves various problems related to self-occlusions. Our proposed model incorporates additional information during training, utilizing both segmentation maps and body key-point information. We show qualitative and quantitative comparisons to several other networks to demonstrate the effectiveness of this technique.

Attentional Wavelet Network for Traditional Chinese Painting Transfer

Rui Wang, Huaibo Huang, Aihua Zheng, Ran He

Auto-TLDR; Attentional Wavelet Network for Photo to Chinese Painting Transfer

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Traditional Chinese paintings pay more attention to ’Gongbi’ and ’Xieyi’ in artworks, which raises a challenging task to generate Chinese paintings from photos. ’Xieyi’ creates high-level conception for paintings, while ’Gongbi’ refers to portraying local details in paintings. This paper proposes an attentional wavelet network for photo to Chinese painting transferring. We first introduce wavelets to obtain high-level conception and local details in Chinese paintings via 2-D haar wavelet transform. Moreover, we design high-level transform stream and local enhancement stream to dispose high frequencies and low frequency respectively. Furthermore, we exploit self-attention mechanism to compatibly pick up high-level information which is used to remedy the missing details when reconstructing the Chinese painting. To advance our experiment, we set up a new dataset named P2ADataset, with diverse photos and Chinese paintings on famous mountains around China. Experimental results comparing with the state-of-the-art style transferring algorithms verify the effectiveness of the proposed method. We will release the codes and data to the public.

Detail Fusion GAN: High-Quality Translation for Unpaired Images with GAN-Based Data Augmentation

Ling Li, Yaochen Li, Chuan Wu, Hang Dong, Peilin Jiang, Fei Wang

Auto-TLDR; Data Augmentation with GAN-based Generative Adversarial Network

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Image-to-image translation, a task to learn the mapping relation between two different domains, is a rapid-growing research field in deep learning. Although existing Generative Adversarial Network(GAN)-based methods have achieved decent results in this field, there are still some limitations in generating high-quality images for practical applications (e.g., data augmentation and image inpainting). In this work, we aim to propose a GAN-based network for data augmentation which can generate translated images with more details and less artifacts. The proposed Detail Fusion Generative Adversarial Network(DFGAN) consists of a detail branch, a transfer branch, a filter module, and a reconstruction module. The detail branch is trained by a super-resolution loss and its intermediate features can be used to introduce more details to the transfer branch by the filter module. Extensive evaluations demonstrate that our model generates more satisfactory images against the state-of-the-art approaches for data augmentation.

Local Facial Attribute Transfer through Inpainting

Ricard Durall, Franz-Josef Pfreundt, Janis Keuper

Auto-TLDR; Attribute Transfer Inpainting Generative Adversarial Network

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The term attribute transfer refers to the tasks of altering images in such a way, that the semantic interpretation of a given input image is shifted towards an intended direction, which is quantified by semantic attributes. Prominent example applications are photo realistic changes of facial features and expressions, like changing the hair color, adding a smile, enlarging the nose or altering the entire context of a scene, like transforming a summer landscape into a winter panorama. Recent advances in attribute transfer are mostly based on generative deep neural networks, using various techniques to manipulate images in the latent space of the generator. In this paper, we present a novel method for the common sub-task of local attribute transfers, where only parts of a face have to be altered in order to achieve semantic changes (e.g. removing a mustache). In contrast to previous methods, where such local changes have been implemented by generating new (global) images, we propose to formulate local attribute transfers as an inpainting problem. Removing and regenerating only parts of images, our Attribute Transfer Inpainting Generative Adversarial Network (ATI-GAN) is able to utilize local context information to focus on the attributes while keeping the background unmodified resulting in visually sound results.

Attributes Aware Face Generation with Generative Adversarial Networks

Zheng Yuan, Jie Zhang, Shiguang Shan, Xilin Chen

Auto-TLDR; AFGAN: A Generative Adversarial Network for Attributes Aware Face Image Generation

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Recent studies have shown remarkable success in face image generations. However, most of the existing methods only generate face images from random noise, and cannot generate face images according to the specific attributes. In this paper, we focus on the problem of face synthesis from attributes, which aims at generating faces with specific characteristics corresponding to the given attributes. To this end, we propose a novel attributes aware face image generator method with generative adversarial networks called AFGAN. Specifically, we firstly propose a two-path embedding layer and self-attention mechanism to convert binary attribute vector to rich attribute features. Then three stacked generators generate 64 * 64, 128 * 128 and 256 * 256 resolution face images respectively by taking the attribute features as input. In addition, an image-attribute matching loss is proposed to enhance the correlation between the generated images and input attributes. Extensive experiments on CelebA demonstrate the superiority of our AFGAN in terms of both qualitative and quantitative evaluations.

Attentive Hybrid Feature Based a Two-Step Fusion for Facial Expression Recognition

Jun Weng, Yang Yang, Zichang Tan, Zhen Lei

Auto-TLDR; Attentive Hybrid Architecture for Facial Expression Recognition

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Facial expression recognition is inherently a challenging task, especially for the in-the-wild images with various occlusions and large pose variations, which may lead to the loss of some crucial information. To address it, in this paper, we propose an attentive hybrid architecture (AHA) which learns global, local and integrated features based on different face regions. Compared with one type of feature, our extracted features own complementary information and can reduce the loss of crucial information. Specifically, AHA contains three branches, where all sub-networks in those branches employ the attention mechanism to further localize the interested pixels/regions. Moreover, we propose a two-step fusion strategy based on LSTM to deeply explore the hidden correlations among different face regions. Extensive experiments on four popular expression databases (i.e., CK+, FER-2013, SFEW 2.0, RAF-DB) show the effectiveness of the proposed method.

Multi-Domain Image-To-Image Translation with Adaptive Inference Graph

The Phuc Nguyen, Stéphane Lathuiliere, Elisa Ricci

Auto-TLDR; Adaptive Graph Structure for Multi-Domain Image-to-Image Translation

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In this work, we address the problem of multi-domain image-to-image translation with particular attention paid to computational cost. In particular, current state of the art models require a large and deep model in order to handle the visual diversity of multiple domains. In a context of limited computational resources, increasing the network size may not be possible. Therefore, we propose to increase the network capacity by using an adaptive graph structure. At inference time, the network estimates its own graph by selecting specific sub-networks. Sub-network selection is implemented using Gumble-Softmax in order to allow end-to-end training. This approach leads to an adjustable increase in number of parameters while preserving an almost constant computational cost. Our evaluation on two publicly available datasets of facial and painting images shows that our adaptive strategy generates better images with fewer artifacts than literature methods.

Selective Kernel and Motion-Emphasized Loss Based Attention-Guided Network for HDR Imaging of Dynamic Scenes

Yipeng Deng, Qin Liu, Takeshi Ikenaga

Auto-TLDR; SK-AHDRNet: A Deep Network with attention module and motion-emphasized loss function to produce ghost-free HDR images

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Ghost-like artifacts caused by ill-exposed and motion areas is one of the most challenging problems in high dynamic range (HDR) image reconstruction.When the motion range is small, previous methods based on optical flow or patch-match can suppress ghost-like artifacts by first aligning input images before merging them.However, they are not robust enough and still produce artifacts for challenging scenes where large foreground motions exist.To this end, we propose a deep network with attention module and motion-emphasized loss function to produce ghost-free HDR images. In attention module, we use channel and spatial attention to guide network to emphasize important components such as motion and saturated areas automatically. With the purpose of being robust to images with different resolutions and objects with distinct scale, we adopt the selective kernel network as the basic framework for channel attention. In addition to the attention module, the motion-emphasized loss function based on the motion and ill-exposed areas mask is designed to help network reconstruct motion areas. Experiments on the public dataset indicate that the proposed SK-AHDRNet produces ghost-free results where detail in ill-exposed areas is well recovered. The proposed method scores 43.17 with PSNR metric and 61.02 with HDR-VDP-2 metric on test which outperforms all conventional works. According to quantitative and qualitative evaluations, the proposed method can achieve state-of-the-art performance.

Detection of Makeup Presentation Attacks Based on Deep Face Representations

Christian Rathgeb, Pawel Drozdowski, Christoph Busch

Auto-TLDR; An Attack Detection Scheme for Face Recognition Using Makeup Presentation Attacks

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Facial cosmetics have the ability to substantially alter the facial appearance, which can negatively affect the decisions of a face recognition. In addition, it was recently shown that the application of makeup can be abused to launch so-called makeup presentation attacks. In such attacks, the attacker might apply heavy makeup in order to achieve the facial appearance of a target subject for the purpose of impersonation. In this work, we assess the vulnerability of a COTS face recognition system to makeup presentation attacks employing the publicly available Makeup Induced Face Spoofing (MIFS) database. It is shown that makeup presentation attacks might seriously impact the security of the face recognition system. Further, we propose an attack detection scheme which distinguishes makeup presentation attacks from genuine authentication attempts by analysing differences in deep face representations obtained from potential makeup presentation attacks and corresponding target face images. The proposed detection system employs a machine learning-based classifier, which is trained with synthetically generated makeup presentation attacks utilizing a generative adversarial network for facial makeup transfer in conjunction with image warping. Experimental evaluations conducted using the MIFS database reveal a detection equal error rate of 0.7% for the task of separating genuine authentication attempts from makeup presentation attacks.

Unsupervised Contrastive Photo-To-Caricature Translation Based on Auto-Distortion

Yuhe Ding, Xin Ma, Mandi Luo, Aihua Zheng, Ran He

Auto-TLDR; Unsupervised contrastive photo-to-caricature translation with style loss

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Photo-to-caricature aims to synthesize the caricature as a rendered image exaggerating the features through sketching, pencil strokes, or other artistic drawings. Style rendering and geometry deformation are the most important aspects in photo-to-caricature translation task. To take both into consideration, we propose an unsupervised contrastive photo-to-caricature translation architecture. Considering the intuitive artifacts in the existing methods, we propose a contrastive style loss for style rendering to enforce the similarity between the style of rendered photo and the caricature, and simultaneously enhance its discrepancy to the photos. To obtain an exaggerating deformation in an unpaired/unsupervised fashion, we propose a Distortion Prediction Module (DPM) to predict a set of displacements vectors for each input image while fixing some controlling points, followed by the thin plate spline interpolation for warping. The model is trained on unpaired photo and caricature while can offer bidirectional synthesizing via inputting either a photo or a caricature. Extensive experiments demonstrate that the proposed model is effective to generate hand-drawn like caricatures compared with existing competitors.

Multi-Laplacian GAN with Edge Enhancement for Face Super Resolution

Shanlei Ko, Bi-Ru Dai

Auto-TLDR; Face Image Super-Resolution with Enhanced Edge Information

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Face image super-resolution has become a research hotspot in the field of image processing. Nowadays, more and more researches add additional information, such as landmark, identity, to reconstruct high resolution images from low resolution ones, and have a good performance in quantitative terms and perceptual quality. However, these additional information is hard to obtain in many cases. In this work, we focus on reconstructing face images by extracting useful information from face images directly rather than using additional information. By observing edge information in each scale of face images, we propose a method to reconstruct high resolution face images with enhanced edge information. In additional, with the proposed training procedure, our method reconstructs photo-realistic images in upscaling factor 8x and outperforms state-of-the-art methods both in quantitative terms and perceptual quality.

Free-Form Image Inpainting Via Contrastive Attention Network

Xin Ma, Xiaoqiang Zhou, Huaibo Huang, Zhenhua Chai, Xiaolin Wei, Ran He

Auto-TLDR; Self-supervised Siamese inference for image inpainting

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Most deep learning based image inpainting approaches adopt autoencoder or its variants to fill missing regions in images. Encoders are usually utilized to learn powerful representational spaces, which are important for dealing with sophisticated learning tasks. Specifically, in the image inpainting task, masks with any shapes can appear anywhere in images (i.e., free-form masks) forming complex patterns. It is difficult for encoders to capture such powerful representations under this complex situation. To tackle this problem, we propose a self-supervised Siamese inference network to improve the robustness and generalization. Moreover, the restored image usually can not be harmoniously integrated into the exiting content, especially in the boundary area. To address this problem, we propose a novel Dual Attention Fusion module (DAF), which can combine both the restored and known regions in a smoother way and be inserted into decoder layers in a plug-and-play way. DAF is developed to not only adaptively rescale channel-wise features by taking interdependencies between channels into account but also force deep convolutional neural networks (CNNs) focusing more on unknown regions. In this way, the unknown region will be naturally filled from the outside to the inside. Qualitative and quantitative experiments on multiple datasets, including facial and natural datasets (i.e., Celeb-HQ, Pairs Street View, Places2 and ImageNet), demonstrate that our proposed method outperforms against state-of-the-arts in generating high-quality inpainting results.

SATGAN: Augmenting Age Biased Dataset for Cross-Age Face Recognition

Wenshuang Liu, Wenting Chen, Yuanlue Zhu, Linlin Shen

Auto-TLDR; SATGAN: Stable Age Translation GAN for Cross-Age Face Recognition

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In this paper, we propose a Stable Age Translation GAN (SATGAN) to generate fake face images at different ages to augment age biased face datasets for Cross-Age Face Recognition (CAFR) . The proposed SATGAN consists of both generator and discriminator. As a part of the generator, a novel Mask Attention Module (MAM) is introduced to make the generator focus on the face area. In addition, the generator employs a Uniform Distribution Discriminator (UDD) to supervise the learning of latent feature map and enforce the uniform distribution. Besides, the discriminator employs a Feature Separation Module (FSM) to disentangle identity information from the age information. The quantitative and qualitative evaluations on Morph dataset prove that SATGAN achieves much better performance than existing methods. The face recognition model trained using dataset (VGGFace2 and MS-Celeb-1M) augmented using our SATGAN achieves better accuracy on cross age dataset like Cross-Age LFW and AgeDB-30.

Mask-Based Style-Controlled Image Synthesis Using a Mask Style Encoder

Jaehyeong Cho, Wataru Shimoda, Keiji Yanai

Auto-TLDR; Style-controlled Image Synthesis from Semantic Segmentation masks using GANs

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In recent years, the advances in Generative Adversarial Networks (GANs) have shown impressive results for image generation and translation tasks. In particular, the image-to-image translation is a method of learning mapping from a source domain to a target domain and synthesizing an image. Image-to-image translation can be applied to a variety of tasks, making it possible to quickly and easily synthesize realistic images from semantic segmentation masks. However, in the existing image-to-image translation method, there is a limitation on controlling the style of the translated image, and it is not easy to synthesize an image by controlling the style of each mask element in detail. Therefore, we propose an image synthesis method that controls the style of each element by improving the existing image-to-image translation method. In the proposed method, we implement a style encoder that extracts style features for each mask element. The extracted style features are concatenated to the semantic mask in the normalization layer, and used the style-controlled image synthesis of each mask element. In experiments, we train style-controlled images synthesis using the datasets consisting of semantic segmentation masks and real images. The results show that the proposed method has excellent performance for style-controlled images synthesis for each element.

Age Gap Reducer-GAN for Recognizing Age-Separated Faces

Daksha Yadav, Naman Kohli, Mayank Vatsa, Richa Singh, Afzel Noore

Auto-TLDR; Generative Adversarial Network for Age-separated Face Recognition

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In this paper, we propose a novel algorithm for matching faces with temporal variations caused due to age progression. The proposed generative adversarial network algorithm is a unified framework which combines facial age estimation and age-separated face verification. The key idea of this approach is to learn the age variations across time by conditioning the input image on the subject's gender and the target age group to which the face needs to be progressed. The loss function accounts for reducing the age gap between the original image and generated face image as well as preserving the identity. Both visual fidelity and quantitative evaluations demonstrate the efficacy of the proposed architecture on different facial age databases for age-separated face recognition.

Exemplar Guided Cross-Spectral Face Hallucination Via Mutual Information Disentanglement

Haoxue Wu, Huaibo Huang, Aijing Yu, Jie Cao, Zhen Lei, Ran He

Auto-TLDR; Exemplar Guided Cross-Spectral Face Hallucination with Structural Representation Learning

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Recently, many Near infrared-visible (NIR-VIS) heterogeneous face recognition (HFR) methods have been proposed in the community. But it remains a challenging problem because of the sensing gap along with large pose variations. In this paper, we propose an Exemplar Guided Cross-Spectral Face Hallucination (EGCH) to reduce the domain discrepancy through disentangled representation learning. For each modality, EGCH contains a spectral encoder as well as a structure encoder to disentangle spectral and structure representation, respectively. It also contains a traditional generator that reconstructs the input from the above two representations, and a structure generator that predicts the facial parsing map from the structure representation. Besides, mutual information minimization and maximization are conducted to boost disentanglement and make representations adequately expressed. Then the translation is built on structure representations between two modalities. Provided with the transformed NIR structure representation and original VIS spectral representation, EGCH is capable to produce high-fidelity VIS images that preserve the topology structure of the input NIR while transfer the spectral information of an arbitrary VIS exemplar. Extensive experiments demonstrate that the proposed method achieves more promising results both qualitatively and quantitatively than the state-of-the-art NIR-VIS methods.

Continuous Learning of Face Attribute Synthesis

Ning Xin, Shaohui Xu, Fangzhe Nan, Xiaoli Dong, Weijun Li, Yuanzhou Yao

Auto-TLDR; Continuous Learning for Face Attribute Synthesis

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The generative adversarial network (GAN) exhibits great superiority in the face attribute synthesis task. However, existing methods have very limited effects on the expansion of new attributes. To overcome the limitations of a single network in new attribute synthesis, a continuous learning method for face attribute synthesis is proposed in this work. First, the feature vector of the input image is extracted and attribute direction regression is performed in the feature space to obtain the axes of different attributes. The feature vector is then linearly guided along the axis so that images with target attributes can be synthesized by the decoder. Finally, to make the network capable of continuous learning, the orthogonal direction modification module is used to extend the newly-added attributes. Experimental results show that the proposed method can endow a single network with the ability to learn attributes continuously, and, as compared to those produced by the current state-of-the-art methods, the synthetic attributes have higher accuracy.

Arbitrary Style Transfer with Parallel Self-Attention

Tiange Zhang, Ying Gao, Feng Gao, Lin Qi, Junyu Dong

Auto-TLDR; Self-Attention-Based Arbitrary Style Transfer Using Adaptive Instance Normalization

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Neural style transfer aims to create artistic images by synthesizing patterns from a given style image. Recently, the Adaptive Instance Normalization (AdaIN) layer is proposed to achieve real-time arbitrary style transfer. However, we observed that if crucial features based on AdaIN can be further emphasized during transfer, both content and style information will be better reflected in stylized images. Furthermore, it is always essential to preserve more details and reduce unexpected artifacts in order to generate appealing results. In this paper, we introduce an improved arbitrary style transfer method based on the self-attention mechanism. A self-attention module is designed to learn what and where to emphasize in the input image. In addition, an extra Laplacian loss is applied to preserve structure details of the content while eliminating artifacts. Experimental results demonstrate that the proposed method outperforms AdaIN and can generate more appealing results.

Efficient Shadow Detection and Removal Using Synthetic Data with Domain Adaptation

Rui Guo, Babajide Ayinde, Hao Sun

Auto-TLDR; Shadow Detection and Removal with Domain Adaptation and Synthetic Image Database

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In recent years, learning based shadow detection and removal approaches have shown prospects and, in most cases, yielded state-of-the-art results. The performance of these approaches, however, relies heavily on the construction of training database of shadow images, shadow-free versions, and shadow maps as ground truth. This conventional data gathering method is time-consuming, expensive, or even practically intractable to realize especially for outdoor scenes with complicated shadow patterns, thus limiting the size of the data available for training. In this paper, we leverage on large high quality synthetic image database and domain adaptation to eliminate the bottlenecks resulting from insufficient training samples and domain bias. Specifically, our approach utilizes adversarial training to predict near-pixel-perfect shadow map from synthetic shadow image for downstream shadow removal steps. At inference time, we capitalize on domain adaptation via image style transfer to map the style of real- world scene to that of synthetic scene for the purpose of detecting and subsequently removing shadow. Comprehensive experiments indicate that our approach outperforms state-of-the-art methods on select benchmark datasets.

Controllable Face Aging

Haien Zeng, Hanjiang Lai

Auto-TLDR; A controllable face aging method via attribute disentanglement generative adversarial network

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Motivated by the following two observations: 1) people are aging differently under different conditions for changeable facial attributes, e.g., skin color may become darker when working outside, and 2) it needs to keep some unchanged facial attributes during the aging process, e.g., race and gender, we propose a controllable face aging method via attribute disentanglement generative adversarial network. To offer fine control over the synthesized face images, first, an individual embedding of the face is directly learned from an image that contains the desired facial attribute. Second, since the image may contain other unwanted attributes, an attribute disentanglement network is used to separate the individual embedding and learn the common embedding that contains information about the face attribute (e.g., race). With the common embedding, we can manipulate the generated face image with the desired attribute in an explicit manner. Experimental results on two common benchmarks demonstrate that our proposed generator achieves comparable performance on the aging effect with state-of-the-art baselines while gaining more flexibility for attribute control. Code is available at supplementary material.

SIDGAN: Single Image Dehazing without Paired Supervision

Pan Wei, Xin Wang, Lei Wang, Ji Xiang, Zihan Wang

Auto-TLDR; DehazeGAN: An End-to-End Generative Adversarial Network for Image Dehazing

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Single image dehazing is challenging without scene airlight and transmission map. Most of existing dehazing algorithms tend to estimate key parameters based on manual designed priors or statistics, which may be invalid in some scenarios. Although deep learning-based dehazing methods provide an effective solution, most of them rely on paired training datasets, which are prohibitively difficult to be collected in real world. In this paper, we propose an effective end-to-end generative adversarial network for image dehazing, named DehazeGAN. The proposed DehazeGAN adopts a U-net architecture with a novel color-consistency loss derived from dark channel prior and perceptual loss, which can be trained in an unsupervised fashion without paired synthetic datasets. We create a RealHaze dataset for network training, including 4,000 outdoor hazy images and 4,000 haze-free images. Extensive experiments demonstrate that our proposed DehazeGAN achieves better performance than existing state-of-the-art methods on both synthetic datasets and real-world datasets in terms of PSNR, SSIM, and subjective visual experience.

Towards Artifacts-Free Image Defogging

Gabriele Graffieti, Davide Maltoni

Auto-TLDR; CurL-Defog: Learning Based Defogging with CycleGAN and HArD

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In this paper we present a novel defogging technique, named CurL-Defog, aimed at minimizing the creation of artifacts. The majority of learning based defogging approaches relies on paired data (i.e., the same images with and without fog), where fog is artificially added to clear images: this often provides good results on mildly fogged images but does not generalize well to real difficult cases. On the other hand, the models trained with real unpaired data (e.g. CycleGAN) can provide visually impressive results but often produce unwanted artifacts. In this paper we propose a curriculum learning strategy coupled with an enhanced CycleGAN model in order to reduce the number of produced artifacts, while maintaining state-of-the- art performance in terms of contrast enhancement and image reconstruction. We also introduce a new metric, called HArD (Hazy Artifact Detector) to numerically quantify the amount of artifacts in the defogged images, thus avoiding the tedious and subjective manual inspection of the results. The proposed approach compares favorably with state-of-the-art techniques on both real and synthetic datasets.

Boundary Guided Image Translation for Pose Estimation from Ultra-Low Resolution Thermal Sensor

Kohei Kurihara, Tianren Wang, Teng Zhang, Brian Carrington Lovell

Auto-TLDR; Pose Estimation on Low-Resolution Thermal Images Using Image-to-Image Translation Architecture

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This work addresses the pose estimation task on low-resolution images captured using thermal sensors which can operate in a no-light environment. Low-resolution thermal sensors have been widely adopted in various applications for cost control and privacy protection purposes. In this paper, targeting the challenging scenario of ultra-low resolution thermal imaging (3232 pixels), we aim to estimate human poses for the purpose of monitoring health conditions and indoor events. To overcome the challenges in ultra-low resolution thermal imaging such as blurred boundaries and data scarcity, we propose a new Image-to-Image (I2I) translation architecture which can translate the original blurred thermal image into a visible light image with sharper boundaries. Then the generated visible light image can be fed into the off-the-shelf pose estimator which was well-trained in the visible domain. Experimental results suggest that the proposed framework outperforms other state-of-the-art methods in the I2I based pose estimation task for our thermal image dataset. Furthermore, we also demonstrated the merits of the proposed method on the publicly available FLIR dataset by measuring the quality of translated images.

UCCTGAN: Unsupervised Clothing Color Transformation Generative Adversarial Network

Shuming Sun, Xiaoqiang Li, Jide Li

Auto-TLDR; An Unsupervised Clothing Color Transformation Generative Adversarial Network

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Clothing color transformation refers to changing the clothes color in an original image to the clothes color in a target image. In this paper, we propose an Unsupervised Clothing Color Transformation Generative Adversarial Network (UCCTGAN) for the task. UCCTGAN adopts the color histogram of a target clothes as color guidance and an improved U-net architecture called AntennaNet is put forward to fuse the extracted color information with the original image. Meanwhile, to accomplish unsupervised learning, the loss function is carefully designed according to color moment, which evaluates the chromatic aberration between the target clothing and the generated clothing. Experimental results show that our network has the ability to generate convincing color transformation results.

Local-Global Interactive Network for Face Age Transformation

Jie Song, Ping Wei, Huan Li, Yongchi Zhang, Nanning Zheng

Auto-TLDR; A Novel Local-Global Interaction Framework for Long-span Face Age Transformation

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Face age transformation, which aims to generate a face image in the past or future, has receiving increasing attention due to its significant application value in some special fields, such as looking for a lost child, tracking criminals and entertainment, etc. Currently, most existing methods mainly focus on unidirectional short-span face aging. In this paper, we propose a novel local-global interaction framework for long-span face age transformation. Firstly, we divide a face image into five independent parts and design a local generative network for each of them to learn the local structure changes of a face image, while we utilize a global generative network to learn the global structure changes. Then we introduce an interactive network and an age classification network, which are respectively used to integrate the local and global features and maintain the corresponding age features in different age groups. Given any face image at a certain age, our network can produce a clear and realistic image of face aging or rejuvenation. We test and evaluate the model on complex datasets, and extensive qualitative comparison experiments has proved the effectiveness and immense potential of our proposed method.

Semantic-Guided Inpainting Network for Complex Urban Scenes Manipulation

Pierfrancesco Ardino, Yahui Liu, Elisa Ricci, Bruno Lepri, Marco De Nadai

Auto-TLDR; Semantic-Guided Inpainting of Complex Urban Scene Using Semantic Segmentation and Generation

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Manipulating images of complex scenes to reconstruct, insert and/or remove specific object instances is a challenging task. Complex scenes contain multiple semantics and objects, which are frequently cluttered or ambiguous, thus hampering the performance of inpainting models. Conventional techniques often rely on structural information such as object contours in multi-stage approaches that generate unreliable results and boundaries. In this work, we propose a novel deep learning model to alter a complex urban scene by removing a user-specified portion of the image and coherently inserting a new object (e.g. car or pedestrian) in that scene. Inspired by recent works on image inpainting, our proposed method leverages the semantic segmentation to model the content and structure of the image, and learn the best shape and location of the object to insert. To generate reliable results, we design a new decoder block that combines the semantic segmentation and generation task to guide better the generation of new objects and scenes, which have to be semantically consistent with the image. Our experiments, conducted on two large-scale datasets of urban scenes (Cityscapes and Indian Driving), show that our proposed approach successfully address the problem of semantically-guided inpainting of complex urban scene.

An Unsupervised Approach towards Varying Human Skin Tone Using Generative Adversarial Networks

Debapriya Roy, Diganta Mukherjee, Bhabatosh Chanda

Auto-TLDR; Unsupervised Skin Tone Change Using Augmented Reality Based Models

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With the increasing popularity of augmented and virtual reality, retailers are now more focusing towards customer satisfaction to increase the amount of sales. Although augmented reality is not a new concept but it has gained its much needed attention over the past few years. Our present work is targeted towards this direction which may be used to enhance user experience in various virtual and augmented reality based applications. We propose a model to change skin tone of person. Given any input image of a person or a group of persons with some value indicating the desired change of skin color towards fairness or darkness, this method can change the skin tone of the persons in the image. This is an unsupervised method and also unconstrained in terms of pose, illumination, number of persons in the image etc. The goal of this work is to reduce the complexity in terms of time and effort which is generally needed for changing the skin tone using existing applications by professionals or novice. Rigorous experiments shows the efficacy of this method in terms of synthesizing perceptually convincing outputs.

Cascade Attention Guided Residue Learning GAN for Cross-Modal Translation

Bin Duan, Wei Wang, Hao Tang, Hugo Latapie, Yan Yan

Auto-TLDR; Cascade Attention-Guided Residue GAN for Cross-modal Audio-Visual Learning

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Since we were babies, we intuitively develop the ability to correlate the input from different cognitive sensors such as vision, audio, and text. However, in machine learning, this cross-modal learning is a nontrivial task because different modalities have no homogeneous properties. Previous works discover that there should be bridges among different modalities. From neurology and psychology perspective, humans have the capacity to link one modality with another one, e.g., associating a picture of a bird with the only hearing of its singing and vice versa. Is it possible for machine learning algorithms to recover the scene given the audio signal? In this paper, we propose a novel Cascade Attention-Guided Residue GAN (CAR-GAN), aiming at reconstructing the scenes given the corresponding audio signals. Particularly, we present a residue module to mitigate the gap between different modalities progressively. Moreover, a cascade attention guided network with a novel classification loss function is designed to tackle the cross-modal learning task. Our model keeps consistency in the high-level semantic label domain and is able to balance two different modalities. The experimental results demonstrate that our model achieves the state-of-the-art cross-modal audio-visual generation on the challenging Sub-URMP dataset.

Attention2AngioGAN: Synthesizing Fluorescein Angiography from Retinal Fundus Images Using Generative Adversarial Networks

Sharif Amit Kamran, Khondker Fariha Hossain, Alireza Tavakkoli, Stewart Lee Zuckerbrod

Auto-TLDR; Fluorescein Angiography from Fundus Images using Attention-based Generative Networks

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Fluorescein Angiography (FA) is a technique that employs the designated camera for Fundus photography incorporating excitation and barrier filters. FA also requires fluorescein dye that is injected intravenously, which might cause adverse effects ranging from nausea, vomiting to even fatal anaphylaxis. Currently, no other fast and non-invasive technique exists that can generate FA without coupling with Fundus photography. To eradicate the need for an invasive FA extraction procedure, we introduce an Attention-based Generative network that can synthesize Fluorescein Angiography from Fundus images. The proposed gan incorporates multiple attention based skip connections in generators and comprises novel residual blocks for both generators and discriminators. It utilizes reconstruction, feature-matching, and perceptual loss along with adversarial training to produces realistic Angiograms that is hard for experts to distinguish from real ones. Our experiments confirm that the proposed architecture surpasses recent state-of-the-art generative networks for fundus-to-angio translation task.

Directed Variational Cross-encoder Network for Few-Shot Multi-image Co-segmentation

Sayan Banerjee, Divakar Bhat S, Subhasis Chaudhuri, Rajbabu Velmurugan

Auto-TLDR; Directed Variational Inference Cross Encoder for Class Agnostic Co-Segmentation of Multiple Images

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In this paper, we propose a novel framework for class agnostic co-segmentation of multiple images using comparatively smaller datasets. We have developed a novel encoder-decoder network termed as DVICE (Directed Variational Inference Cross Encoder), which learns a continuous embedding space to ensure better similarity learning. We employ a combination of the proposed variational encoder-decoder and a novel few-shot learning approach to tackle the small sample size problem in co-segmentation. Furthermore, the proposed framework does not use any semantic class labels and is entirely class agnostic. Through exhaustive experimentation using a small volume of data over multiple datasets, we have demonstrated that our approach outperforms all existing state-of-the-art techniques.

Hybrid Approach for 3D Head Reconstruction: Using Neural Networks and Visual Geometry

Oussema Bouafif, Bogdan Khomutenko, Mohammed Daoudi

Auto-TLDR; Recovering 3D Head Geometry from a Single Image using Deep Learning and Geometric Techniques

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Recovering the 3D geometric structure of a face from a single input image is a challenging active research area in computer vision. In this paper, we present a novel method for reconstructing 3D heads from a single or multiple image(s) using a hybrid approach based on deep learning and geometric techniques. We propose an encoder-decoder network based on the U-net architecture and trained on synthetic data only. It predicts both pixel-wise normal vectors and landmarks maps from a single input photo. Landmarks are used for the pose computation and the initialization of the optimization problem, which, in turn, reconstructs the 3D head geometry by using a parametric morphable model and normal vector fields. State-of-the-art results are achieved through qualitative and quantitative evaluation tests on both single and multi-view settings. Despite the fact that the model was trained only on synthetic data, it successfully recovers 3D geometry and precise poses for real-world images.

DAPC: Domain Adaptation People Counting Via Style-Level Transfer Learning and Scene-Aware Estimation

Na Jiang, Xingsen Wen, Zhiping Shi

Auto-TLDR; Domain Adaptation People counting via Style-Level Transfer Learning and Scene-Aware Estimation

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People counting concentrates on predicting the number of people in surveillance images. It remains challenging due to the rich variations in scene type and crowd density. Besides, the limited closed-set with ground truth from reality significantly increase the difficulty of people counting in actual open-set. Targeting to solve these problems, this paper proposes a domain adaptation people counting via style-level transfer learning (STL) and scene-aware estimation (SAE). The style-level transfer learning explicitly leverages the style constraint and content similarity between images to learn effective knowledge transfer, which narrows the gap between closed-set and open-set by generating domain adaptation images. The scene-aware estimation introduces scene classifier to provide scene-aware weights for adaptively fusing density maps, which alleviates interference of variations in scene type and crowd density on domain adaptation people counting. Extensive experimental results demonstrate that images generated by STL are more suitable for domain adaptation learning and our proposed approach significantly outperforms the state-of-the-art methods on multiple cross-domain pairs.

Face Super-Resolution Network with Incremental Enhancement of Facial Parsing Information

Shuang Liu, Chengyi Xiong, Zhirong Gao

Auto-TLDR; Learning-based Face Super-Resolution with Incremental Boosting Facial Parsing Information

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Recently, facial priors based face super-resolution (SR) methods have obtained significant performance gains in dealing with extremely degraded facial images, and facial priors have also been proved useful in facilitating the inference of face images. Based on this, how to fully fuse facial priors into deep features to improve face SR performance has attracted a major attention. In this paper, we propose a learning-based face SR approach with incremental boosting facial parsing information (IFPSR) for high-magnification of low-resolution faces. The proposed IFPSR method consists of three main parts: i) a three-stage parsing map embedded features upsampling network, in which image recovery and prior estimation processes are performed simultaneously and progressively to improve the image resolution; ii) a progressive training method and a joint facial attention and heatmap loss to obtain better facial attributes; iii) the channel attention strategy in residual dense blocks to adaptively learn facial features. Extensive experimental results show that compared with the state-of-the-art methods in terms of quantitative and qualitative metrics, our approach can achieve an outstanding balance between SR image quality and low network complexity.

Unsupervised Learning of Landmarks Based on Inter-Intra Subject Consistencies

Weijian Li, Haofu Liao, Shun Miao, Le Lu, Jiebo Luo

Auto-TLDR; Unsupervised Learning for Facial Landmark Discovery using Inter-subject Landmark consistencies

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We present a novel unsupervised learning approach to image landmark discovery by incorporating the inter-subject landmark consistencies on facial images. This is achieved via an inter-subject mapping module that transforms original subject landmarks based on an auxiliary subject-related structure. To recover from the transformed images back to the original subject, the landmark detector is forced to learn spatial locations that contain the consistent semantic meanings both for the paired intra-subject images and between the paired inter-subject images. Our proposed method is extensively evaluated on two public facial image datasets (MAFL, AFLW) with various settings. Experimental results indicate that our method can extract the consistent landmarks for both datasets and achieve better performances compared to the previous state-of-the-art methods quantitatively and qualitatively.

Pose Variation Adaptation for Person Re-Identification

Lei Zhang, Na Jiang, Qishuai Diao, Yue Xu, Zhong Zhou, Wei Wu

Auto-TLDR; Pose Transfer Generative Adversarial Network for Person Re-identification

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Person re-identification (reid) plays an important role in surveillance video analysis, especially for criminal investigation and intelligent security. Although a large number of effective feature or distance metric learning approaches have been proposed, it still suffers from pedestrians appearance variations caused by pose changing. Most of the previous methods address this problem by learning a pose-invariant descriptor subspace. In this paper, we propose a pose variation adaptation method for person reid in the view of data augmentation. It can reduce the probability of deep learning network over-fitting. Specifically, we introduce a pose transfer generative adversarial network with a similarity measurement constraint. With the learned pose transfer model, training images can be pose-transferred to any given poses, and along with the original images, form a augmented training dataset. It increases data diversity against over-fitting. In contrast to previous GAN-based methods, we consider the influence of pose variations on similarity measure to generate more realistic and shaper samples for person reid. Besides, we optimize hard example mining to introduce a novel manner of samples (pose-transferred images) used with the learned pose transfer model. It focuses on the inferior samples which are caused by pose variations to increase the number of effective hard examples for learning discriminative features and improve the generalization ability. We extensively conduct comparative evaluations to demonstrate the advantages and superiority of our proposed method over the state-of-the-art approaches on Market-1501 and DukeMTMC-reID, the rank-1 accuracy is 96.1% for Market-1501 and 92.0% for DukeMTMC-reID.

Dual-Attention Guided Dropblock Module for Weakly Supervised Object Localization

Junhui Yin, Siqing Zhang, Dongliang Chang, Zhanyu Ma, Jun Guo

Auto-TLDR; Dual-Attention Guided Dropblock for Weakly Supervised Object Localization

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Attention mechanisms is frequently used to learn the discriminative features for better feature representations. In this paper, we extend the attention mechanism to the task of weakly supervised object localization (WSOL) and propose the dual-attention guided dropblock module (DGDM), which aims at learning the informative and complementary visual patterns for WSOL. This module contains two key components, the channel attention guided dropout (CAGD) and the spatial attention guided dropblock (SAGD). To model channel interdependencies, the CAGD ranks the channel attentions and treats the top-k attentions with the largest magnitudes as the important ones. It also keeps some low-valued elements to increase their value if they become important during training. The SAGD can efficiently remove the most discriminative information by erasing the contiguous regions of feature maps rather than individual pixels. This guides the model to capture the less discriminative parts for classification. Furthermore, it can also distinguish the foreground objects from the background regions to alleviate the attention misdirection. Experimental results demonstrate that the proposed method achieves new state-of-the-art localization performance.

RGB-Infrared Person Re-Identification Via Image Modality Conversion

Huangpeng Dai, Qing Xie, Yanchun Ma, Yongjian Liu, Shengwu Xiong

Auto-TLDR; CE2L: A Novel Network for Cross-Modality Re-identification with Feature Alignment

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As a cross modality retrieval task, RGB-infrared person re-identification(Re-ID) is an important and challenging tasking, because of its important role in video surveillance applications and large cross-modality variations between visible and infrared images. Most previous works addressed the problem of cross-modality gap with feature alignment by original feature representation learning straightly. In this paper, different from existing works, we propose a novel network(CE2L) to tackle the cross-modality gap with feature alignment. CE2L mainly focuses on adding discriminative information and learning robust features by converting modality between visible and infrared images. Its merits are highlighted in two aspects: 1)Using CycleGAN to convert infrared images into color images can not only increase the recognition characteristics of images, but also allow the our network to better learn the two modal image features; 2)Our novel method can serve as data augmentation. Specifically, it can increase data diversity and total data against over-fitting by converting labeled training images to another modal images. Extensive experimental results on two datasets demonstrate superior performance compared to the baseline and the state-of-the-art methods.

SAT-Net: Self-Attention and Temporal Fusion for Facial Action Unit Detection

Zhihua Li, Zheng Zhang, Lijun Yin

Auto-TLDR; Temporal Fusion and Self-Attention Network for Facial Action Unit Detection

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Research on facial action unit detection has shown remarkable performances by using deep spatial learning models in recent years, however, it is far from reaching its full capacity in learning due to the lack of use of temporal information of AUs across time. Since the AU occurrence in one frame is highly likely related to previous frames in a temporal sequence, exploring temporal correlation of AUs across frames becomes a key motivation of this work. In this paper, we propose a novel temporal fusion and AU-supervised self-attention network (a so-called SAT-Net) to address the AU detection problem. First of all, we input the deep features of a sequence into a convolutional LSTM network and fuse the previous temporal information into the feature map of the last frame, and continue to learn the AU occurrence. Second, considering the AU detection problem is a multi-label classification problem that individual label depends only on certain facial areas, we propose a new self-learned attention mask by focusing the detection of each AU on parts of facial areas through the learning of individual attention mask for each AU, thus increasing the AU independence without the loss of any spatial relations. Our extensive experiments show that the proposed framework achieves better results of AU detection over the state-of-the-arts on two benchmark databases (BP4D and DISFA).

Self and Channel Attention Network for Person Re-Identification

Asad Munir, Niki Martinel, Christian Micheloni

Auto-TLDR; SCAN: Self and Channel Attention Network for Person Re-identification

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Recent research has shown promising results for person re-identification by focusing on several trends. One is designing efficient metric learning loss functions such as triplet loss family to learn the most discriminative representations. The other is learning local features by designing part based architectures to form an informative descriptor from semantically coherent parts. Some efforts adjust distant outliers to their most similar positions by using soft attention and learn the relationship between distant similar features. However, only a few prior efforts focus on channel-wise dependencies and learn non-local sharp similar part features directly for the degraded data in the person re-identification task. In this paper, we propose a novel Self and Channel Attention Network (SCAN) to model long-range dependencies between channels and feature maps. We add multiple classifiers to learn discriminative global features by using classification loss. Self Attention (SA) module and Channel Attention (CA) module are introduced to model non-local and channel-wise dependencies in the learned features. Spectral normalization is applied to the whole network to stabilize the training process. Experimental results on the person re-identification benchmarks show the proposed components achieve significant improvement with respect to the baseline.

Two-Level Attention-Based Fusion Learning for RGB-D Face Recognition

Hardik Uppal, Alireza Sepas-Moghaddam, Michael Greenspan, Ali Etemad

Auto-TLDR; Fused RGB-D Facial Recognition using Attention-Aware Feature Fusion

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With recent advances in RGB-D sensing technologies as well as improvements in machine learning and fusion techniques, RGB-D facial recognition has become an active area of research. A novel attention aware method is proposed to fuse two image modalities, RGB and depth, for enhanced RGB-D facial recognition. The proposed method first extracts features from both modalities using a convolutional feature extractor. These features are then fused using a two layer attention mechanism. The first layer focuses on the fused feature maps generated by the feature extractor, exploiting the relationship between feature maps using LSTM recurrent learning. The second layer focuses on the spatial features of those maps using convolution. The training database is preprocessed and augmented through a set of geometric transformations, and the learning process is further aided using transfer learning from a pure 2D RGB image training process. Comparative evaluations demonstrate that the proposed method outperforms other state-of-the-art approaches, including both traditional and deep neural network-based methods, on the challenging CurtinFaces and IIIT-D RGB-D benchmark databases, achieving classification accuracies over 98.2% and 99.3% respectively. The proposed attention mechanism is also compared with other attention mechanisms, demonstrating more accurate results.

Augmented Cyclic Consistency Regularization for Unpaired Image-To-Image Translation

Takehiko Ohkawa, Naoto Inoue, Hirokatsu Kataoka, Nakamasa Inoue

Auto-TLDR; Augmented Cyclic Consistency Regularization for Unpaired Image-to-Image Translation

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Unpaired image-to-image (I2I) translation has received considerable attention in pattern recognition and computer vision because of recent advancements in generative adversarial networks (GANs). However, due to the lack of explicit supervision, unpaired I2I models often fail to generate realistic images, especially in challenging datasets with different backgrounds and poses. Hence, stabilization is indispensable for real-world applications and GANs. Herein, we propose Augmented Cyclic Consistency Regularization (ACCR), a novel regularization method for unpaired I2I translation. Our main idea is to enforce consistency regularization originating from semi-supervised learning on the discriminators leveraging real, fake, reconstructed, and augmented samples. We regularize the discriminators to output similar predictions when fed pairs of original and perturbed images. We qualitatively clarify the generation property between unpaired I2I models and standard GANs, and explain why consistency regularization on fake and reconstructed samples works well. Quantitatively, our method outperforms the consistency regularized GAN (CR-GAN) in real-world translations and demonstrates efficacy against several data augmentation variants and cycle-consistent constraints.

Global Image Sentiment Transfer

Jie An, Tianlang Chen, Songyang Zhang, Jiebo Luo

Auto-TLDR; Image Sentiment Transfer Using DenseNet121 Architecture

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Transferring the sentiment of an image is an unexplored research topic in computer vision. This work proposes a novel framework consisting of a reference image retrieval step and a global sentiment transfer step to transfer image sentiment according to a given sentiment tag. The proposed image retrieval algorithm is based on the SSIM index. The retrieved reference images by the proposed algorithm are more content-related than the algorithm based on the perceptual loss. Therefore, it can lead to a better image sentiment transfer result. In addition, we propose a global sentiment transfer step, which employs an optimization algorithm to iteratively transfer image sentiment based on the feature maps produced by the DenseNet121 architecture. The proposed sentiment transfer algorithm can transfer image sentiment while keeping the content of the input image intact. Both qualitative and quantitative evaluations demonstrate that the proposed sentiment transfer framework outperforms existing artistic and photo-realistic style transfer algorithms in producing satisfactory sentiment transfer results with ﬁne and exact details.