“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

We present a caricature generation framework based on shape and style manipulation using StyleGAN. Our framework, dubbed StyleCariGAN, automatically creates a realistic and detailed caricature from an input photo with optional controls on shape exaggeration degree and color stylization type. The key component of our method is shape exaggeration blocks that are used for modulating coarse layer feature maps of StyleGAN to produce desirable caricature shape exaggerations. We first build a layer-mixed StyleGAN for photo-to-caricature style conversion by swapping fine layers of the StyleGAN for photos to the corresponding layers of the StyleGAN trained to generate caricatures. Given an input photo, the layer-mixed model produces detailed color stylization for a caricature but without shape exaggerations. We then append shape exaggeration blocks to the coarse layers of the layer-mixed model and train the blocks to create shape exaggerations while preserving the characteristic appearances of the input. Experimental results show that our StyleCariGAN generates realistic and detailed caricatures compared to the current state-of-the-art methods. We demonstrate StyleCariGAN also supports other StyleGAN-based image manipulations, such as facial expression control.

StyleCariGAN: Caricature Generation via StyleGAN Feature Map Modulation

StyleCariGAN: caricature generation via StyleGAN feature map modulation

The purpose of few-shot recognition is to recognize novel categories with a limited number of labeled examples in each class. To encourage learning from a supplementary view, recent approaches have introduced auxiliary semantic modalities into effective metric-learning frameworks that aim to learn a feature similarity between training samples (support set) and test samples (query set). However, these approaches only augment the representations of samples with available semantics while ignoring the query set, which loses the potential for the improvement and may lead to a shift between the modalities combination and the pure-visual representation. In this paper, we devise an attributes-guided attention module (AGAM) to utilize human-annotated attributes and learn more discriminative features. This plug-and-play module enables visual contents and corresponding attributes to collectively focus on important channels and regions for the support set. And the feature selection is also achieved for query set with only visual information while the attributes are not available. Therefore, representations from both sets are improved in a fine-grained manner. Moreover, an attention alignment mechanism is proposed to distill knowledge from the guidance of attributes to the pure-visual branch for samples without attributes. Extensive experiments and analysis show that our proposed module can significantly improve simple metric-based approaches to achieve state-of-the-art performance on different datasets and settings.

Attributes-Guided and Pure-Visual Attention Alignment for Few-Shot Recognition

Few-shot learning aims to train an effective classifier in a small data regime. Due to the scarcity of training samples (usually as small as 1 or 5), traditional deep learning solutions often suffer from overfitting. To address this issue, an intuitive idea is to augment or hallucinate sufficient training data. For this purpose, in this paper, we propose a simple yet effective method to build a model for novel categories with few samples. Specifically, we assume that each category in the base set follows a Gaussian distribution, so that we can employ Maximum A Posteriori (MAP) to estimate the distribution of a novel category with even one example. To achieve this goal, we first transform each base category into Gaussian form with power transformation for MAP estimation. Then, we estimate the Gaussian mean of the novel category under the Gaussian prior given few samples from it. Finally, each novel category is represented by a unique Gaussian distribution, where sufficient trainable features can be sampled to obtain a highly accurate classifier for final predictions. Experimental results on four few-shot benchmarks show that it significantly outperforms the baseline methods on both 1- and 5-shot tasks. Extensive results on cross-domain tasks and visualization of estimated feature distribution also demonstrate its effectiveness.

Feature hallucination via Maximum A Posteriori for few-shot learning

Few-shot learning for visual recognition aims to adapt to novel unseen classes with only a few images. Recent work, especially the work based on low-level information, has achieved great progress. In these work, local representations (LRs) are typically employed, because LRs are more consistent among the seen and unseen classes. However, most of them are limited to an individual imageto-image or image-to-class measure manner, which cannot fully exploit the capabilities of LRs, especially in the context of a certain task. This paper proposes an Adaptive Task-aware Local Representations Network (ATL-Net) to address this limitation by introducing episodic attention, which can adaptively select the important local patches among the entire task, as the process of human recognition. We achieve much superior results on multiple benchmarks. On the miniImagenet, ATL-Net gains 0.93% and 0.88% improvements over the compared methods under the 5-way 1-shot and 5-shot settings. Moreover, ATL-Net can naturally tackle the problem that how to adaptively identify and weight the importance of different key local parts, which is the major concern of fine-grained recognition. Specifically, on the fine-grained dataset Stanford Dogs, ATL-Net outperforms the second best method with 5.39% and 9.69% gains under the 5way 1-shot and 5-shot settings.

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Learning Task-aware Local Representations for Few-shot Learning.

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LoFGAN: Fusing Local Representations for Few-Shot Image Generation

Caricature generation aims to translate real photos into caricatures with artistic styles and shape exaggerations while maintaining the identity of the subject. Different from generic image-to-image translation, drawing caricatures automatically is a more challenging task due to the existence of various spatial deformations. Previous caricature generation methods are obsessed with predicting definite image warping from a given photo while ignoring the intrinsic representation and distribution of geometric exaggerations in caricatures. This limits their ability on diverse exaggeration generation. In this paper, we generalize the caricature generation problem from instance-level warping prediction to distribution-level deformation modeling. Based on this assumption, we present the first exploration for unpaired CARIcature generation with Multiple Exaggerations (CariMe). Technically, we propose a Multi-exaggeration Warper network to learn the distribution-level mapping from photos to facial exaggerations. This makes it possible to generate diverse and reasonable exaggerations from randomly sampled warp codes given one input photo. To better represent the facial exaggeration and produce fine-grained warping, a deformation-field-based warping method is also proposed, which captures more detailed exaggerations than previous point-based warping methods. Experiments and two perceptual studies prove the superiority of our method comparing with other state-of-the-art methods, showing the improvement of our work on caricature generation.

CariMe: Unpaired Caricature Generation with Multiple Exaggerations

Caricature attributes provide distinctive facial features to help research in Psychology and Neuroscience. However, unlike the facial photo attribute datasets that have a quantity of annotated images, the annotations of caricature attributes are rare. To facility the research in attribute learning of caricatures, we propose a caricature attribute dataset, namely WebCariA. Moreover, to utilize models that trained by face attributes, we propose a novel unsupervised domain adaptation framework for cross-modality (i.e., photos to caricatures) attribute recognition, with an integrated inter- and intra-domain consistency learning scheme. Specifically, the inter-domain consistency learning scheme consisting an image-to-image translator to first fill the domain gap between photos and caricatures by generating intermediate image samples, and a label consistency learning module to align their semantic information. The intra-domain consistency learning scheme integrates the common feature consistency learning module with a novel attribute-aware attention-consistency learning module for a more efficient alignment. We did an extensive ablation study to show the effectiveness of the proposed method. And the proposed method also outperforms the state-of-the-art methods by a margin. The implementation of the proposed method is available at https://github.com/KeleiHe/DAAN.

Unsupervised Domain Attention Adaptation Network for Caricature Attribute Recognition

The goal of video based multi-modal person identification is to identify a person of interest using multi-modal video features, such as person's face, body, audio or head features. This task is challenging due to many factors, for example, variant body or face poses, poor face image quality, low frame resolution, etc. To address these problems, we propose a deep model ensemble framework, namely DeepMEF. Specifically, the proposed framework includes three novel modules, i.e., the video feature fusion module, the multi-modal feature fusion module and the model ensemble module. The first and second module form the basic deep model for ensemble, with the video feature fusion module fuses facial features from different frames as one. Then the multi-modal feature fusion module further fuses the face feature and features of other modalities for identification. In this work, we adopt the scene feature extracted by ourselves as the additional input of the multi-modal module. At last, the model ensemble module promotes the overall performance by combining the predictions of multiple multi-modal learners. The proposed method achieves a competitive result of 89.86% in mAP on the iQIYI-VID-2019 dataset, which helps us win the third place in the 2019 iQIYI Celebrity Video Identification Challenge.

Zheng Gu

Papers

Learning Task-aware Local Representations for Few-shot Learning.

LoFGAN: Fusing Local Representations for Few-Shot Image Generation

CariMe: Unpaired Caricature Generation with Multiple Exaggerations

Unsupervised Domain Attention Adaptation Network for Caricature Attribute Recognition

DeepMEF: A Deep Model Ensemble Framework for Video Based Multi-modal Person Identification