pwc-archive/papers-with-abstracts huggingface dataset:
FaceForensics++: Learning to Detect Manipulated Facial Images
Authors: Andreas Rossler,  Davide Cozzolino,  Luisa Verdoliva,  Christian Riess,  Justus Thies,  Matthias Niessner
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Face Swapping, Image Generation

The rapid progress in synthetic image generation and manipulation has now come to a point where it raises significant concerns for the implications towards society. At best, this leads to a loss of trust in digital content, but could potentially cause further harm by spreading false information or fake news. This paper examines the realism of state-of-the-art image manipulations, and how difficult it is to detect them, either automatically or by humans. To standardize the evaluation of detection methods, we propose an automated benchmark for facial manipulation detection. In particular, the benchmark is based on Deep-Fakes, Face2Face, FaceSwap and NeuralTextures as prominent representatives for facial manipulations at random compression level and size. The benchmark is publicly available and contains a hidden test set as well as a database of over 1.8 million manipulated images. This dataset is over an order of magnitude larger than comparable, publicly available, forgery datasets. Based on this data, we performed a thorough analysis of data-driven forgery detectors. We show that the use of additional domain-specific knowledge improves forgery detection to unprecedented accuracy, even in the presence of strong compression, and clearly outperforms human observers.
DeepVCP: An End-to-End Deep Neural Network for Point Cloud Registration
Authors: Weixin Lu,  Guowei Wan,  Yao Zhou,  Xiangyu Fu,  Pengfei Yuan,  Shiyu Song
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Point Cloud Registration

We present DeepVCP - a novel end-to-end learning-based 3D point cloud registration framework that achieves comparable registration accuracy to prior state-of-the-art geometric methods. Different from other keypoint based methods where a RANSAC procedure is usually needed, we implement the use of various deep neural network structures to establish an end-to-end trainable network. Our keypoint detector is trained through this end-to-end structure and enables the system to avoid the interference of dynamic objects, leverages the help of sufficiently salient features on stationary objects, and as a result, achieves high robustness. Rather than searching the corresponding points among existing points, the key contribution is that we innovatively generate them based on learned matching probabilities among a group of candidates, which can boost the registration accuracy. We comprehensively validate the effectiveness of our approach using both the KITTI dataset and the Apollo-SouthBay dataset. Results demonstrate that our method achieves comparable registration accuracy and runtime efficiency to the state-of-the-art geometry-based methods, but with higher robustness to inaccurate initial poses. Detailed ablation and visualization analysis are included to further illustrate the behavior and insights of our network. The low registration error and high robustness of our method make it attractive to the substantial applications relying on the point cloud registration task.
Shape Reconstruction Using Differentiable Projections and Deep Priors
Authors: Matheus Gadelha,  Rui Wang,  Subhransu Maji
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Shape Reconstruction

We investigate the problem of reconstructing shapes from noisy and incomplete projections in the presence of viewpoint uncertainities. The problem is cast as an optimization over the shape given measurements obtained by a projection operator and a prior. We present differentiable projection operators for a number of reconstruction problems which when combined with the deep image prior or shape prior allows efficient inference through gradient descent. We apply our method on a variety of reconstruction problems, such as tomographic reconstruction from a few samples, visual hull reconstruction incorporating view uncertainties, and 3D shape reconstruction from noisy depth maps. Experimental results show that our approach is effective for such shape reconstruction problems, without requiring any task-specific training.
Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization
Authors: Mans Larsson,  Erik Stenborg,  Carl Toft,  Lars Hammarstrand,  Torsten Sattler,  Fredrik Kahl
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Autonomous Driving, Segmentation, Visual Localization

Long-term visual localization is the problem of estimating the camera pose of a given query image in a scene whose appearance changes over time. It is an important problem in practice that is, for example, encountered in autonomous driving. In order to gain robustness to such changes, long-term localization approaches often use segmantic segmentations as an invariant scene representation, as the semantic meaning of each scene part should not be affected by seasonal and other changes. However, these representations are typically not very discriminative due to the very limited number of available classes. In this paper, we propose a novel neural network, the Fine-Grained Segmentation Network (FGSN), that can be used to provide image segmentations with a larger number of labels and can be trained in a self-supervised fashion. In addition, we show how FGSNs can be trained to output consistent labels across seasonal changes. We show through extensive experiments that integrating the fine-grained segmentations produced by our FGSNs into existing localization algorithms leads to substantial improvements in localization performance.
SANet: Scene Agnostic Network for Camera Localization
Authors: Luwei Yang,  Ziqian Bai,  Chengzhou Tang,  Honghua Li,  Yasutaka Furukawa,  Ping Tan
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Camera Localization

This paper presents a scene agnostic neural architecture for camera localization, where model parameters and scenes are independent from each other.Despite recent advancement in learning based methods, most approaches require training for each scene one by one, not applicable for online applications such as SLAM and robotic navigation, where a model must be built on-the-fly.Our approach learns to build a hierarchical scene representation and predicts a dense scene coordinate map of a query RGB image on-the-fly given an arbitrary scene. The 6D camera pose of the query image can be estimated with the predicted scene coordinate map. Additionally, the dense prediction can be used for other online robotic and AR applications such as obstacle avoidance. We demonstrate the effectiveness and efficiency of our method on both indoor and outdoor benchmarks, achieving state-of-the-art performance.
Hierarchical Self-Attention Network for Action Localization in Videos
Authors: Rizard Renanda Adhi Pramono,  Yie-Tarng Chen,  Wen-Hsien Fang
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Action Detection, Action Localization

This paper presents a novel Hierarchical Self-Attention Network (HISAN) to generate spatial-temporal tubes for action localization in videos. The essence of HISAN is to combine the two-stream convolutional neural network (CNN) with hierarchical bidirectional self-attention mechanism, which comprises of two levels of bidirectional self-attention to efficaciously capture both of the long-term temporal dependency information and spatial context information to render more precise action localization. Also, a sequence rescoring (SR) algorithm is employed to resolve the dilemma of inconsistent detection scores incurred by occlusion or background clutter. Moreover, a new fusion scheme is invoked, which integrates not only the appearance and motion information from the two-stream network, but also the motion saliency to mitigate the effect of camera motion. Simulations reveal that the new approach achieves competitive performance as the state-of-the-art works in terms of action localization and recognition accuracy on the widespread UCF101-24 and J-HMDB datasets.
DewarpNet: Single-Image Document Unwarping With Stacked 3D and 2D Regression Networks
Authors: Sagnik Das,  Ke Ma,  Zhixin Shu,  Dimitris Samaras,  Roy Shilkrot
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D geometry, Local Distortion, MS-SSIM, Optical Character Recognition (OCR), regression, SSIM

Capturing document images with hand-held devices in unstructured environments is a common practice nowadays. However, "casual" photos of documents are usually unsuitable for automatic information extraction, mainly due to physical distortion of the document paper, as well as various camera positions and illumination conditions. In this work, we propose DewarpNet, a deep-learning approach for document image unwarping from a single image. Our insight is that the 3D geometry of the document not only determines the warping of its texture but also causes the illumination effects. Therefore, our novelty resides on the explicit modeling of 3D shape for document paper in an end-to-end pipeline. Also, we contribute the largest and most comprehensive dataset for document image unwarping to date - Doc3D. This dataset features multiple ground-truth annotations, including 3D shape, surface normals, UV map, albedo image, etc. Training with Doc3D, we demonstrate state-of-the-art performance for DewarpNet with extensive qualitative and quantitative evaluations. Our network also significantly improves OCR performance on captured document images, decreasing character error rate by 42% on average. Both the code and the dataset are released.
Learning Robust Facial Landmark Detection via Hierarchical Structured Ensemble
Authors: Xu Zou,  Sheng Zhong,  Luxin Yan,  Xiangyun Zhao,  Jiahuan Zhou,  Ying Wu
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Face Alignment, Facial Landmark Detection

Heatmap regression-based models have significantly advanced the progress of facial landmark detection. However, the lack of structural constraints always generates inaccurate heatmaps resulting in poor landmark detection performance. While hierarchical structure modeling methods have been proposed to tackle this issue, they all heavily rely on manually designed tree structures. The designed hierarchical structure is likely to be completely corrupted due to the missing or inaccurate prediction of landmarks. To the best of our knowledge, in the context of deep learning, no work before has investigated how to automatically model proper structures for facial landmarks, by discovering their inherent relations. In this paper, we propose a novel Hierarchical Structured Landmark Ensemble (HSLE) model for learning robust facial landmark detection, by using it as the structural constraints. Different from existing approaches of manually designing structures, our proposed HSLE model is constructed automatically via discovering the most robust patterns so HSLE has the ability to robustly depict both local and holistic landmark structures simultaneously. Our proposed HSLE can be readily plugged into any existing facial landmark detection baselines for further performance improvement. Extensive experimental results demonstrate our approach significantly outperforms the baseline by a large margin to achieve a state-of-the-art performance.
Generative Adversarial Training for Weakly Supervised Cloud Matting
Authors: Zhengxia Zou,  Wenyuan Li,  Tianyang Shi,  Zhenwei Shi,  Jieping Ye
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Cloud Detection, Earth Observation, Image Matting, Semantic Segmentation

The detection and removal of cloud in remote sensing images are essential for earth observation applications. Most previous methods consider cloud detection as a pixel-wise semantic segmentation process (cloud v.s. background), which inevitably leads to a category-ambiguity problem when dealing with semi-transparent clouds. We re-examine the cloud detection under a totally different point of view, i.e. to formulate it as a mixed energy separation process between foreground and background images, which can be equivalently implemented under an image matting paradigm with a clear physical significance. We further propose a generative adversarial framework where the training of our model neither requires any pixel-wise ground truth reference nor any additional user interactions. Our model consists of three networks, a cloud generator G, a cloud discriminator D, and a cloud matting network F, where G and D aim to generate realistic and physically meaningful cloud images by adversarial training, and F learns to predict the cloud reflectance and attenuation. Experimental results on a global set of satellite images demonstrate that our method, without ever using any pixel-wise ground truth during training, achieves comparable and even higher accuracy over other fully supervised methods, including some recent popular cloud detectors and some well-known semantic segmentation frameworks.
Instance-Guided Context Rendering for Cross-Domain Person Re-Identification
Authors: Yanbei Chen,  Xiatian Zhu,  Shaogang Gong
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Generative Adversarial Network, Image Generation, Person Re-Identification, Unsupervised Domain Adaptation

Existing person re-identification (re-id) methods mostly assume the availability of large-scale identity labels for model learning in any target domain deployment. This greatly limits their scalability in practice. To tackle this limitation, we propose a novel Instance-Guided Context Rendering scheme, which transfers the source person identities into diverse target domain contexts to enable supervised re-id model learning in the unlabelled target domain. Unlike previous image synthesis methods that transform the source person images into limited fixed target styles, our approach produces more visually plausible, and diverse synthetic training data. Specifically, we formulate a dual conditional generative adversarial network that augments each source person image with rich contextual variations. To explicitly achieve diverse rendering effects, we leverage abundant unlabelled target instances as contextual guidance for image generation. Extensive experiments on Market-1501, DukeMTMC-reID and CUHK03 benchmarks show that the re-id performance can be significantly improved when using our synthetic data in cross-domain re-id model learning.
Instance-Level Future Motion Estimation in a Single Image Based on Ordinal Regression
Authors: Kyung-Rae Kim,  Whan Choi,  Yeong Jun Koh,  Seong-Gyun Jeong,  Chang-Su Kim
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Motion Estimation, Multi-Object Tracking, Object Tracking, regression

A novel algorithm to estimate instance-level future motion in a single image is proposed in this paper. We first represent the future motion of an instance with its direction, speed, and action classes. Then, we develop a deep neural network that exploits different levels of semantic information to perform the future motion estimation. For effective future motion classification, we adopt ordinal regression. Especially, we develop the cyclic ordinal regression scheme using binary classifiers. Experiments demonstrate that the proposed algorithm provides reliable performance and thus can be used effectively for vision applications, including single and multi object tracking. Furthermore, we release the future motion (FM) dataset, collected from diverse sources and annotated manually, as a benchmark for single-image future motion estimation.
Towards Latent Attribute Discovery From Triplet Similarities
Authors: Ishan Nigam,  Pavel Tokmakov,  Deva Ramanan
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Attribute, Triplet

This paper addresses the task of learning latent attributes from triplet similarity comparisons. Consider, for instance, the three shoes in Fig. 1(a). They can be compared according to color, comfort, size, or shape resulting in different rankings. Most approaches for embedding learning either make a simplifying assumption - that all inputs are comparable under a single criterion, or require expensive attribute supervision. We introduce Latent Similarity Networks (LSNs): a simple and effective technique to discover the underlying latent notions of similarity in data without any explicit attribute supervision. LSNs can be trained with standard triplet supervision and learn several latent embeddings that can be used to compare images under multiple notions of similarity. LSNs achieve state-of-the-art performance on UT-Zappos-50k Shoes and Celeb-A Faces datasets and also demonstrate the ability to uncover meaningful latent attributes.
Automatic and Robust Skull Registration Based on Discrete Uniformization
Authors: Junli Zhao,  Xin Qi,  Chengfeng Wen,  Na Lei,  Xianfeng Gu
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10

 Skull registration plays a fundamental role in forensic science and is crucial for craniofacial reconstruction. The complicated topology, lack of anatomical features, and low quality reconstructed mesh make skull registration challenging. In this work, we propose an automatic skull registration method based on the discrete uniformization theory, which can handle complicated topologies and is robust to low quality meshes. We apply dynamic Yamabe flow to realize discrete uniformization, which modifies the mesh combinatorial structure during the flow and conformally maps the multiply connected skull surface onto a planar disk with circular holes. The 3D surfaces can be registered by matching their planar images using harmonic maps. This method is rigorous with theoretic guarantee, automatic without user intervention, and robust to low mesh quality. Our experimental results demonstrate the efficiency and efficacy of the method.
Few-Shot Image Recognition With Knowledge Transfer
Authors: Zhimao Peng,  Zechao Li,  Junge Zhang,  Yan Li,  Guo-Jun Qi,  Jinhui Tang
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Transfer Learning

Human can well recognize images of novel categories just after browsing few examples of these categories. One possible reason is that they have some external discriminative visual information about these categories from their prior knowledge. Inspired from this, we propose a novel Knowledge Transfer Network architecture (KTN) for few-shot image recognition. The proposed KTN model jointly incorporates visual feature learning, knowledge inferring and classifier learning into one unified framework for their optimal compatibility. First, the visual classifiers for novel categories are learned based on the convolutional neural network with the cosine similarity optimization. To fully explore the prior knowledge, a semantic-visual mapping network is then developed to conduct knowledge inference, which enables to infer the classifiers for novel categories from base categories. Finally, we design an adaptive fusion scheme to infer the desired classifiers by effectively integrating the above knowledge and visual information. Extensive experiments are conducted on two widely-used Mini-ImageNet and ImageNet Few-Shot benchmarks to evaluate the effectiveness of the proposed method. The results compared with the state-of-the-art approaches show the encouraging performance of the proposed method, especially on 1-shot and 2-shot tasks.
Pose-Guided Feature Alignment for Occluded Person Re-Identification
Authors: Jiaxu Miao,  Yu Wu,  Ping Liu,  Yuhang Ding,  Yi Yang
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Occluded Person Re-Identification, Person Re-Identification, Person Retrieval, Retrieval

Persons are often occluded by various obstacles in person retrieval scenarios. Previous person re-identification (re-id) methods, either overlook this issue or resolve it based on an extreme assumption. To alleviate the occlusion problem, we propose to detect the occluded regions, and explicitly exclude those regions during feature generation and matching. In this paper, we introduce a novel method named Pose-Guided Feature Alignment (PGFA), exploiting pose landmarks to disentangle the useful information from the occlusion noise. During the feature constructing stage, our method utilizes human landmarks to generate attention maps. The generated attention maps indicate if a specific body part is occluded and guide our model to attend to the non-occluded regions. During matching, we explicitly partition the global feature into parts and use the pose landmarks to indicate which partial features belonging to the target person. Only the visible regions are utilized for the retrieval. Besides, we construct a large-scale dataset for the Occluded Person Re-ID problem, namely Occluded-DukeMTMC, which is by far the largest dataset for the Occlusion Person Re-ID. Extensive experiments are conducted on our constructed occluded re-id dataset, two partial re-id datasets, and two commonly used holistic re-id datasets. Our method largely outperforms existing person re-id methods on three occlusion datasets, while remains top performance on two holistic datasets.
Robust Person Re-Identification by Modelling Feature Uncertainty
Authors: Tianyuan Yu,  Da Li,  Yongxin Yang,  Timothy M. Hospedales,  Tao Xiang
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Person Re-Identification

We aim to learn deep person re-identification (ReID) models that are robust against noisy training data. Two types of noise are prevalent in practice: (1) label noise caused by human annotator errors and (2) data outliers caused by person detector errors or occlusion. Both types of noise pose serious problems for training ReID models, yet have been largely ignored so far. In this paper, we propose a novel deep network termed DistributionNet for robust ReID. Instead of representing each person image as a feature vector, DistributionNet models it as a Gaussian distribution with its variance representing the uncertainty of the extracted features. A carefully designed loss is formulated in DistributionNet to unevenly allocate uncertainty across training samples. Consequently, noisy samples are assigned large variance/uncertainty, which effectively alleviates their negative impacts on model fitting. Extensive experiments demonstrate that our model is more effective than alternative noise-robust deep models. The source code is available at: https://github.com/TianyuanYu/DistributionNet
Co-Segmentation Inspired Attention Networks for Video-Based Person Re-Identification
Authors: Arulkumar Subramaniam,  Athira Nambiar,  Anurag Mittal
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Person Re-Identification, Segmentation, Video-Based Person Re-Identification

Person re-identification (Re-ID) is an important real-world surveillance problem that entails associating a person's identity over a network of cameras. Video-based Re-ID approaches have gained significant attention recently since a video, and not just an image, is often available. In this work, we propose a novel Co-segmentation inspired video Re-ID deep architecture and formulate a Co-segmentation based Attention Module (COSAM) that activates a common set of salient features across multiple frames of a video via mutual consensus in an unsupervised manner. As opposed to most of the prior work, our approach is able to attend to person accessories along with the person. Our plug-and-play and interpretable COSAM module applied on two deep architectures (ResNet50, SE-ResNet50) outperform the state-of-the-art methods on three benchmark datasets.