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Learning Progressive Point Embeddings for 3D Point Cloud Generation

Learning Progressive Point Embeddings for 3D Point Cloud Gen...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Wen, Cheng Yu, Baosheng Tao, Dacheng Univ Sydney Fac Engn Sch Comp Sci 6 Cleveland St Darlington NSW 2008 Australia

ISBN: (纸本)9781665445092

Generative models for 3D point clouds are extremely important for scene/object reconstruction applications in autonomous driving and robotics. Despite recent success of deep learning-based representation learning, it remains a great challenge for deep neural networks to synthesize or reconstruct high-fidelity point clouds, because of the difficulties in 1) learning effective pointwise representations;and 2) generating realistic point clouds from complex distributions. In this paper, we devise a dual-generators framework for point cloud generation, which generalizes vanilla generative adversarial learning framework in a progressive manner. Specifically, the first generator aims to learn effective point embeddings in a breadth-first manner, while the second generator is used to refine the generated point cloud based on a depth-first point embedding to generate a robust and uniform point cloud. The proposed dual-generators framework thus is able to progressively learn effective point embeddings for accurate point cloud generation. Experimental results on a variety of object categories from the most popular point cloud generation dataset, ShapeNet, demonstrate the state-of-the-art performance of the proposed method for accurate point cloud generation.

关键词： Deep learning Solid modeling computer vision Three-dimensional displays Generators Adversarial machine learning pattern recognition

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Learning To Count Everything

Learning To Count Everything

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Ranjan, Viresh Sharma, Udbhav Thu Nguyen Hoai, Minh SUNY Stony Brook Stony Brook NY 11794 USA VinAI Res Hanoi Vietnam

ISBN: (纸本)9781665445092

Existing works on visual counting primarily focus on one specific category at a time, such as people, animals, and cells. In this paper, we are interested in counting everything, that is to count objects from any category given only a few annotated instances from that category. To this end, we pose counting as a few-shot regression task. To tackle this task, we present a novel method that takes a query image together with a few exemplar objects from the query image and predicts a density map for the presence of all objects of interest in the query image. We also present a novel adaptation strategy to adapt our network to any novel visual category at test time, using only a few exemplar objects from the novel category. We also introduce a dataset of 147 object categories containing over 6000 images that are suitable for the few-shot counting task. The images are annotated with two types of annotation, dots and bounding boxes, and they can be used for developing few-shot counting models. Experiments on this dataset shows that our method outperforms several state-of-the-art object detectors and few-shot counting approaches. Our code and dataset can be found at https://***/cvlab-stonybrook/LearningToCountEverything.

关键词： Visualization computer vision Codes Annotations Animals Detectors pattern recognition

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Enhancing the Transferability of Adversarial Attacks through Variance Tuning

Enhancing the Transferability of Adversarial Attacks through...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Wang, Xiaosen He, Kun Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9781665445092

Deep neural networks are vulnerable to adversarial examples that mislead the models with imperceptible perturbations. Though adversarial attacks have achieved incredible success rates in the white-box setting, most existing adversaries often exhibit weak transferability in the black-box setting, especially under the scenario of attacking models with defense mechanisms. In this work, we propose a new method called variance tuning to enhance the class of iterative gradient based attack methods and improve their attack transferability. Specifically, at each iteration for the gradient calculation, instead of directly using the current gradient for the momentum accumulation, we further consider the gradient variance of the previous iteration to tune the current gradient so as to stabilize the update direction and escape from poor local optima. Empirical results on the standard ImageNet dataset demonstrate that our method could significantly improve the transferability of gradient-based adversarial attacks. Besides, our method could be used to attack ensemble models or be integrated with various input transformations. Incorporating variance tuning with input transformations on iterative gradient-based attacks in the multi-model setting, the integrated method could achieve an average success rate of 90.1% against nine advanced defense methods, improving the current best attack performance significantly by 85.1%.

关键词： Deep learning computer vision Codes Perturbation methods Computational modeling pattern recognition Iterative methods

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KOALAnet: Blind Super-Resolution using Kernel-Oriented Adaptive Local Adjustment

KOALAnet: Blind Super-Resolution using Kernel-Oriented Adapt...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Kim, Soo Ye Sim, Hyeonjun Kim, Munchurl Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665445092

Blind super-resolution (SR) methods aim to generate a high quality high resolution image from a low resolution image containing unknown degradations. However, natural images contain various types and amounts of blur: some may be due to the inherent degradation characteristics of the camera, but some may even be intentional, for aesthetic purposes (e.g. Bokeh effect). In the case of the latter, it becomes highly difficult for SR methods to disentangle the blur to remove, and that to leave as is. In this paper, we propose a novel blind SR framework based on kernel-oriented adaptive local adjustment (KOALA) of SR features, called KOALAnet, which jointly learns spatially-variant degradation and restoration kernels in order to adapt to the spatially-variant blur characteristics in real images. Our KOALAnet outperforms recent blind SR methods for synthesized LR images obtained with randomized degradations, and we further show that the proposed KOALAnet produces the most natural results for artistic photographs with intentional blur, which are not over-sharpened, by effectively handling images mixed with in-focus and out-of-focus areas.

关键词： Degradation computer vision Codes Filtering Superresolution Cameras Data models

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CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning

CReST: A Class-Rebalancing Self-Training Framework for Imbal...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Wei, Chen Sohn, Kihyuk Mellina, Clayton Yuille, Alan Yang, Fan Johns Hopkins Univ Baltimore MD 21218 USA Google Cloud AI Mountain View CA USA Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Semi-supervised learning on class-imbalanced data, although a realistic problem, has been under studied. While existing semi-supervised learning (SSL) methods are known to perform poorly on minority classes, we find that they still generate high precision pseudo-labels on minority classes. By exploiting this property, in this work, we propose Class-Rebalancing Self-Training (CReST), a simple yet effective framework to improve existing SSL methods on class-imbalanced data. CReST iteratively retrains a baseline SSL model with a labeled set expanded by adding pseudo labeled samples from an unlabeled set, where pseudo labeled samples from minority classes are selected more frequently according to an estimated class distribution. We also propose a progressive distribution alignment to adaptively adjust the rebalancing strength dubbed CReST+. We show that CReST and CReST+ improve state-of-the-art SSL algorithms on various class-imbalanced datasets and consistently outperform other popular rebalancing methods. Code has been made available at ht-sps//***/google-research/crest.

关键词： computer vision Adaptation models Codes Semisupervised learning pattern recognition

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Image Change Captioning by Learning from an Auxiliary Task

Image Change Captioning by Learning from an Auxiliary Task

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Hosseinzadeh, Mehrdad Wang, Yang Univ Manitoba Winnipeg MB Canada Huawei Technol Canada Markham ON Canada

ISBN: (纸本)9781665445092

We tackle the challenging task of image change captioning. The goal is to describe the subtle difference between two very similar images by generating a sentence caption. While the recent methods mainly focus on proposing new model architectures for this problem, we instead focus on an alternative training scheme. Inspired by the success of multi-task learning, we formulate a training scheme that uses an auxiliary task to improve the training of the change captioning network. We argue that the task of composed query image retrieval is a natural choice as the auxiliary task. Given two almost similar images as the input, the primary network generates a caption describing the fine change between those two images. Next, the auxiliary network is provided with the generated caption and one of those two images. It then tries to pick the second image among a set of candidates. This forces the primary network to generate detailed and precise captions via having an extra supervision loss by the auxiliary network. Furthermore, we propose a new scheme for selecting a negative set of candidates for the retrieval task that can effectively improve the performance. We show that the proposed training strategy performs well on the task of change captioning on benchmark datasets.

关键词： Training computer vision Image color analysis Image retrieval Semantics Benchmark testing pattern recognition

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Temporal Action Segmentation from Timestamp Supervision

Temporal Action Segmentation from Timestamp Supervision

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Li, Zhe Abu Farha, Yazan Gall, Juergen Univ Bonn Bonn Germany

ISBN: (纸本)9781665445092

Temporal action segmentation approaches have been very successful recently. However;annotating videos with frame-wise labels to train such models is very expensive and time consuming. While weakly supervised methods trained using only ordered action lists require less annotation effort, the performance is still worse than filly supervised approaches. In this paper, we propose to use timestamp supervision for the temporal action segmentation task. Timestamps require a comparable annotation effort to weakly supervised approaches, and yet provide a more supervisory signal. To demonstrate the effectiveness of timestamp supervision, we propose an approach to train a segmentation model using only timestamps annotations. Our approach uses the model output and the annotated timestamps to generate frame-wise labels by detecting the action changes. We further introduce a confidence loss that forces the predicted probabilities to monotonically decrease as the distance to the timestamps increases. This ensures that all and not only the most distinctive frames of an action are learned during training. The evaluation on four datasets shows that models trained with timestamps annotations achieve comparable performance to the fully supervised approaches.

关键词： Training computer vision Annotations Computational modeling Predictive models pattern recognition Task analysis

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引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Faraki, Masoud Yu, Xiang Tsai, Yi-Hsuan Suh, Yumin Chandraker, Manmohan NEC Labs Amer Princeton NJ 08540 USA Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9781665445092

Face recognition models trained under the assumption of identical training and test distributions often suffer from poor generalization when faced with unknown variations, such as a novel ethnicity or unpredictable individual make-ups during test time. In this paper, we introduce a novel cross-domain metric learning loss, which we dub Cross-Domain Triplet (CDT) loss, to improve face recognition in unseen domains. The CDT loss encourages learning semantically meaningful features by enforcing compact feature clusters of identities from one domain, where the compactness is measured by underlying similarity metrics that belong to another training domain with different statistics. Intuitively, it discriminatively correlates explicit metrics derived from one domain, with triplet samples from another domain in a unified loss function to be minimized within a network, which leads to better alignment of the training domains. The network parameters are further enforced to learn generalized features under domain shift, in a model-agnostic learning pipeline. Unlike the recent work of Meta Face recognition [18], our method does not require careful hard-pair sample mining and filtering strategy during training. Extensive experiments on various face recognition benchmarks show the superiority of our method in handling variations, compared to baseline and the state-of-the-art methods.

关键词： Training computer vision Filtering Face recognition Pipelines Benchmark testing Loss measurement

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Memory-guided Unsupervised Image-to-image Translation

Memory-guided Unsupervised Image-to-image Translation

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Jeong, Somi Kim, Youngjung Lee, Eungbean Sohn, Kwanghoon Yonsei Univ Dept Elect & Elect Engn Seoul South Korea Agcy Def Dev ADD Daejeon South Korea

ISBN: (纸本)9781665445092

We present a novel unsupervised framework for instance-level image-to-image translation. Although recent advances have been made by incorporating additional object annotations, existing methods often fail to handle images with multiple disparate objects. The main cause is that, during inference, they apply a global style to the whole image and do not consider the large style discrepancy between instance and background, or within instances. To address this problem, we propose a class-aware memory network that explicitly reasons about local style variations. A key-values memory structure, with a set of read/update operations, is introduced to record class-wise style variations and access them without requiring an object detector at the test time. The key stores a domain-agnostic content representation for allocating memory items, while the values encode domain-specific style representations. We also present a feature contrastive loss to boost the discriminative power of memory items. We show that by incorporating our memory, we can transfer class-aware and accurate style representations across domains. Experimental results demonstrate that our model outperforms recent instance-level methods and achieves state-of-the-art performance.

关键词： computer vision Annotations Memory management Object detection Detectors pattern recognition

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ReMix: Towards Image-to-Image Translation with Limited Data

ReMix: Towards Image-to-Image Translation with Limited Data

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Cao, Jie Hou, Luanxuan Yang, Ming-Hsuan He, Ran Sun, Zhenan CASIA CRIPAC NLPR Beijing Peoples R China CASIA CEBSIT Beijing Peoples R China UCAS AIR Beijing Peoples R China Univ Calif Merced Merced CA USA Google Res Mountain View CA USA Yonsei Univ Seoul South Korea

ISBN: (纸本)9781665445092

Image-to-image (I2I) translation methods based on generative adversarial networks (GANs) typically suffer from overfitting when limited training data is available. In this work, we propose a data augmentation method (ReMix) to tackle this issue. We interpolate training samples at the feature level and propose a novel content loss based on the perceptual relations among samples. The generator learns to translate the in-between samples rather than memorizing the training set, and thereby forces the discriminator to generalize. The proposed approach effectively reduces the ambiguity of generation and renders content-preserving results. The ReMix method can be easily incorporated into existing GAN models with minor modifications. Experimental results on numerous tasks demonstrate that GAN models equipped with the ReMix method achieve significant improvements.

关键词： Training Measurement Image quality computer vision Computational modeling Training data Generative adversarial networks

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