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共 11423 条记录，以下是261-270 订阅

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Large-capacity Image Steganography Based on Invertible Neural Networks

Large-capacity Image Steganography Based on Invertible Neura...

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

作者： Lu, Shao-Ping Wang, Rong Zhong, Tao Rosin, Paul L. Nankai Univ CS TKLNDST Tianjin Peoples R China Cardiff Univ Sch Comp Sci & Informat Cardiff Wales

ISBN: (纸本)9781665445092

Many attempts have been made to hide information in images, where one main challenge is how to increase the payload capacity without the container image being detected as containing a message. In this paper, we propose a large-capacity Invertible Steganography Network (ISN) for image steganography. We take steganography and the recovery of hidden images as a pair of inverse problems on image domain transformation, and then introduce the forward and backward propagation operations of a single invertible network to leverage the image embedding and extracting problems. Sharing all parameters of our single ISN architecture enables us to efficiently generate both the container image and the revealed hidden image(s) with high quality. Moreover, in our architecture the capacity of image steganography is significantly improved by naturally increasing the number of channels of the hidden image branch. Comprehensive experiments demonstrate that with this significant improvement of the steganography payload capacity, our ISN achieves state-of-the-art in both visual and quantitative comparisons.

关键词： Backpropagation Steganography Visualization computer vision Inverse problems Neural networks computer architecture

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Estimating (and fixing) the Effect of Face Obfuscation in Video recognition

Estimating (and <i>fixing</i>) the Effect of Face Obfuscatio...

引用

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

作者： Tomei, Matteo Baraldi, Lorenzo Bronzin, Simone Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy Metaliquid Srl Milan Italy

ISBN: (纸本)9781665448994

Recent research has shown that faces can be obfuscated in large-scale datasets with a minimal performance impact on image classification and downstream tasks like object recognition. In this paper, we investigate the role of face obfuscation in video classification datasets and quantify a more significant reduction in performance caused by face blurring. To reduce such performance effects, we propose a generalized distillation approach in which a privacy-preserving action recognition network is trained with privileged information given by face identities. We show, through experiments performed on Kinetics-400, that the proposed approach can fully close the performance gap caused by face anonymization.

关键词： Visualization computer vision Face recognition conferences Computational modeling Pipelines Object recognition

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Self-Supervised Wasserstein Pseudo-Labeling for Semi-Supervised Image Classification

Self-Supervised Wasserstein Pseudo-Labeling for Semi-Supervi...

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

作者： Taherkhani, Fariborz Dabouei, Ali Soleymani, Sobhan Dawson, Jeremy Nasrabadi, Nasser M. West Virginia Univ Morgantown WV 26506 USA

ISBN: (纸本)9781665445092

The goal is to use Wasserstein metric to provide pseudo labels for the unlabeled images to train a Convolutional Neural Networks (CNN) in a Semi-Supervised Learning (SSL) manner for the classification task. The basic premise in our method is that the discrepancy between two discrete empirical measures (e.g., clusters) which come from the same or similar distribution is expected to be less than the case where these measures come from completely two different distributions. In our proposed method, we first pre-train our CNN using a self-supervised learning method to make a cluster assumption on the unlabeled images. Next, inspired by the Wasserstein metric which considers the geometry of the metric space to provide a natural notion of similarity between discrete empirical measures, we leverage it to cluster the unlabeled images and then match the clusters to their similar class of labeled images to provide a pseudo label for the data within each cluster. We have evaluated and compared our method with state-of-the-art SSL methods on the standard datasets to demonstrate its effectiveness.

关键词： Measurement Training Clustering algorithms Semisupervised learning Data models pattern recognition Convolutional neural networks

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PLOP: Learning without Forgetting for Continual Semantic Segmentation

PLOP: Learning without Forgetting for Continual Semantic Seg...

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

作者： Douillard, Arthur Chen, Yifu Dapogny, Arnaud Cord, Matthieu Sorbonne Univ Paris France Heuritech Paris France Datakalab Paris France Valeoai Paris France

ISBN: (纸本)9781665445092

Deep learning approaches are nowadays ubiquitously used to tackle computer vision tasks such as semantic segmentation, requiring large datasets and substantial computational power. Continual learning for semantic segmentation (CSS) is an emerging trend that consists in updating an old model by sequentially adding new classes. However, continual learning methods are usually prone to catastrophic forgetting. This issue is further aggravated in CSS where, at each step, old classes from previous iterations are collapsed into the background. In this paper, we propose Local POD, a multi-scale pooling distillation scheme that preserves long- and short-range spatial relationships at feature level. Furthermore, we design an entropy-based pseudo-labelling of the background w.r.t. classes predicted by the old model to deal with background shift and avoid catastrophic forgetting of the old classes. Our approach, called PLOP, significantly outperforms state-of-the-art methods in existing CSS scenarios, as well as in newly proposed challenging benchmarks(1).

关键词： Learning systems computer vision Semantics Benchmark testing Predictive models Market research Stability analysis

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A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classification

A Realistic Evaluation of Semi-Supervised Learning for Fine-...

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

作者： Su, Jong-Chyi Cheng, Zezhou Maji, Subhransu Univ Massachusetts Amherst Amherst MA 01003 USA

ISBN: (纸本)9781665445092

We evaluate the effectiveness of semi-supervised learning (SSL) on a realistic benchmark where data exhibits considerable class imbalance and contains images from novel classes. Our benchmark consists of two fine-grained classification datasets obtained by sampling classes from the Aves and Fungi taxonomy. We find that recently proposed SSL methods provide significant benefits, and can effectively use out-of-class data to improve performance when deep networks are trained from scratch. Yet their performance pales in comparison to a transfer learning baseline, an alternative approach for learning from a few examples. Furthermore, in the transfer setting, while existing SSL methods provide improvements, the presence of out-of-class is often detrimental. In this setting, standard fine-tuning followed by distillation-based self-training is the most robust. Our work suggests that semi-supervised learning with experts on realistic datasets may require different strategies than those currently prevalent in the literature.

关键词： Fungi Technological innovation Transfer learning Taxonomy Benchmark testing Semisupervised learning pattern recognition

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Behavior-Driven Synthesis of Human Dynamics

Behavior-Driven Synthesis of Human Dynamics

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

作者： Blattmann, Andreas Milbich, Timo Dorkenwald, Michael Ommer, Bjoern Heidelberg Univ Heidelberg Collaboratory Image Proc IWR Heidelberg Germany

ISBN: (纸本)9781665445092

Generating and representing human behavior are of major importance for various computer vision applications. Commonly, human video synthesis represents behavior as sequences of postures while directly predicting their likely progressions or merely changing the appearance of the depicted persons, thus not being able to exercise control over their actual behavior during the synthesis process. In contrast, controlled behavior synthesis and transfer across individuals requires a deep understanding of body dynamics and calls for a representation of behavior that is independent of appearance and also of specific postures. In this work, we present a model for human behavior synthesis which learns a dedicated representation of human dynamics independent of postures. Using this representation, we are able to change the behavior of a person depicted in an arbitrary posture, or to even directly transfer behavior observed in a given video sequence. To this end, we propose a conditional variational framework which explicitly disentangles posture from behavior. We demonstrate the effectiveness of our approach on this novel task, evaluating capturing, transferring, and sampling fine-grained, diverse behavior, both quantitatively and qualitatively.

关键词： computer vision Video sequences Process control pattern recognition Task analysis

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Understanding Object Dynamics for Interactive Image-to-Video Synthesis

Understanding Object Dynamics for Interactive Image-to-Video...

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

作者： Blattmann, Andreas Milbich, Timo Dorkenwald, Michael Ommer, Bjoern Heidelberg Univ Interdisciplinary Ctr Sci Comp HCI Heidelberg Germany

ISBN: (纸本)9781665445092

What would be the effect of locally poking a static scene? We present an approach that learns naturally-looking global articulations caused by a local manipulation at a pixel level. Training requires only videos of moving objects but no information of the underlying manipulation of the physical scene. Our generative model learns to infer natural object dynamics as a response to user interaction and learns about the interrelations between different object body regions. Given a static image of an object and a local poking of a pixel, the approach then predicts how the object would deform over time. In contrast to existing work on video prediction, we do not synthesize arbitrary realistic videos but enable local interactive control of the deformation. Our model is not restricted to particular object categories and can transfer dynamics onto novel unseen object instances. Extensive experiments on diverse objects demonstrate the effectiveness of our approach compared to common video prediction frameworks. Project page is available st https://***/3cxfa2L.

关键词： Training computer vision Limiting Shape Computational modeling Video sequences Data models

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Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection

Lips Don't Lie: A Generalisable and Robust Approach to Face ...

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

作者： Haliassos, Alexandros Vougioukas, Konstantinos Petridis, Stavros Pantic, Maja Imperial Coll London London England Facebook London London England

ISBN: (纸本)9781665445092

Although current deep learning-based face forgery detectors achieve impressive performance in constrained scenarios, they are vulnerable to samples created by unseen manipulation methods. Some recent works show improvements in generalisation but rely on cues that are easily corrupted by common post-processing operations such as compression. In this paper, we propose LipForensics, a detection approach capable of both generalising to novel manipulations and withstanding various distortions. LipForensics targets high-level semantic irregularities in mouth movements, which are common in many generated videos. It consists in first pretraining a spatio-temporal network to perform visual speech recognition (lipreading), thus learning rich internal representations related to natural mouth motion. A temporal network is subsequently finetuned on fixed mouth embeddings of real and forged data in order to detect fake videos based on mouth movements without overfitting to low-level, manipulation-specific artefacts. Extensive experiments show that this simple approach significantly surpasses the state-of-the-art in terms of generalisation to unseen manipulations and robustness to perturbations, as well as shed light on the factors responsible for its performance.

关键词： Visualization Face recognition Perturbation methods Semantics Meetings Mouth Speech recognition

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Continuous Face Aging via Self-estimated Residual Age Embedding

Continuous Face Aging via Self-estimated Residual Age Embedd...

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

作者： Li, Zeqi Jiang, Ruowei Aarabi, Parham ModiFace Toronto ON Canada

ISBN: (纸本)9781665445092

Face synthesis, including face aging, in particular, has been one of the major topics that witnessed a substantial improvement in image fidelity by using generative adversarial networks (GANs). Most existing face aging approaches divide the dataset into several age groups and leverage groupbased training strategies, which lacks the ability to provide fine-controlled continuous aging synthesis in nature. In this work, we propose a unified network structure that embeds a linear age estimator into a GAN-based model, where the embedded age estimator is trained jointly with the encoder and decoder to estimate the age of a face image and provide a personalized target age embedding for age progression/regression. The personalized target age embedding is synthesized by incorporating both personalized residual age embedding of the current age and exemplar-face aging basis of the target age, where all preceding aging bases are derived from the learned weights of the linear age estimator. This formulation brings the unified perspective of estimating the age and generating personalized aged face, where self-estimated age embeddings can be learned for every single age. The qualitative and quantitative evaluations on different datasets further demonstrate the significant improvement in the continuous face aging aspect over the state-of-the-art.

关键词： Training computer vision Face recognition Estimation Aging Generative adversarial networks Generators

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DAP: Detection-Aware Pre-training with Weak Supervision

DAP: Detection-Aware Pre-training with Weak Supervision

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

作者： Zhong, Yuanyi Wang, Jianfeng Wang, Lijuan Peng, Jian Wang, Yu-Xiong Zhang, Lei Univ Illinois Urbana IL 61801 USA Microsoft Redmond WA USA

ISBN: (纸本)9781665445092

This paper presents a detection-aware pre-training (DAP) approach, which leverages only weakly-labeled classification-style datasets (e.g., ImageNet) for pre-training, but is specifically tailored to benefit object de- tection tasks. In contrast to the widely used image classification-based pre-training (e.g., on ImageNet), which does not include any location-related training tasks, we transform a classification dataset into a detection dataset through a weakly supervised object localization method based on Class Activation Maps to directly pre-train a detector, making the pre-trained model location-aware and capable of predicting bounding boxes. We show that DAP can outperform the traditional classification pre-training in terms of both sample efficiency and convergence speed in downstream detection tasks including VOC and COCO. In particular, DAP boosts the detection accuracy by a large margin when the number of examples in the downstream task is small.

关键词： Training Location awareness computer vision Transforms Object detection Detectors Predictive models

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