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共 11753 条记录，以下是4421-4430 订阅

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Disentangled Cycle Consistency for Highly-realistic Virtual Try-On

Disentangled Cycle Consistency for Highly-realistic Virtual ...

引用

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

作者： Ge, Chongjian Song, Yibing Ge, Yuying Yang, Han Liu, Wei Luo, Ping Univ Hong Kong Hong Kong Peoples R China Tencent AI Lab Bellevue WA 98004 USA Swiss Fed Inst Technol Zurich Switzerland Tencent Data Platform Bellevue WA USA

ISBN: (纸本)9781665445092

Image virtual try-on replaces the clothes on a person image with a desired in-shop clothes image. It is challenging because the person and the in-shop clothes are unpaired. Existing methods formulate virtual try-on as either in-painting or cycle consistency. Both of these two formulations encourage the generation networks to reconstruct the input image in a self-supervised manner. However, existing methods do not differentiate clothing and non-clothing regions. A straightforward generation impedes the virtual try-on quality because of the heavily coupled image contents. In this paper, we propose a Disentangled Cycle-consistency Try-On Network (DCTON). The DCTON is able to produce highly-realistic try-on images by disentangling important components of virtual try-on including clothes warping, skin synthesis, and image composition. Moreover, DCTON can be naturally trained in a self-supervised manner following cycle consistency learning. Extensive experiments on challenging benchmarks show that DCTON outperforms state-of-the-art approaches favorably.

关键词： computer vision Clothing Benchmark testing Skin pattern recognition Image reconstruction

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Iterative Shrinking for Referring Expression Grounding Using Deep Reinforcement Learning

Iterative Shrinking for Referring Expression Grounding Using...

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

作者： Sun, Mingjie Xiao, Jimin Lim, Eng Gee Xian Jiaotong Liverpool Univ Suzhou Peoples R China Univ Liverpool Liverpool Merseyside England

ISBN: (纸本)9781665445092

In this paper, we are tackling the proposal-free referring expression grounding task, aiming at localizing the target object according to a query sentence, without relying on off-the-shelf object proposals. Existing proposal-free methods employ a query-image matching branch to select the highest-score point in the image feature map as the target box center, with its width and height predicted by another branch. Such methods, however, fail to utilize the contextual relation between the target and reference objects, and lack interpretability on its reasoning procedure. To solve these problems, we propose an iterative shrinking mechanism to localize the target, where the shrinking direction is decided by a reinforcement learning agent, with all contents within the current image patch comprehensively considered. Besides, the sequential shrinking processes enable to demonstrate the reasoning about how to iteratively find the target. Experiments show that the proposed method boosts the accuracy by 4.32% against the previous state-of-the-art (SOTA) method on the RefCOCOg dataset, where query sentences are long and complex with many targets referred by other reference objects.

关键词： computer vision Art Grounding Reinforcement learning Cognition pattern recognition Proposals

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Variational Pedestrian Detection

Variational Pedestrian Detection

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

作者： Zhang, Yuang He, Huanyu Li, Jianguo Li, Yuxi See, John Lin, Weiyao Shanghai Jiao Tong Univ Shanghai Peoples R China Ant Grp Hangzhou Peoples R China Heriot Watt Univ Putrajaya Malaysia

ISBN: (纸本)9781665445092

Pedestrian detection in a crowd is a challenging task due to a high number of mutually-occluding human instances, which brings ambiguity and optimization difficulties to the current IoU-based ground truth assignment procedure in classical object detection methods. In this paper, we develop a unique perspective of pedestrian detection as a variational inference problem. We formulate a novel and efficient algorithm for pedestrian detection by modeling the dense proposals as a latent variable while proposing a customized Auto-Encoding Variational Bayes (AEVB) algorithm. Through the optimization of our proposed algorithm, a classical detector can be fashioned into a variational pedestrian detector. Experiments conducted on CrowdHuman and CityPersons datasets show that the proposed algorithm serves as an efficient solution to handle the dense pedestrian detection problem for the case of single-stage detectors. Our method can also be flexibly applied to two-stage detectors, achieving notable performance enhancement.

关键词： computer vision Computational modeling Detectors Object detection Propulsion Inference algorithms pattern recognition

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Conditional Bures Metric for Domain Adaptation

Conditional Bures Metric for Domain Adaptation

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

作者： Luo, You-Wei Ren, Chuan-Xian Sun Yat Sen Univ Sch Math Guangzhou Guangdong Peoples R China Pazhou Lab Guangzhou Peoples R China

ISBN: (纸本)9781665445092

As a vital problem in classification-oriented transfer, unsupervised domain adaptation (UDA) has attracted widespread attention in recent years. Previous UDA methods assume the marginal distributions of different domains are shifted while ignoring the discriminant information in the label distributions. This leads to classification performance degeneration in real applications. In this work, we focus on the conditional distribution shift problem which is of great concern to current conditional invariant models. We aim to seek a kernel covariance embedding for conditional distribution which remains yet unexplored. Theoretically, we propose the Conditional Kernel Bures (CKB) metric for characterizing conditional distribution discrepancy, and derive an empirical estimation for the CKB metric without introducing the implicit kernel feature map. It provides an interpretable approach to understand the knowledge transfer mechanism. The established consistency theory of the empirical estimation provides a theoretical guarantee for convergence. A conditional distribution matching network is proposed to learn the conditional invariant and discriminative features for UDA. Extensive experiments and analysis show the superiority of our proposed model.

关键词： Measurement computer vision Analytical models Adaptation models Computational modeling Estimation pattern recognition

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Consensus Maximisation Using Influences of Monotone Boolean Functions

Consensus Maximisation Using Influences of Monotone Boolean ...

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

作者： Tennakoon, Ruwan Suter, David Zhang, Erchuan Chin, Tat-Jun Bab-Hadiashar, Alireza RMIT Univ Melbourne Vic Australia Edith Cowen Univ Perth WA Australia Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9781665445092

Consensus maximisation (MaxCon), which is widely used for robust fitting in computer vision, aims to find the largest subset of data that fits the model within some tolerance level. In this paper, we outline the connection between MaxCon problem and the abstract problem of finding the maximum upper zero of a Monotone Boolean Function (MBF) defined over the Boolean Cube. Then, we link the concept of influences (in a MBF) to the concept of outlier (in MaxCon) and show that influences of points belonging to the largest structure in data would generally be smaller under certain conditions. Based on this observation, we present an iterative algorithm to perform consensus maximisation. Results for both synthetic and real visual data experiments show that the MBF based algorithm is capable of generating a near optimal solution relatively quickly. This is particularly important where there are large number of outliers (gross or pseudo) in the observed data.

关键词： computer vision Visualization Boolean functions Runtime Computational modeling Fitting Memory management

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DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows

DeFlow: Learning Complex Image Degradations from Unpaired Da...

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

作者： Wolf, Valentin Lugmayr, Andreas Danelljan, Martin Van Gool, Luc Timofte, Radu Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9781665445092

The difficulty of obtaining paired data remains a major bottleneck for learning image restoration and enhancement models for real-world applications. Current strategies aim to synthesize realistic training data by modeling noise and degradations that appear in real-world settings. We propose DeFlow, a method for learning stochastic image degradations from unpaired data. Our approach is based on a novel unpaired learning formulation for conditional normalizing flows. We model the degradation process in the latent space of a shared flow encoder-decoder network. This allows us to learn the conditional distribution of a noisy image given the clean input by solely minimizing the negative log-likelihood of the marginal distributions. We validate our DeFlow formulation on the task of joint image restoration and super-resolution. The models trained with the synthetic data generated by DeFlow outperform previous learnable approaches on three recent datasets.

关键词： Degradation Superresolution Training data Stochastic processes Data models Image restoration pattern recognition

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Accelerating vision-Language Pretraining with Free Language Modeling

Accelerating Vision-Language Pretraining with Free Language ...

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conference on computer vision and pattern recognition (cvpr)

作者： Teng Wang Yixiao Ge Feng Zheng Ran Cheng Ying Shan Xiaohu Qie Ping Luo Southern University of Science and Technology The University of Hong Kong ARC Lab Peng Cheng Laboratory Tencent PCG Shanghai AI Laboratory

The state of the arts in vision-language pretraining (VLP) achieves exemplary performance but suffers from high training costs resulting from slow convergence and long training time, especially on large-scale web datasets. An essential obstacle to training efficiency lies in the entangled prediction rate (percentage of tokens for reconstruction) and corruption rate (percentage of corrupted tokens) in masked language modeling (MLM), that is, a proper corruption rate is achieved at the cost of a large portion of output tokens being excluded from prediction loss. To accelerate the convergence of VLP, we propose a new pretraining task, namely, free language modeling (FLM), that enables a 100% prediction rate with arbitrary corruption rates. FLM successfully frees the prediction rate from the tie-up with the corruption rate while allowing the corruption spans to be customized for each token to be predicted. FLM-trained models are encouraged to learn better and faster given the same GPU time by exploiting bidirectional contexts more flexibly. Extensive experiments show FLM could achieve an impressive 2.5 × pretraining time reduction in comparison to the MLM-based methods, while keeping competitive performance on both vision-language understanding and generation tasks. Code will be public at https://***/TencentARC/FLM.

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PSD: Principled Synthetic-to-Real Dehazing Guided by Physical Priors

PSD: Principled Synthetic-to-Real Dehazing Guided by Physica...

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

作者： Chen, Zeyuan Wang, Yangchao Yang, Yang Liu, Dong Univ Sci & Technol China Hefei Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9781665445092

Deep learning-based methods have achieved remarkable performance for image dehazing. However, previous studies are mostly focused on training models with synthetic hazy images, which incurs performance drop when the models are used for real-world hazy images. We propose a Principled Synthetic-to-real Dehazing (PSD) framework to improve the generalization performance of dehazing. Starting from a dehazing model backbone that is pre-trained on synthetic data, PSD exploits real hazy images to fine-tune the model in an unsupervised fashion. For the fine-tuning, we leverage several well-grounded physical priors and combine them into a prior loss committee. PSD allows for most of the existing dehazing models as its backbone, and the combination of multiple physical priors boosts dehazing significantly. Through extensive experiments, we demonstrate that our PSD framework establishes the new state-of-the-art performance for real-world dehazing, in terms of visual quality assessed by no-reference quality metrics as well as subjective evaluation and downstream task performance indicator.

关键词： Training Measurement Learning systems Visualization computer vision Data models pattern recognition

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Learning to Associate Every Segment for Video Panoptic Segmentation

Learning to Associate Every Segment for Video Panoptic Segme...

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

作者： Woo, Sanghyun Kim, Dahun Lee, Joon-Young Kweon, In So Korea Adv Inst Sci & Technol Daejeon South Korea Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

Temporal correspondence - linking pixels or objects across frames - is a fundamental supervisory signal for the video models. For the panoptic understanding of dynamic scenes, we further extend this concept to every segment. Specifically, we aim to learn coarse segment-level matching and fine pixel-level matching together. We implement this idea by designing two novel learning objectives. To validate our proposals, we adopt a deep siamese model and train the model to learn the temporal correspondence on two different levels (i.e., segment and pixel) along with the target task. At inference time, the model processes each frame independently without any extra computation and post-processing. We show that our per-frame inference model can achieve new state-of-the-art results on Cityscapes-VPS and VIPER datasets. Moreover, due to its high efficiency, the model runs in a fraction of time (3x) compared to the previous state-of-the-art approach.

关键词： Learning systems computer vision Computational modeling Linear programming pattern recognition Proposals Task analysis

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Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink

Adversarial Laser Beam: Effective Physical-World Attack to D...

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

作者： Duan, Ranjie Mao, Xiaofeng Qin, A. K. Chen, Yuefeng Ye, Shaokai He, Yuan Yang, Yun Swinburne Univ Technol Hawthorn Vic Australia Alibaba Grp Hangzhou Zhejiang Peoples R China Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9781665445092

Though it is well known that the performance of deep neural networks (DNNs) degrades under certain light conditions, there exists no study on the threats of light beams emitted from some physical source as adversarial attacker on DNNs in a real-world scenario. In this work, we show by simply using a laser beam that DNNs are easily fooled. To this end, we propose a novel attack method called Adversarial Laser Beam (AdvLB), which enables manipulation of laser beam's physical parameters to perform adversarial attack. Experiments demonstrate the effectiveness of our proposed approach in both digital- and physical-settings. We further empirically analyze the evaluation results and reveal that the proposed laser beam attack may lead to some interesting prediction errors of the state-of-the-art DNNs. We envisage that the proposed AdvLB method enriches the current family of adversarial attacks and builds the foundation for future robustness studies for light.

关键词： Deep learning Laser theory computer vision Robustness pattern recognition Laser beams

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