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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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Privacy-Preserving Face recognition Using Trainable Feature Subtraction

Privacy-Preserving Face Recognition Using Trainable Feature ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yuxi Mi Zhizhou Zhong Yuge Huang Jiazhen Ji Jianqing Xu Jun Wang Shaoming Wang Shouhong Ding Shuigeng Zhou Fudan University Youtu Lab Tencent WeChat Pay Lab33 Tencent

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The widespread adoption of face recognition has led to increasing privacy concerns, as unauthorized access to face images can expose sensitive personal information. This paper explores face image protection against viewing and recovery attacks. Inspired by image compression, we propose creating a visually uninformative face image through feature subtraction between an original face and its model-produced regeneration. Recognizable identity features within the image are encouraged by co-training a recognition model on its high-dimensional feature represen-tation. To enhance privacy, the high-dimensional represen-tation is crafted through random channel shuffling, resulting in randomized recognizable images devoid of attacker-leverageable texture details. We distill our methodologies into a novel privacy-preserving face recognition method, MinusFace. Experiments demonstrate its high recognition accuracy and effective privacy protection. Its code is avail-able at https://***/Tencent/TFace.

关键词： Privacy computer vision Image recognition Image coding Codes Accuracy Face recognition

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CASTing Your Model: Learning to Localize Improves Self-Supervised Representations

CASTing Your Model: Learning to Localize Improves Self-Super...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Selvaraju, Ramprasaath R. Desai, Karan Johnson, Justin Naik, Nikhil Salesforce Res Palo Alto CA 94301 USA Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781665445092

Recent advances in self-supervised learning (SSL) have largely closed the gap with supervised ImageNet pretraining. Despite their success these methods have been primarily applied to unlabeled ImageNet images, and show marginal gains when trained on larger sets of uncurated images. We hypothesize that current SSL methods perform best on iconic images, and struggle on complex scene images with many objects. Analyzing contrastive SSL methods shows that they have poor visual grounding and receive poor supervisory signal when trained on scene images. We propose Contrastive Attention-Supervised Tuning (CAST) to overcome these limitations. CAST uses unsupervised saliency maps to intelligently sample crops, and to provide grounding supervision via a Grad-CAM attention loss. Experiments on COCO show that CAST significantly improves the features learned by SSL methods on scene images, and further experiments show that CAST-trained models are more robust to changes in backgrounds. Our code is available at https://***/salesforce/CAST/.

关键词： Visualization computer vision Correlation Codes Grounding Crops Robustness

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Guidance Network with Staged Learning for Image enhancement

Guidance Network with Staged Learning for Image enhancement

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Liang, Luming Zharkov, Ilya Amjadi, Faezeh Joze, Hamid Reza Vaezi Pradeep, Vivek Microsoft One Microsoft Way Redmond WA 98052 USA

ISBN: (纸本)9781665448994

Many important yet not fully resolved problems in computational photography and image enhancement, e.g. generating well-lit images from their low-light counterparts or producing RGB images from their RAW camera inputs share a common nature: discovering a color mapping between input pixels to output pixels based on both global information and local details. We propose a novel deep neural network architecture to learn the RAW to RGB mapping based on this common nature. This architecture consists of both global and local sub-networks, where the first sub-network focuses on determining illumination and color mapping, the second sub-network deals with recovering image details. The result of the global network serves as a guidance to the local network to form the final RGB images. Our method outperforms state-of-the-art with a significantly smaller size of network features on various image enhancement tasks.

关键词： Training Photography Image color analysis Lighting computer architecture Cameras Service-oriented architecture

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GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation

GDR-Net: Geometry-Guided Direct Regression Network for Monoc...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Gu Manhardt, Fabian Tombari, Federico Ji, Xiangyang Tsinghua Univ BNRist Beijing Peoples R China Tech Univ Munich Munich Germany Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

6D pose estimation from a single RGB image is a fundamental task in computer vision. The current top-performing deep learning-based methods rely on an indirect strategy, i.e., first establishing 2D-3D correspondences between the coordinates in the image plane and object coordinate system, and then applying a variant of the PnP/RANSAC algorithm. However, this two-stage pipeline is not end-toend trainable, thus is hard to be employed for many tasks requiring differentiable poses. On the other hand, methods based on direct regression are currently inferior to geometry-based methods. In this work, we perform an indepth investigation on both direct and indirect methods, and propose a simple yet effective Geometry-guided Direct Regression Network (GDR-Net) to learn the 6D pose in an end-to-end manner from dense correspondence-based intermediate geometric representations. Extensive experiments show that our approach remarkably outperforms state-of-the-art methods on LM, LM-O and YCB-V datasets. Code is available at https://***/GDR-Net.

关键词： Learning systems Convolutional codes computer vision Pose estimation Pipelines Real-time systems pattern recognition

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Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text recognition

Read Like Humans: Autonomous, Bidirectional and Iterative La...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Fang, Shancheng Xie, Hongtao Wang, Yuxin Mao, Zhendong Zhang, Yongdong Univ Sci & Technol China Hefei Anhui Peoples R China

ISBN: (纸本)9781665445092

Linguistic knowledge is of great benefit to scene text recognition. However, how to effectively model linguistic rules in end-to-end deep networks remains a research challenge. In this paper, we argue that the limited capacity of language models comes from: 1) implicitly language modeling;2) unidirectional feature representation;and 3) language model with noise input. Correspondingly, we propose an autonomous, bidirectional and iterative ABINet for scene text recognition. Firstly, the autonomous suggests to block gradient flow between vision and language models to enforce explicitly language modeling. Secondly, a novel bidirectional doze network (BCN) as the language model is proposed based on bidirectional feature representation. Thirdly, we propose an execution manner of iterative correction for language model which can effectively alleviate the impact of noise input. Additionally, based on the ensemble of iterative predictions, we propose a self-training method which can learn from unlabeled images effectively. Extensive experiments indicate that ABINet has superiority on low-quality images and achieves state-of-the-art results on several mainstream benchmarks. Besides, the ABINet trained with ensemble self-training shows promising improvement in realizing human-level recognition.

关键词： computer vision Codes Text recognition Linguistics Benchmark testing Semisupervised learning Predictive models

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Dynamic Cues-Assisted Transformer for Robust Point Cloud Registration

Dynamic Cues-Assisted Transformer for Robust Point Cloud Reg...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Hong Chen Pei Yan Sihe Xiang Yihua Tan Hubei Engineering Research Center of Machine Vision and Intelligent Systems School of Artificial Intelligence and Automation Huazhong University of Science and Technology China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Point Cloud Registration is a critical and challenging task in computer vision. Recent advancements have pre-dominantly embraced a coarse-to-fine matching mechanism, with the key to matching the superpoints located in patches with interframe consistent structures. How-ever, previous methods still face challenges with ambiguous matching, because the interference information aggregated from irrelevant regions may disturb the capture of interframe consistency relations, leading to wrong matches. To address this issue, we propose Dynamic Cues-Assisted Transformer (DCATr). Firstly, the interference from irrelevant regions is greatly reduced by constraining attention to certain cues, i.e., regions with highly correlated structures of potential corresponding superpoints. Secondly, cues-assisted attention is designed to mine the interframe consistency relations, while more attention is assigned to pairs with high consistent confidence in feature aggregation. Finally, a dynamic updating fashion is proposed to facilitate mining richer consistency information, further improving aggregated features' distinctiveness and relieving matching ambiguity. Extensive evaluations on indoor and outdoor standard benchmarks demonstrate that DCATr outperforms all state-of-the-art methods.

关键词： Point cloud compression computer vision Interference Benchmark testing Transformers Feature extraction pattern recognition

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Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps

Guided Interactive Video Object Segmentation Using Reliabili...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Heo, Yuk Koh, Yeong Jun Kim, Chang-Su Korea Univ Seoul South Korea Chungnam Natl Univ Daejeon South Korea

ISBN: (纸本)9781665445092

We propose a novel guided interactive segmentation (GIS) algorithm for video objects to improve the segmentation accuracy and reduce the interaction time. First, we design the reliability-based attention module to analyze the reliability of multiple annotated frames. Second, we develop the intersection-aware propagation module to propagate segmentation results to neighboring frames. Third, we introduce the GIS mechanism for a user to select unsatisfactory frames quickly with less e f fort. Experimental results demonstrate that the proposed algorithm provides more accurate segmentation results at a faster speed than conventional algorithms. Codes are available at https://***/yuk6heo/GIS-RAmap.

关键词： computer vision Codes Object segmentation Inspection Reliability engineering Feature extraction Data mining

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Weakly Supervised Video Salient Object Detection

Weakly Supervised Video Salient Object Detection

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhao, Wangbo Zhang, Jing Li, Long Barnes, Nick Liu, Nian Han, Junwei Northwestern Polytech Univ Brain & Artificial Intelligence Lab Xian Peoples R China Australian Natl Univ Canberra ACT Australia CSIRO Canberra ACT Australia Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9781665445092

Significant performance improvement has been achieved for fully-supervised video salient object detection with the pixel-wise labeled training datasets, which are time-consuming and expensive to obtain. To relieve the burden of data annotation, we present the first weakly supervised video salient object detection model based on relabeled "fixation guided scribble annotations". Specifically, an "Appearance-motion fusion module" and bidirectional ConvLSTM based framework are proposed to achieve effective multi-modal learning and long-term temporal context modeling based on our new weak annotations. Further, we design a novel foreground-background similarity loss to further explore the labeling similarity across frames. A weak annotation boosting strategy is also introduced to boost our model performance with a new pseudo-label generation technique. Extensive experimental results on six benchmark video saliency detection datasets illustrate the effectiveness of our solution(1).

关键词： Training computer vision Annotations Object detection Boosting Data models pattern recognition

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Video Class Agnostic Segmentation Benchmark for Autonomous Driving

Video Class Agnostic Segmentation Benchmark for Autonomous D...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Siam, Mennatullah Kendall, Alex Jagersand, Martin Univ Alberta Edmonton AB Canada Wayve London England

ISBN: (纸本)9781665448994

Semantic segmentation approaches are typically trained on large-scale data with a closed finite set of known classes without considering unknown objects. In certain safety-critical robotics applications, especially autonomous driving, it is important to segment all objects, including those unknown at training time. We formalize the task of video class agnostic segmentation from monocular video sequences in autonomous driving to account for unknown objects. Video class agnostic segmentation can be formulated as an open-set or a motion segmentation problem. We discuss both formulations and provide datasets and benchmark different baseline approaches for both tracks. In the motion-segmentation track we benchmark real-time joint panoptic and motion instance segmentation, and evaluate the effect of ego-flow suppression. In the open-set segmentation track we evaluate baseline methods that combine appearance, and geometry to learn prototypes per semantic class. We then compare it to a model that uses an auxiliary contrastive loss to improve the discrimination between known and unknown objects. Datasets and models are publicly released at https://***/vca/.

关键词： Training computer vision Tracking Motion segmentation Semantics Video sequences Benchmark testing

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AsymmNet: Towards ultralight convolution neural networks using asymmetrical bottlenecks

AsymmNet: Towards ultralight convolution neural networks usi...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yang, Haojin Shen, Zhen Zhao, Yucheng Hasso Plattner Inst Potsdam Germany Alibaba Cloud Hangzhou Peoples R China ByteDance Beijing Peoples R China

ISBN: (纸本)9781665448994

Deep convolutional neural networks (CNN) have achieved astonishing results in a large variety of applications. However, using these models on mobile or embedded devices is difficult due to the limited memory and computation resources. Recently, the inverted residual block becomes the dominating solution for the architecture design of compact CNNs. In this work, we comprehensively investigated the existing design concepts, rethink the functional characteristics of two pointwise convolutions in the inverted residuals. We propose a novel design, called asymmetrical bottlenecks. Precisely, we adjust the first pointwise convolution dimension, enrich the information flow by feature reuse, and migrate saved computations to the second pointwise convolution. Doing so we can further improve the accuracy without increasing the computation overhead. The asymmetrical bottlenecks can be adopted as a dropin replacement for the existing CNN blocks. We can thus create AsymmNet by easily stack those blocks according to proper depth and width conditions. Extensive experiments demonstrate that our proposed block design is more beneficial than the original inverted residual bottlenecks for mobile networks, especially useful for those ultralight CNNs within the regime of <220M MAdds. Code is available at https://***/Spark001/AsymmNet

关键词： Convolutional codes computer vision conferences Computational modeling Neural networks computer architecture pattern recognition

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