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

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Towards Pose Robust Face recognition

Towards Pose Robust Face Recognition

引用

26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Yi, Dong Lei, Zhen Li, Stan Z. Chinese Acad Sci Inst Automat Ctr Biometr & Secur Res Beijing 100864 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100864 Peoples R China

ISBN: (纸本)9780769549897

Most existing pose robust methods are too computational complex to meet practical applications and their performance under unconstrained environments are rarely evaluated. In this paper, we propose a novel method for pose robust face recognition towards practical applications, which is fast, pose robust and can work well under unconstrained environments. Firstly, a 3D deformable model is built and a fast 3D model fitting algorithm is proposed to estimate the pose of face image. Secondly, a group of Gabor filters are transformed according to the pose and shape of face image for feature extraction. Finally, PCA is applied on the pose adaptive Gabor features to remove the redundances and Cosine metric is used to evaluate the similarity. The proposed method has three advantages: (1) The pose correction is applied in the filter space rather than image space, which makes our method less affected by the precision of the 3D model;(2) By combining the holistic pose transformation and local Gabor filtering, the final feature is robust to pose and other negative factors in face recognition;(3) The 3D structure and facial symmetry are successfully used to deal with self-occlusion. Extensive experiments on FERET and PIE show the proposed method outperforms state-of-the-art methods significantly, meanwhile, the method works well on LFW.

关键词： Gabor filters face recognition feature extraction hidden feature removal pose estimation principal component analysis solid modelling

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VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild

VSPW: A Large-scale Dataset for Video Scene Parsing in the W...

引用

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

作者： Miao, Jiaxu Wei, Yunchao Wu, Yu Liang, Chen Li, Guangrui Yang, Yi Zhejiang Univ Hangzhou Zhejiang Peoples R China Baidu Res Beijing Peoples R China Univ Technol Sydney ReLER Sydney NSW Australia

ISBN: (纸本)9781665445092

In this paper, we present a new dataset with the target of advancing the scene parsing task from images to videos. Our dataset aims to perform Video Scene Parsing in the Wild (VSPW), which covers a wide range of real-world scenarios and categories. To be specific, our VSPW is featured from the following aspects: 1) Well-trimmed long-temporal clips. Each video contains a complete shot, lasting around 5 seconds on average. 2) Dense annotation. The pixel-level annotations are provided at a high frame rate of 15 f/s. 3) High resolution. Over 96% of the captured videos are with high spatial resolutions from 720P to 4K. We totally annotate 3,536 videos, including 251,633 frames from 124 categories. To the best of our knowledge, our VSPW is the first attempt to tackle the challenging video scene parsing task in the wild by considering diverse scenarios. Based on VSPW, we design a generic Temporal Context Blending (TCB) network, which can effectively harness long-range contextual information from the past frames to help segment the current one. Extensive experiments show that our TCB network improves both the segmentation performance and temporal stability comparing with image-/video-based state-of-the-art methods. We hope that the scale, diversity, long-temporal, and high frame rate of our VSPW can significantly advance the research of video scene parsing and beyond. The dataset is available at https://***/.

关键词： Image segmentation computer vision Annotations pattern recognition Task analysis Spatial resolution Videos

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DegAE: A New Pretraining Paradigm for Low-level vision

DegAE: A New Pretraining Paradigm for Low-level Vision

引用

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

作者： Liu, Yihao He, Jingwen Gu, Jinjin Kong, Xiangtao Qiao, Yu Dong, Chao Shanghai Artificial Intelligence Lab Shanghai Peoples R China Chinese Acad Sci ShenZhen Key Lab Comp Vis & Pattern Recognit Shenzhen Inst Adv Technol Shenzhen Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9798350301298

Self-supervised pretraining has achieved remarkable success in high-level vision, but its application in low-level vision remains ambiguous and not well-established. What is the primitive intention of pretraining? What is the core problem of pretraining in low-level vision? In this paper, we aim to answer these essential questions and establish a new pretraining scheme for low-level vision. Specifically, we examine previous pretraining methods in both high-level and low-level vision, and categorize current low-level vision tasks into two groups based on the difficulty of data acquisition: low-cost and high-cost tasks. Existing literature has mainly focused on pretraining for low-cost tasks, where the observed performance improvement is often limited. However, we argue that pretraining is more significant for highcost tasks, where data acquisition is more challenging. To learn a general low-level vision representation that can improve the performance of various tasks, we propose a new pretraining paradigm called degradation autoencoder (DegAE). DegAE follows the philosophy of designing pretext task for self-supervised pretraining and is elaborately tailored to low-level vision. With DegAE pretraining, SwinIR achieves a 6.88dB performance gain on image dehaze task, while Uformer obtains 3.22dB and 0.54dB improvement on dehaze and derain tasks, respectively.

关键词： Low-level vision

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Self-Supervised Deep Visual Odometry with Online Adaptation

Self-Supervised Deep Visual Odometry with Online Adaptation

引用

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

作者： Li, Shunkai Wang, Xin Cao, Yingdian Xue, Fei Yan, Zike Zha, Hongbin Peking Univ Key Lab Machine Percept MOE Sch EECS PKU SenseTime Machine Vis Joint Lab Beijing Peoples R China

ISBN: (纸本)9781728171685

Self-supervised VO methods have shown great success in jointly estimating camera pose and depth from videos. However, like most data-driven methods, existing VO networks suffer from a notable decrease in performance when confronted with scenes different from the training data, which makes them unsuitable for practical applications. In this paper, we propose an online meta-learning algorithm to enable VO networks to continuously adapt to new environments in a self-supervised manner. The proposed method utilizes convolutional long short-term memory (convLSTM) to aggregate rich spatial-temporal information in the past. The network is able to memorize and learn from its past experience for better estimation and fast adaptation to the current frame. When running VO in the open world, in order to deal with the changing environment, we propose an online feature alignment method by aligning feature distributions at different time. Our VO network is able to seamlessly adapt to different environments. Extensive experiments on unseen outdoor scenes, virtual to real world and outdoor to indoor environments demonstrate that our method consistently outperforms state-of-the-art self-supervised VO base-lines considerably.

关键词： computer vision

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Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation

Target-Aware Object Discovery and Association for Unsupervis...

引用

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

作者： Zhou, Tianfei Li, Jianwu Li, Xueyi Shao, Ling Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Incept Inst Artificial Intelligence Al Ain U Arab Emirates

ISBN: (纸本)9781665445092

This paper addresses the task of unsupervised video multi-object segmentation. Current approaches follow a two-stage paradigm: 1) detect object proposals using pre-trained Mask R-CNN, and 2) conduct generic feature matching for temporal association using re-identification techniques. However, the generic features, widely used in both stages, are not reliable for characterizing unseen objects, leading to poor generalization. To address this, we introduce a novel approach for more accurate and efficient spatio-temporal segmentation. In particular, to address instance discrimination, we propose to combine foreground region estimation and instance grouping together in one network, and additionally introduce temporal guidance for segmenting each frame, enabling more accurate object discovery. For temporal association, we complement current video object segmentation architectures with a discriminative appearance model, capable of capturing more fine-grained target-specific information. Given object proposals from the instance discrimination network, three essential strategies are adopted to achieve accurate segmentation: 1) target-specific tracking using a memory-augmented appearance model;2) target-agnostic verification to trace possible tracklets for the proposal;3) adaptive memory updating using the verified segments. We evaluate the proposed approach on DAVIS(17) and YouTube-VIS, and the results demonstrate that it outperforms state-of-the-art methods both in segmentation accuracy and inference speed.

关键词： Adaptation models computer vision Target tracking Estimation Object segmentation Feature extraction pattern recognition

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Class-Incremental Learning by Knowledge Distillation with Adaptive Feature Consolidation

Class-Incremental Learning by Knowledge Distillation with Ad...

引用

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

作者： Kang, Minsoo Park, Jaeyoo Han, Bohyung Seoul Natl Univ ECE Seoul South Korea Seoul Natl Univ ASRI Seoul South Korea Seoul Natl Univ IPAI Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present a novel class incremental learning approach based on deep neural networks, which continually learns new tasks with limited memory for storing examples in the previous tasks. Our algorithm is based on knowledge distillation and provides a principled way to maintain the representations of old models while adjusting to new tasks effectively. The proposed method estimates the relationship between the representation changes and the resulting loss increases incurred by model updates. It minimizes the upper bound of the loss increases using the representations, which exploits the estimated importance of each feature map within a backbone model. Based on the importance, the model restricts updates of important features for robustness while allowing changes in less critical features for flexibility. This optimization strategy effectively alleviates the notorious catastrophic forgetting problem despite the limited accessibility of data in the previous tasks. The experimental results show significant accuracy improvement of the proposed algorithm over the existing methods on the standard datasets. Code is available.(1)

关键词： Representation learning Deep learning Upper bound Neural networks Linear programming Robustness pattern recognition

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Revisiting Document Image Dewarping by Grid Regularization

Revisiting Document Image Dewarping by Grid Regularization

引用

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

作者： Jiang, Xiangwei Long, Rujiao Xue, Nan Yang, Zhibo Yao, Cong Xia, Gui-Song Wuhan Univ Sch Comp Sci Wuhan Peoples R China Wuhan Univ LIESMARS Wuhan Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper addresses the problem of document image dewarping, which aims at eliminating the geometric distortion in document images for document digitization. Instead of designing a better neural network to approximate the optical flow fields between the inputs and outputs, we pursue the best readability by taking the text lines and the document boundaries into account from a constrained optimization perspective. Specifically, our proposed method first learns the boundary points and the pixels in the text lines and then follows the most simple observation that the boundaries and text lines in both horizontal and vertical directions should be kept after dewarping to introduce a novel grid regularization scheme. To obtain the final forward mapping for dewarping, we solve an optimization problem with our proposed grid regularization. The experiments comprehensively demonstrate that our proposed approach outperforms the prior arts by large margins in terms of readability (with the metrics of Character Errors Rate and the Edit Distance) while maintaining the best image quality on the publicly-available DocUNet benchmark.

关键词： Measurement Image quality Error analysis Neural networks Optical distortion Benchmark testing pattern recognition

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TemporalUV: Capturing Loose Clothing with Temporally Coherent UV Coordinates

TemporalUV: Capturing Loose Clothing with Temporally Coheren...

引用

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

作者： Xie, You Mao, Huiqi Yao, Angela Thuerey, Nils Tech Univ Munich Dept Informat Munich Germany Natl Univ Singapore Dept Comp Sci Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose a novel approach to generate temporally coherent UV coordinates for loose clothing. Our method is not constrained by human body outlines and can capture loose garments and hair. We implemented a differentiable pipeline to learn UV mapping between a sequence of RGB inputs and textures via UV coordinates. Instead of treating the UV coordinates of each frame separately, our data generation approach connects all UV coordinates via feature matching for temporal stability. Subsequently, a generative model is trained to balance the spatial quality and temporal stability. It is driven by supervised and unsupervised losses in both UV and image spaces. Our experiments show that the trained models output high-quality UV coordinates and generalize to new poses. Once a sequence of UV coordinates has been inferred by our model, it can be used to flexibly synthesize new looks and modified visual styles. Compared to existing methods, our approach reduces the computational workload to animate new outfits by several orders of magnitude.

关键词： Hair Visualization computer vision Computational modeling Clothing Pipelines Stability analysis

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AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval

AxIoU: An Axiomatically Justified Measure for Video Moment R...

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

作者： Togashi, Riku Otani, Mayu Nakashima, Yuta Rahtu, Esa Heikkila, Janne Sakai, Tetsuya Waseda Univ Cyberagent Inc Tokyo Japan Cyberagent Inc Tokyo Japan Osaka Univ Suita Osaka Japan Tampere Univ Tampere Finland Univ Oulu Oulu Finland Waseda Univ Tokyo Japan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Evaluation measures have a crucial impact on the direction of research. Therefore, it is of utmost importance to develop appropriate and reliable evaluation measures for new applications where conventional measures are not well suited. Video Moment Retrieval (VMR) is one such application, and the current practice is to use R@ K, theta for evaluating VMR systems. However, this measure has two disadvantages. First, it is rank-insensitive: It ignores the rank positions of successfully localised moments in the top-K ranked list by treating the list as a set. Second, it binarizes the Intersection over Union (IoU) of each retrieved video moment using the threshold theta and thereby ignoring fine-grained localisation quality of ranked moments. We propose an alternative measure for evaluating VMR, called Average Max IoU (AxIoU), which is free from the above two problems. We show that AxIoU satisfies two important axioms for VMR evaluation, namely, Invariance against Redundant Moments and Monotonicity with respect to the Best Moment, and also that R@ K, theta satisfies the first axiom only. We also empirically examine how AxIoU agrees with R@ K, theta, as well as its stability with respect to change in the test data and human-annotated temporal boundaries.

关键词： computer vision Current measurement Computational modeling Stability analysis pattern recognition Reliability

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DualGraph: A graph-based method for reasoning about label noise

DualGraph: A graph-based method for reasoning about label no...

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

作者： Zhang, HaiYang Xing, XiMing Liu, Liang Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9781665445092

Unreliable labels derived from large-scale dataset prevent neural networks from fully exploring the data. Existing methods of learning with noisy labels primarily take noise-cleaning-based and sample-selection-based methods. However, for numerous studies on account of the above two views, selected samples cannot take full advantage of all data points and cannot represent actual distribution of categories, in particular if label annotation is corrupted. In this paper, we start from a different perspective and propose a robust learning algorithm called DualGraph, which aims to capture structural relations among labels at two different levels with graph neural networks including instance-level and distribution-level relations. Specifically, the instance-level relation utilizes instance similarity characterize sample category, while the distribution-level relation describes instance similarity distribution from each sample to all other samples. Since the distribution-level relation is robust to label noise, our network propagates it as supervised signals to refine instance-level similarity. Combining two level relations, we design an end-to-end training paradigm to counteract noisy labels while generating reliable predictions. We conduct extensive experiments on the noisy CIFAR-10 dataset, CIFAR-100 dataset, and the Clothing1M dataset. The results demonstrate the advantageous performance of the proposed method in comparison to state-of-the-art baselines.

关键词： Training computer vision Recurrent neural networks Reliability engineering Graph neural networks Robustness pattern recognition

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