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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023"

共 11753 条记录，以下是4381-4390 订阅

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Convolutional Hough Matching Networks

Convolutional Hough Matching Networks

引用

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

作者： Min, Juhong Cho, Minsu POSTECH CSE Pohang South Korea POSTECH GSAI Pohang South Korea

ISBN: (纸本)9781665445092

Despite advances in feature representation, leveraging geometric relations is crucial for establishing reliable visual correspondences under large variations of images. In this work we introduce a Hough transform perspective on convolutional matching and propose an effective geometric matching algorithm, dubbed Convolutional Hough Matching (CHM). The method distributes similarities of candidate matches over a geometric transformation space and evaluate them in a convolutional manner. We cast it into a trainable neural layer with a semi-isotropic high-dimensional kernel, which learns non-rigid matching with a small number of interpretable parameters. To validate the effect, we develop the neural network with CHM layers that perform convolutional matching in the space of translation and scaling. Our method sets a new state of the art on standard benchmarks for semantic visual correspondence, proving its strong robustness to challenging intra-class variations.

关键词： Visualization computer vision Semantics Neural networks Transforms Benchmark testing Robustness

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Mitigating Face recognition Bias via Group Adaptive Classifier

Mitigating Face Recognition Bias via Group Adaptive Classifi...

引用

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

作者： Gong, Sixue Liu, Xiaoming Jain, Anil K. Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)9781665445092

Face recognition is known to exhibit bias - subjects in a certain demographic group can be better recognized than other groups. This work aims to learn a fair face representation, where faces of every group could be more equally represented. Our proposed group adaptive classifier mitigates bias by using adaptive convolution kernels and attention mechanisms on faces based on their demographic attributes. The adaptive module comprises kernel masks and channel-wise attention maps for each demographic group so as to activate different facial regions for identification, leading to more discriminative features pertinent to their demographics. Our introduced automated adaptation strategy determines whether to apply adaptation to a certain layer by iteratively computing the dissimilarity among demographic-adaptive parameters. A new de-biasing loss function is proposed to mitigate the gap of average intra-class distance between demographic groups. Experiments on face benchmarks (RFW, LFW, IJB-A, and IJB-C) show that our work is able to mitigate face recognition bias across demographic groups while maintaining the competitive accuracy.

关键词： computer vision Automation Convolution Face recognition Performance gain Benchmark testing Robustness

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Learning a Facial Expression Embedding Disentangled from Identity

Learning a Facial Expression Embedding Disentangled from Ide...

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

作者： Zhang, Wei Ji, Xianpeng Chen, Keyu Ding, Yu Fan, Changjie Netease Fuxi AI Lab Virtual Human Grp Beijing Peoples R China Univ Sci & Technol China Langfang Peoples R China

ISBN: (纸本)9781665445092

The facial expression analysis requires a compact and identity-ignored expression representation. In this paper, we model the expression as the deviation from the identity by a subtraction operation, extracting a continuous and identity-invariant expression embedding. We propose a Deviation Learning Network (DLN) with a pseudo-siamese structure to extract the deviation feature vector. To reduce the optimization difficulty caused by additional fully connection layers, DLN directly provides high-order polynomial to nonlinearly project the high-dimensional feature to a low-dimensional manifold. Taking label noise into account, we add a crowd layer to DLN for robust embedding extraction. Also, to achieve a more compact representation, we use hierarchical annotation for data augmentation. We evaluate our facial expression embedding on the FEC validation set. The quantitative results prove that we achieve the state-of-the-art, both in terms of fine-grained and identity-invariant property. We further conduct extensive experiments to show that our expression embedding is of high quality for expression recognition, image retrieval, and face manipulation.

关键词： Manifolds computer vision Image recognition Head Annotations Face recognition Image retrieval

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Mask Guided Matting via Progressive Refinement Network

Mask Guided Matting via Progressive Refinement Network

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

作者： Yu, Qihang Zhang, Jianming Zhang, He Wang, Yilin Lin, Zhe Xu, Ning Bai, Yutong Yuille, Alan Johns Hopkins Univ Baltimore MD 21218 USA Adobe San Jose CA USA

ISBN: (纸本)9781665445092

We propose Mask Guided (MG) Matting, a robust matting framework that takes a general coarse mask as guidance. MG Matting leverages a network (PRN) design which encourages the matting model to provide self-guidance to progressively refine the uncertain regions through the decoding process. A series of guidance mask perturbation operations are also introduced in the training to further enhance its robustness to external guidance. We show that PRN can generalize to unseen types of guidance masks such as trimap and low-quality alpha matte, making it suitable for various application pipelines. In addition, we revisit the foreground color prediction problem for matting and propose a surprisingly simple improvement to address the dataset issue. Evaluation on real and synthetic benchmarks shows that MG Matting achieves state-of-the-art performance using various types of guidance inputs.

关键词： Training Image segmentation Image color analysis Computational modeling Perturbation methods Pipelines Rendering (computer graphics)

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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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Enlarging Instance-specific and Class-specific Information for Open-set Action recognition

Enlarging Instance-specific and Class-specific Information f...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Jun Cen Shiwei Zhang Xiang Wang Yixuan Pei Zhiwu Qing Yingya Zhang Qifeng Chen The Hong Kong University of Science and Technology Alibaba Group Huazhong University of Science and Technology Xi'an Jiaotong University

Open-set action recognition is to reject unknown human action cases which are out of the distribution of the training set. Existing methods mainly focus on learning better uncertainty scores but dismiss the importance of feature representations. We find that features with richer semantic diversity can significantly improve the open-set performance under the same uncertainty scores. In this paper, we begin with analyzing the feature representation behavior in the open-set action recognition (OSAR) problem based on the information bottleneck (IB) theory, and propose to enlarge the instance-specific (IS) and classspecific (CS) information contained in the feature for better performance. To this end, a novel Prototypical Similarity Learning (PSL) framework is proposed to keep the instance variance within the same class to retain more IS information. Besides, we notice that unknown samples sharing similar appearances to known samples are easily misclassified as known classes. To alleviate this issue, video shuffling is further introduced in our PSL to learn distinct temporal information between original and shuffled samples, which we find enlarges the CS information. Extensive experiments demonstrate that the proposed PSL can significantly boost both the open-set and closed-set performance and achieves state-of-the-art results on multiple benchmarks. Code is available at https://***/Jun-CEN/PSL.

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Privacy Preserving Localization and Mapping from Uncalibrated Cameras

Privacy Preserving Localization and Mapping from Uncalibrate...

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

作者： Geppert, Marcel Larsson, Viktor Speciale, Pablo Schonberger, Johannes L. Pollefeys, Marc Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Microsoft Redmond WA USA

ISBN: (纸本)9781665445092

Recent works on localization and mapping from privacy preserving line features have made significant progress towards addressing the privacy concerns arising from cloud-based solutions in mixed reality and robotics. The requirement for calibrated cameras is a fundamental limitation for these approaches, which prevents their application in many crowd-sourced mapping scenarios. In this paper, we propose a solution to the uncalibrated privacy preserving localization and mapping problem. Our approach simultaneously recovers the intrinsic and extrinsic calibration of a camera from line-features only. This enables uncalibrated devices to both localize themselves within an existing map as well as contribute to the map, while preserving the privacy of the image contents. Furthermore, we also derive a solution to bootstrapping maps from scratch using only uncalibrated devices. Our approach provides comparable performance to the calibrated scenario and the privacy compromising alternatives based on traditional point features.

关键词： Location awareness Performance evaluation Privacy Robot vision systems Pose estimation Poles and towers Cameras

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IterativePFN: True Iterative Point Cloud Filtering

IterativePFN: True Iterative Point Cloud Filtering

引用

conference on computer vision and pattern recognition (cvpr)

作者： Dasith de Silva Edirimuni Xuequan Lu Zhiwen Shao Gang Li Antonio Robles-Kelly Ying He School of Information Technology Deakin University School of Computer Science and Technology China University of Mining and Technology Defense Science and Technology Group Australia School of Computer Science and Engineering Nanyang Technological University

The quality of point clouds is often limited by noise introduced during their capture process. Consequently, a fundamental 3D vision task is the removal of noise, known as point cloud filtering or denoising. State-of-the-art learning based methods focus on training neural networks to infer filtered displacements and directly shift noisy points onto the underlying clean surfaces. In high noise conditions, they iterate the filtering process. However, this iterative filtering is only done at test time and is less effective at ensuring points converge quickly onto the clean surfaces. We propose IterativePFN (iterative point cloud filtering network), which consists of multiple IterationModules that model the true iterative filtering process internally, within a single network. We train our IterativePFn network using a novel loss function that utilizes an adaptive ground truth target at each iteration to capture the relationship between intermediate filtering results during training. This ensures that the filtered results converge faster to the clean surfaces. Our method is able to obtain better performance compared to state-of-the-art methods. The source code can be found at: https://***/ddsediri/IterativePFN

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Tri-Perspective View for vision-Based 3D Semantic Occupancy Prediction

Tri-Perspective View for Vision-Based 3D Semantic Occupancy ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yuanhui Huang Wenzhao Zheng Yunpeng Zhang Jie Zhou Jiwen Lu Beijing National Research Center for Information Science and Technology China Department of Automation Tsinghua University China

Modern methods for vision-centric autonomous driving perception widely adopt the bird's-eye-view (BEV) representation to describe a 3D scene. Despite its better efficiency than voxel representation, it has difficulty describing the fine-grained 3D structure of a scene with a single plane. To address this, we propose a tri-perspective view (TPV) representation which accompanies BEV with two additional perpendicular planes. We model each point in the 3D space by summing its projected features on the three planes. To lift image features to the 3D TPV space, we further propose a transformer-based TPV encoder (TPVFormer) to obtain the TPV features effectively. We employ the attention mechanism to aggregate the image features corresponding to each query in each TPV plane. Experiments show that our model trained with sparse supervision effectively predicts the semantic occupancy for all voxels. We demonstrate for the first time that using only camera inputs can achieve comparable performance with LiDAR-based methods on the LiDAR segmentation task on nuScenes. Code: https://***/wzzheng/TPVFormer.

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LOHO: Latent Optimization of Hairstyles via Orthogonalization

LOHO: Latent Optimization of Hairstyles via Orthogonalizatio...

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

作者： Saha, Rohit Duke, Brendan Shkurti, Florian Taylor, Graham W. Aarabi, Parham Univ Toronto Toronto ON Canada Modiface Inc Toronto ON Canada Univ Guelph Guelph ON Canada Vector Inst Toronto ON Canada

ISBN: (纸本)9781665445092

Hairstyle transfer is challenging due to hair structure differences in the source and target hair. Therefore, we propose Latent Optimization of Hairstyles via Orthogonalization (LOHO), an optimization-based approach using GAN inversion to infill missing hair structure details in latent space during hairstyle transfer. Our approach decomposes hair into three attributes: perceptual structure, appearance, and style, and includes tailored losses to model each of these attributes independently. Furthermore, we propose two-stage optimization and gradient orthogonalization to enable disentangled latent space optimization of our hair attributes. Using LOHO for latent space manipulation, users can synthesize novel photorealistic images by manipulating hair attributes either individually or jointly, transferring the desired attributes from reference hairstyles. LOHO achieves a superior FID compared with the current state-of-the-art (SOTA) for hairstyle transfer. Additionally, LOHO preserves the subject's identity comparably well according to PSNR and SSIM when compared to SOTA image embedding pipelines.

关键词： Hair Training Head Shape Face recognition Pipelines Task analysis

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