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

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Finger Pointnet: An End-to-end Embedded Network for 3D Fingerprint Verification 4

Finger Pointnet: An End-to-end Embedded Network for 3D Finge...

引用

4th IEEE International conference on pattern recognition and Machine Learning, PRML 2023

作者： Huang, Yixin Liu, Feng Yang, Dongliang Song, Changjiang Shenzhen University Shenzhen China Heilongjiang Academy of Sciences Intelligent Manufacturing Research Institute Heilongjiang China

ISBN: (纸本)9798350324303

Contactless 3D fingerprint gains significant attention in recent years. Much of previous works for 3D fingerprint verification were based on minutiae. They required a series of laborious preprocessing and relied on software to extract minutiae. To alleviate these problems, this research presents an end-to-end embedded network for fingerprint verification. Firstly, we propose to extract ridge lines from the original large and redundant 3D fingerprint point cloud data. Then, we design two weight-shared encoders to extract features from a pair of positive 3D fingerprints. Finally, loss backpropagation from the hardest sample in a mini-batch permitted in our approach makes it can distinguish negative samples. The experiments on a public database and an in-house database verify that our approach achieves desirable performance and significantly speeds up feature extraction and matching compared with traditional approaches. © 2023 IEEE.

关键词： computer vision

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FACESEC: A Fine-grained Robustness Evaluation Framework for Face recognition Systems

FACESEC: A Fine-grained Robustness Evaluation Framework for ...

引用

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

作者： Tong, Liang Chen, Zhengzhang Ni, Jingchao Cheng, Wei Song, Dongjin Chen, Haifeng Vorobeychik, Yevgeniy Washington Univ St Louis MO 14263 USA NEC Labs Amer Princeton NJ 08540 USA Univ Connecticut Storrs CT USA

ISBN: (纸本)9781665445092

We present FACESEC, a framework for fine-grained robustness evaluation of face recognition systems. FACESEC evaluation is performed along four dimensions of adversarial modeling: the nature of perturbation (e.g., pixel-level or face accessories), the attacker's system knowledge (about training data and learning architecture), goals (dodging or impersonation), and capability (tailored to individual inputs or across sets of these). We use FACESEC to study five face recognition systems in both closed-set and open-set settings, and to evaluate the state-of-the-art approach for defending against physically realizable attacks on these. We find that accurate knowledge of neural architecture is significantly more important than knowledge of the training data in black-box attacks. Moreover, we observe that open-set face recognition systems are more vulnerable than closed-set systems under different types of attacks. The efficacy of attacks for other threat model variations, however, appears highly dependent on both the nature of perturbation and the neural network architecture. For example, attacks that involve adversarial face masks are usually more potent, even against adversarially trained models, and the ArcFace architecture tends to be more robust than the others.

关键词： computer vision Systematics Face recognition Perturbation methods Neural networks Training data computer architecture

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Semi-Supervised Action recognition with Temporal Contrastive Learning

Semi-Supervised Action Recognition with Temporal Contrastive...

引用

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

作者： Singh, Ankit Chakraborty, Omprakash Varshney, Ashutosh Panda, Rameswar Feris, Rogerio Saenko, Kate Das, Abir IIT Madras Chennai Tamil Nadu India IIT Kharagpur Kharagpur W Bengal India MIT IBM Watson AI Lab Cambridge MA USA Boston Univ Boston MA 02215 USA

ISBN: (纸本)9781665445092

Learning to recognize actions from only a handful of labeled videos is a challenging problem due to the scarcity of tediously collected activity labels. We approach this problem by learning a two-pathway temporal contrastive model using unlabeled videos at two different speeds leveraging the fact that changing video speed does not change an action. Specifically, we propose to maximize the similarity between encoded representations of the same video at two different speeds as well as minimize the similarity between different videos played at different speeds. This way we use the rich supervisory information in terms of 'time' that is present in otherwise unsupervised pool of videos. With this simple yet effective strategy of manipulating video playback rates, we considerably outperform video extensions of sophisticated state-of-the-art semi-supervised image recognition methods across multiple diverse benchmark datasets and network architectures. Interestingly, our proposed approach benefits from out-of-domain unlabeled videos showing generalization and robustness. We also perform rigorous ablations and analysis to validate our approach.

关键词： computer vision Image recognition Semantics Network architecture Benchmark testing Robustness pattern recognition

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Learning to Segment Rigid Motions from Two Frames

Learning to Segment Rigid Motions from Two Frames

引用

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

作者： Yang, Gengshan Ramanan, Deva Carnegie Mellon Univ Pittsburgh PA 15213 USA Argo AI Pittsburgh PA USA

ISBN: (纸本)9781665445092

Appearance-based detectors achieve remarkable performance on common scenes, benefiting from high-capacity models and massive annotated data, but tend to fail for scenarios that lack training data. Geometric motion segmentation algorithms, however, generalize to novel scenes, but have yet to achieve comparable performance to appearance-based ones, due to noisy motion estimations and degenerate motion configurations. To combine the best of both worlds, we propose a modular network, whose architecture is motivated by a geometric analysis of what independent object motions can be recovered from an egomotion field. It takes two consecutive frames as input and predicts segmentation masks for the background and multiple rigidly moving objects, which are then parameterized by 3D rigid transformations. Our method achieves state-of-the-art performance for rigid motion segmentation on KITTI and Sintel. The inferred rigid motions lead to a significant improvement for depth and scene flow estimation.

关键词： computer vision Three-dimensional displays Motion segmentation Motion estimation Estimation Training data computer architecture

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Learning Graphs for Knowledge Transfer with Limited Labels

Learning Graphs for Knowledge Transfer with Limited Labels

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

作者： Ghosh, Pallabi Saini, Nirat Davis, Larry S. Shrivastava, Abhinav Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781665445092

Fixed input graphs are a mainstay in approaches that utilize Graph Convolution Networks (GCNs) for knowledge transfer. The standard paradigm is to utilize relationships in the input graph to transfer information using GCNs from training to testing nodes in the graph;for example, the semi-supervised, zero-shot, and few-shot learning setups. We propose a generalized framework for learning and improving the input graph as part of the standard GCN-based learning setup. Moreover, we use additional constraints between similar and dissimilar neighbors for each node in the graph by applying triplet loss on the intermediate layer output. We present results of semi-supervised learning on Citeseer, Cora, and Pubmed benchmarking datasets, and zero/few-shot action recognition on UCF101 and HMDB51 datasets, significantly outperforming current approaches. We also present qualitative results visualizing the graph connections that our approach learns to update.

关键词： Training Visualization computer vision Convolution Semisupervised learning Benchmark testing pattern recognition

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Unsupervised Part Segmentation through Disentangling Appearance and Shape

Unsupervised Part Segmentation through Disentangling Appeara...

引用

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

作者： Liu, Shilong Zhang, Lei Yang, Xiao Su, Hang Zhu, Jun Tsinghua Univ Dept Comp Sci & Tech BNRist Ctr Inst AITsinghua Bosch Joint ML Ctr Beijing 100084 Peoples R China Microsoft Corp Redmond WA 98052 USA

ISBN: (纸本)9781665445092

We study the problem of unsupervised discovery and segmentation of object parts, which, as an intermediate local representation, are capable of finding intrinsic object structure and providing more explainable recognition results. Recent unsupervised methods have greatly relaxed the dependency on annotated data which are costly to obtain, but still rely on additional information such as object segmentation mask or saliency map. To remove such a dependency and further improve the part segmentation performance, we develop a novel approach by disentangling the appearance and shape representations of object parts followed with reconstruction losses without using additional object mask information. To avoid degenerated solutions, a bottleneck block is designed to squeeze and expand the appearance representation, leading to a more effective disentanglement between geometry and appearance. Combined with a self-supervised part classification loss and an improved geometry concentration constraint, we can segment more consistent parts with semantic meanings. Comprehensive experiments on a wide variety of objects such as face, bird, and PASCAL VOC objects demonstrate the effectiveness of the proposed method.

关键词： Geometry computer vision Shape Annotations Face recognition Semantics Neural networks

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SelfAugment: Automatic Augmentation Policies for Self-Supervised Learning

SelfAugment: Automatic Augmentation Policies for Self-Superv...

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

作者： Reed, Colorado J. Metzger, Sean Srinivas, Aravind Darrell, Trevor Keutzer, Kurt Univ Calif Berkeley BAIR Dept Comp Sci Berkeley CA 94720 USA Weill Neurosci Inst Grad Grp Bioengn Berkeley UCSF San Francisco CA USA UCSF Neurol Surg San Francisco CA USA

ISBN: (纸本)9781665445092

A common practice in unsupervised representation learning is to use labeled data to evaluate the quality of the learned representations. This supervised evaluation is then used to guide critical aspects of the training process such as selecting the data augmentation policy. However, guiding an unsupervised training process through supervised evaluations is not possible for real-world data that does not actually contain labels (which may be the case, for example, in privacy sensitive fields such as medical imaging). Therefore, in this work we show that evaluating the learned representations with a self-supervised image rotation task is highly correlated with a standard set of supervised evaluations (rank correlation > 0.94). We establish this correlation across hundreds of augmentation policies, training settings, and network architectures and provide an algorithm (SelfAugment) to automatically and efficiently select augmentation policies without using supervised evaluations. Despite not using any labeled data, the learned augmentation policies perform comparably with augmentation policies that were determined using exhaustive supervised evaluations.

关键词： Training computer vision Data privacy Correlation Image recognition Network architecture pattern recognition

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Solving Masked Jigsaw Puzzles with Diffusion vision Transformers*

Solving Masked Jigsaw Puzzles with Diffusion Vision Transfor...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Jinyang Liu Wondmgezahu Teshome Sandesh Ghimire Mario Sznaier Octavia Camps Northeastern University Boston MA Qualcomm San Diego CA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Solving image and video jigsaw puzzles poses the chal-lenging task of rearranging image fragments or video frames from unordered sequences to restore meaningful images and video sequences. Existing approaches often hinge on discriminative models tasked with predicting either the absolute positions of puzzle elements or the permutation actions applied to the original data. Unfortunately, these methods face limitations in effectively solving puzzles with a large number of elements. In this paper, we propose JPDVT, an innovative approach that harnesses diffusion transform-ers to address this challenge. Specifically, we generate positional information for image patches or video frames, conditioned on their underlying visual content. This infor-mation is then employed to accurately assemble the puzzle pieces in their correct positions, even in scenarios involving missing pieces. Our method achieves state-of-the-art performance on several datasets.

关键词： Visualization computer vision Face recognition Noise reduction Video sequences Predictive models Fasteners

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Post-hoc Uncertainty Calibration for Domain Drift Scenarios

Post-hoc Uncertainty Calibration for Domain Drift Scenarios

引用

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

作者： Tomani, Christian Gruber, Sebastian Erdem, Muhammed Ebrar Cremers, Daniel Buettner, Florian Tech Univ Munich Munich Germany Siemens AG Munich Germany German Canc Consortium Heidelberg Germany German Canc Res Ctr Heidelberg Heidelberg Germany Frankfurt Univ Frankfurt Germany

ISBN: (纸本)9781665445092

We address the problem of uncertainty calibration. While standard deep neural networks typically yield uncalibrated predictions, calibrated confidence scores that are representative of the true likelihood of a prediction can be achieved using post-hoc calibration methods. However, to date, the focus of these approaches has been on in-domain calibration. Our contribution is two-fold. First, we show that existing post-hoc calibration methods yield highly overconfident predictions under domain shift. Second, we introduce a simple strategy where perturbations are applied to samples in the validation set before performing the post-hoc calibration step. In extensive experiments, we demonstrate that this perturbation step results in substantially better calibration under domain shift on a wide range of architectures and modelling tasks.

关键词： Deep learning computer vision Uncertainty Perturbation methods Computational modeling computer architecture Calibration

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Differentiable Diffusion for Dense Depth Estimation from Multi-view Images

Differentiable Diffusion for Dense Depth Estimation from Mul...

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

作者： Khan, Numair Kim, Min H. Tompkin, James Brown Univ Providence RI 02912 USA Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665445092

We present a method to estimate dense depth by optimizing a sparse set of points such that their diffusion into a depth map minimizes a multi-view repmjection error from RGB supervision. We optimize point positions, depths, and weights with respect to the loss by differential splatting that models points as Gaussians with analytic transmittance. Further;we develop an efficient optimization routine that can simultaneously optimize the 50k+ points required for complex scene reconstruction. We validate our routine using ground truth data and show high reconstruction quality. Then, we apply this to light field and wider baseline images via self supervision, and show improvements in both average and outlier error for depth maps diffused from inaccurate sparse points. Finally, we compare qualitative and quantitative results to image processing and deep learning methods.

关键词： Deep learning computer vision Analytical models Estimation Rendering (computer graphics) Propagation losses Light fields

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