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LAFS: Landmark-Based Facial Self-Supervised Learning for Face recognition

LAFS: Landmark-Based Facial Self-Supervised Learning for Fac...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Zhonglin Sun Chen Feng Ioannis Patras Georgios Tzimiropoulos Queen Mary University of London London UK

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In this work we focus on learning facial representations that can be adapted to train effective face recognition models, particularly in the absence of labels. Firstly, compared with existing labelled face datasets, a vastly larger magnitude of unlabeled faces exists in the real world. We explore the learning strategy of these unlabeled facial images through self-supervised pretraining to transfer gener-alized face recognition performance. Moreover, motivated by one recent finding, that is, the face saliency area is critical for face recognition, in contrast to utilizing random cropped blocks of images for constructing augmentations in pretraining, we utilize patches localized by extracted facial landmarks. This enables our method - namely LAndmark-based Facial Self-supervised learning (LAFS), to learn key representation that is more critical for face recognition. We also incorporate two landmark-specific augmen-tations which introduce more diversity of landmark information to further regularize the learning. With learned landmark-based facial representations, we further adapt the representation for face recognition with regularization mitigating variations in landmark positions. Our method achieves significant improvement over the state-of-the-art on multiple face recognition benchmarks, especially on more challenging few-shot scenarios. The code is available at https://***/szlbiubiubiulLAFS_cvpr2024.

关键词： Training computer vision Adaptation models Codes Accuracy Face recognition Perturbation methods

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Prototype Completion with Primitive Knowledge for Few-Shot Learning

Prototype Completion with Primitive Knowledge for Few-Shot L...

引用

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

作者： Zhang, Baoquan Li, Xutao Ye, Yunming Huang, Zhichao Zhang, Lisai Harbin Inst Technol Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Few-shot learning is a challenging task, which aims to learn a classifier for novel classes with few examples. Pretraining based meta-learning methods effectively tackle the problem by pre-training a feature extractor and then fine-tuning it through the nearest centroid based meta-learning. However, results show that the fine-tuning step makes very marginal improvements. In this paper, 1) we figure out the key reason, i.e., in the pre-trained feature space, the base classes already form compact clusters while novel classes spread as groups with large variances, which implies that fine-tuning the feature extractor is less meaningful;2) instead of fine-tuning the feature extractor, we focus on estimating more representative prototypes during meta-learning. Consequently, we propose a novel prototype completion based meta-learning framework. This framework first introduces primitive knowledge (i.e., class-level part or attribute annotations) and extracts representative attribute features as priors. Then, we design a prototype completion network to learn to complete prototypes with these priors. To avoid the prototype completion error caused by primitive knowledge noises or class differences, we further develop a Gaussian based prototype fusion strategy that combines the mean-based and completed prototypes by exploiting the unlabeled samples. Extensive experiments show that our method: (i) can obtain more accurate prototypes;(ii) outperforms state-of-the-art techniques by 2% similar to 9% in terms of classification accuracy. Our code is available online(1).

关键词： Knowledge engineering computer vision Codes Annotations Computational modeling Prototypes Feature extraction

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DiLiGenRT: A Photometric Stereo Dataset with Quantified Roughness and Translucency

DiLiGenRT: A Photometric Stereo Dataset with Quantified Roug...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Heng Guo Jieji Ren Feishi Wang Boxin Shi Mingjun Ren Yasuyuki Matsushita School of Artificial Intelligence Beijing University of Posts and Telecommunications School of Mechanical Engineering Shanghai Jiao Tong University National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University AI Innovation Center School of Computer Science Peking University Graduate School of Information Science and Technology Osaka University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Photometric stereo faces challenges from non-Lambertian reflectance in real-world scenarios. Systematically measuring the reliability of photometric stereo methods in handling such complex reflectance necessitates a real-world dataset with quantitatively controlled reflectances. This paper introduces DiLiGenRT, the first real-world dataset for evaluating photometric stereo methods under quantified reflectances by manufacturing 54 hemispheres with varying degrees of two reflectance properties: Roughness and Transluency, Unlike qualitative and semantic labels, such as “diffuse” and “specular,” that have been used in previous datasets, our quantified dataset allows comprehensive and systematic benchmark evaluations. In addition, it facilitates selecting best-fit photometric stereo methods based on the quantitative reflectance properties. Our dataset and benchmark results are available at https://***/***.

关键词： Reflectivity computer vision Systematics Semantics Benchmark testing pattern recognition Manufacturing

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Flow Guided Transformable Bottleneck Networks for Motion Retargeting

Flow Guided Transformable Bottleneck Networks for Motion Ret...

引用

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

作者： Ren, Jian Chai, Menglei Woodford, Oliver J. Olszewski, Kyle Tulyakov, Sergey Snap Inc Santa Monica CA 90405 USA

ISBN: (纸本)9781665445092

Human motion retargeting aims to transfer the motion of one person in a "driving" video or set of images to another person. Existing efforts leverage a long training video from each target person to train a subject-specific motion transfer model. However, the scalability of such methods is limited, as each model can only generate videos for the given target subject, and such training videos are labor-intensive to acquire and process. Few-shot motion transfer techniques, which only require one or a few images from a target, have recently drawn considerable attention. Methods addressing this task generally use either 2D or explicit 3D representations to transfer motion, and in doing so, sacrifice either accurate geometric modeling or the flexibility of an end-to-end learned representation. Inspired by the Transformable Bottleneck Network, which renders novel views and manipulations of rigid objects, we propose an approach based on an implicit volumetric representation of the image content, which can then be spatially manipulated using volumetric flow fields. We address the challenging question of how to aggregate information across different body poses, learning flow fields that allow for combining content from the appropriate regions of input images of highly non-rigid human subjects performing complex motions into a single implicit volumetric representation. This allows us to learn our 3D representation solely from videos of moving people. Armed with both 3D object understanding and end-to-end learned rendering, this categorically novel representation delivers state-of-the-art image generation quality, as shown by our quantitative and qualitative evaluations.

关键词： Training computer vision Three-dimensional displays Image synthesis Scalability Pose estimation Rendering (computer graphics)

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Proximal Splitting Adversarial Attack for Semantic Segmentation

Proximal Splitting Adversarial Attack for Semantic Segmentat...

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conference on computer vision and pattern recognition (cvpr)

作者： Jérôme Rony Jean-Christophe Pesquet Ismail Ben Ayed ÉTS Montréal Centre de Vision Numérique Université Paris-Saclay CentraleSupélec Inria

Classification has been the focal point of research on adversarial attacks, but only a few works investigate methods suited to denser prediction tasks, such as semantic segmentation. The methods proposed in these works do not accurately solve the adversarial segmentation problem and, therefore, overestimate the size of the perturbations required to fool models. Here, we propose a white-box attack for these models based on a proximal splitting to produce adversarial perturbations with much smaller $\ell_{\infty}$ norms. Our attack can handle large numbers of constraints within a nonconvex minimization framework via an Augmented Lagrangian approach, coupled with adaptive constraint scaling and masking strategies. We demonstrate that our attack significantly outperforms previously proposed ones, as well as classification attacks that we adapted for segmentation, providing a first comprehensive benchmark for this dense task.

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Learning Scene Structure Guidance via Cross-Task Knowledge Transfer for Single Depth Super-Resolution

Learning Scene Structure Guidance via Cross-Task Knowledge T...

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

作者： Sun, Baoli Ye, Xinchen Li, Baopu Li, Haojie Wang, Zhihui Xu, Rui Dalian Univ Technol Int Sch Informat Sci & Engn Dalian Peoples R China Key Lab Ubiquitous Network & Serv Software Liaoni Dalian Peoples R China Baidu Res Beijing Peoples R China

ISBN: (纸本)9781665445092

Existing color-guided depth super-resolution (DSR) approaches require paired RGB-D data as training samples where the RGB image is used as structural guidance to recover the degraded depth map due to their geometrical similarity. However, the paired data may be limited or expensive to be collected in actual testing environment. Therefore, we explore for the first time to learn the cross-modality knowledge at training stage, where both RGB and depth modalities are available, but test on the target dataset, where only single depth modality exists. Our key idea is to distill the knowledge of scene structural guidance from RGB modality to the single DSR task without changing its network architecture. Specifically, we construct an auxiliary depth estimation (DE) task that takes an RGB image as input to estimate a depth map, and train both DSR task and DE task collaboratively to boost the performance of DSR. Upon this, a cross-task interaction module is proposed to realize bilateral cross-task knowledge transfer. First, we design a cross-task distillation scheme that encourages DSR and DE networks to learn from each other in a teacher-student role-exchanging fashion. Then, we advance a structure prediction (SP) task that provides extra structure regularization to help both DSR and DE networks learn more informative structure representations for depth recovery. Extensive experiments demonstrate that our scheme achieves superior performance in comparison with other DSR methods.

关键词： Training Knowledge engineering Runtime Superresolution Network architecture pattern recognition Task analysis

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Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation

Pose-Controllable Talking Face Generation by Implicitly Modu...

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

作者： Zhou, Hang Sun, Yasheng Wu, Wayne Loy, Chen Change Wang, Xiaogang Liu, Ziwei Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Tokyo Inst Technol Tokyo Japan Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9781665445092

While accurate lip synchronization has been achieved for arbitrary-subject audio-driven talking face generation, the problem of how to efficiently drive the head pose remains. Previous methods rely on pre-estimated structural information such as landmarks and 3D parameters, aiming to generate personalized rhythmic movements. However, the inaccuracy of such estimated information under extreme conditions would lead to degradation problems. In this paper, we propose a clean yet effective framework to generate posecontrollable talking faces. We operate on non-aligned raw face images, using only a single photo as an identity reference. The key is to modularize audio-visual representations by devising an implicit low-dimension pose code. Substantially, both speech content and head pose information lie in a joint non-identity embedding space. While speech content information can be defined by learning the intrinsic synchronization between audio-visual modalities, we identify that a pose code will be complementarily learned in a modulated convolution-based reconstruction framework. Extensive experiments show that our method generates accurately lip-synced talking faces whose poses are controllable by other videos. Moreover, our model has multiple advanced capabilities including extreme view robustness and talking face frontalization.(1)

关键词： Three-dimensional displays Speech coding Face recognition Lips Speech recognition Aerospace electronics Robustness

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Positional Encoding as Spatial Inductive Bias in GANs

Positional Encoding as Spatial Inductive Bias in GANs

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

作者： Xu, Rui Wang, Xintao Chen, Kai Zhou, Bolei Loy, Chen Change Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China Nanyang Technol Univ S Lab Singapore Singapore Tencent PCG Appl Res Ctr Shenzhen Peoples R China SenseTime Res Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9781665445092

SinGAN shows impressive capability in learning internal patch distribution despite its limited effective receptive field. We are interested in knowing how such a translation-invariant convolutional generator could capture the global structure with just a spatially i.i.d. input. In this work, taking SinGAN and StyleGAN2 as examples, we show that such capability, to a large extent, is brought by the implicit positional encoding when using zero padding in the generators. Such positional encoding is indispensable for generating images with high fidelity. The same phenomenon is observed in other generative architectures such as DCGAN and PGGAN. We further show that zero padding leads to an unbalanced spatial bias with a vague relation between locations. To offer a better spatial inductive bias, we investigate alternative positional encodings and analyze their effects. Based on a more flexible positional encoding explicitly, we propose a new multi-scale training strategy and demonstrate its effectiveness in the state-of-the-art unconditional generator StyleGAN2. Besides, the explicit spatial inductive bias substantially improves SinGAN for more versatile image manipulation.

关键词： Training computer vision Image coding Image synthesis computer architecture Generators Encoding

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Group-aware Label Transfer for Domain Adaptive Person Re-identification

Group-aware Label Transfer for Domain Adaptive Person Re-ide...

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

作者： Zheng, Kecheng Liu, Wu He, Lingxiao Mei, Tao Luo, Jiebo Zha, Zheng-Jun Univ Sci & Technol China Hefei Peoples R China AI Res JD Mountain View CA USA Univ Rochester Rochester NY 14627 USA

ISBN: (纸本)9781665445092

Unsupervised Domain Adaptive (UDA) person reidentification (ReID) aims at adapting the model trained on a labeled source-domain dataset to a target-domain dataset without any further annotations. Most successful UDA-ReID approaches combine clustering-based pseudo-label prediction with representation learning and perform the two steps in an alternating fashion. However, offline interaction between these two steps may allow noisy pseudo labels to substantially hinder the capability of the model. In this paper, we propose a Group-aware Label Transfer (GLT) algorithm, which enables the online interaction and mutual promotion of pseudo-label prediction and representation learning. Specifically, a label transfer algorithm simultaneously uses pseudo labels to train the data while refining the pseudo labels as an online clustering algorithm. It treats the online label refinery problem as an optimal transport problem, which explores the minimum cost for assigning M samples to N pseudo labels. More importantly, we introduce a group-aware strategy to assign implicit attribute group IDs to samples. The combination of the online label refining algorithm and the group-aware strategy can better correct the noisy pseudo label in an online fashion and narrow down the search space of the target identity. The effectiveness of the proposed GLT is demonstrated by the experimental results (Rank-1 accuracy) for Market1501 -> DukeMTMC (82.0%) and DukeMTMC -> Market1501 (92.2%), remarkably closing the gap between unsupervised and supervised performance on person re-identification. (1)

关键词： Training computer vision Costs Annotations Refining Clustering algorithms Prediction algorithms

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RGB No More: Minimally-Decoded JPEG vision Transformers

RGB No More: Minimally-Decoded JPEG Vision Transformers

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conference on computer vision and pattern recognition (cvpr)

作者： Jeongsoo Park Justin Johnson University of Michigan

Most neural networks for computer vision are designed to infer using RGB images. However, these RGB images are commonly encoded in JPEG before saving to disk; decoding them imposes an unavoidable overhead for RGB networks. Instead, our work focuses on training vision Transformers (ViT) directly from the encoded features of JPEG. This way, we can avoid most of the decoding overhead, accelerating data load. Existing works have studied this aspect but they focus on CNNs. Due to how these encoded features are structured, CNNs require heavy modification to their architecture to accept such data. Here, we show that this is not the case for ViTs. In addition, we tackle data augmentation directly on these encoded features, which to our knowledge, has not been explored in-depth for training in this setting. With these two improvements - ViT and data augmentation - we show that our ViT-Ti model achieves up to 39.2% faster training and 17.9% faster inference with no accuracy loss compared to the RGB counterpart. 1 1 Code available at https://***/JeongsooP/RGB-no-more

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