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An Efficient Training Approach for Very Large Scale Face recognition

An Efficient Training Approach for Very Large Scale Face Rec...

引用

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

作者： Wang, Kai Wang, Shuo Zhang, Panpan Zhou, Zhipeng Zhu, Zheng Wang, Xiaobo Peng, Xiaojiang Sun, Baigui Li, Hao You, Yang Natl Univ Singapore Singapore Singapore Alibaba Grp Hangzhou Peoples R China Tsinghua Univ Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Shenzhen Technol Univ Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Face recognition has achieved significant progress in deep learning era due to the ultra-large-scale and well-labeled datasets. However, training on the outsize datasets is time-consuming and takes up a lot of hardware resource. Therefore, designing an efficient training approach is indispensable. The heavy computational and memory costs mainly result from the million-level dimensionality of the fully connected (FC) layer. To this end, we propose a novel training approach, termed Faster Face Classification ((FC)-C-2), to alleviate time and cost without sacrificing the performance. This method adopts Dynamic Class Pool (DCP) for storing and updating the identities' features dynamically, which could be regarded as a substitute for the FC layer. DCP is efficiently time-saving and cost-saving, as its smaller size with the independence from the whole face identities together. We further validate the proposed (FC)-C-2 method across several face benchmarks and private datasets, and display comparable results, meanwhile the speed is faster than state-of-the-art FC-based methods in terms of recognition accuracy and hardware costs. Moreover, our method is further improved by a well-designed dual data loader including indentity-based and instance-based loaders, which makes it more efficient for updating DCP parameters.

关键词： Training Deep learning Technological innovation Ethics computer vision Costs Face recognition

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Exploring Geometric Consistency for Monocular 3D Object Detection

Exploring Geometric Consistency for Monocular 3D Object Dete...

引用

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

作者： Lian, Qing Ye, Botao Xu, Ruijia Yao, Weilong Zhang, Tong Hong Kong Univ Sci & Technol Hong Kong Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Autowise AI Hayward CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper investigates the geometric consistency for monocular 3D object detection, which suffers from the ill-posed depth estimation. We first conduct a thorough analysis to reveal how existing methods fail to consistently localize objects when different geometric shifts occur. In particular, we design a series of geometric manipulations to diagnose existing detectors and then illustrate their vulnerability to consistently associate the depth with object apparent sizes and positions. To alleviate this issue, we propose four geometry-aware data augmentation approaches to enhance the geometric consistency of the detectors. We first modify some commonly used data augmentation methods for 2D images so that they can maintain geometric consistency in 3D spaces. We demonstrate such modifications are important. In addition, we propose a 3D-specific image perturbation method that employs the camera movement. During the augmentation process, the camera system with the corresponding image is manipulated, while the geometric visual cues for depth recovery are preserved. We show that by using the geometric consistency constraints, the proposed augmentation techniques lead to improvements on the KITTI and nuScenes monocular 3D detection benchmarks with state-of-the-art results. In addition, we demonstrate that the augmentation methods are well suited for semi-supervised training and cross-dataset generalization.

关键词： Training Visualization computer vision Three-dimensional displays Perturbation methods Estimation Object detection

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Discovering Objects that Can Move

Discovering Objects that Can Move

引用

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

作者： Bao, Zhipeng Tokmakov, Pavel Jabri, Allan Wang, Yu-Xiong Gaidon, Adrien Hebert, Martial CMU Pittsburgh PA 15213 USA Toyota Res Inst Tokyo Japan Univ Calif Berkeley Berkeley CA USA UIUC Champaign IL USA TRI Tokyo Japan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper studies the problem of object discovery - separating objects from the background without manual labels. Existing approaches utilize appearance cues, such as color, texture, and location, to group pixels into object-like regions. However, by relying on appearance alone, these methods fail to separate objects from the background in cluttered scenes. This is a fundamental limitation since the definition of an object is inherently ambiguous and context-dependent. To resolve this ambiguity, we choose to focus on dynamic objects - entities that can move independently in the world We then scale the recent auto-encoder based frameworks for unsupervised object discovery from toy synthetic images to complex real-world scenes. 'lb this end, we simplify their architecture, and augment the resulting model with a weak learning signal from general motion segmentation algorithms. Our experiments demonstrate that, despite only capturing a small subset of the objects that move, this signal is enough to generalize to segment both moving and static instances of dynamic objects. We show that our model scales to a newly collected, photo-realistic synthetic dataset with street driving scenarios. Additionally, we leverage ground truth segmentation and flow annotations in this dataset for thorough ablation and evaluation. Finally, our experiments on the real-world KITTI benchmark demonstrate that the proposed approach outperforms both heuristic- and learning-based methods by capitalizing on motion cues.

关键词： Representation learning Learning systems computer vision Tracking Shape Motion segmentation Dynamics

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Voice Activity Detection by Upper Body Motion Analysis and Unsupervised Domain Adaptation 17

Voice Activity Detection by Upper Body Motion Analysis and U...

引用

ieee/cvf International conference on computer vision (ICCV)

作者： Shahid, Muhammad Beyan, Cigdem Murino, Vittorio Ist Italian Tecnol Pattern Anal & Comp Vis Genoa Italy

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150239

We present a novel vision-biased voice activity detection (VAD) method that relies only on automatic upper body motion (UBM) analysis. Traditionally, VAO is performed using audio features only, but the use of visual cues instead of audio can be desirable especially when audio is not available such as due to technical, ethical or legal issues. Psychology literature confirms that the way people move while speaking, is different from while they are not speaking. This motivates us to claim that an effective representation-of UBM can he used to detect "Who is Speaking and When". On the other hand, the way people move during their speech varies a lot from culture to culture, and even person to person in the same culture. This results in unrelated UBM representations, such that the distribution of training and test data becomes disparate. To overcome this, we combine stacked sparse autoencoders and simple subspace alignment methods while a classifier is jointly learned using the VAD labels of the training data only. This yields new domain invariant feature representations for training and test data, showing improved VAD results. Our approach is applicable to any person without requiring re-training The tests applied on a publicly available real-life VAD dataset show better results as compared to the state-qf-the-art video-only VAD methods. Moreover the ablation study justifies the superiority of the proposed method and demonstrates the positive contribution of each component.

关键词： Feature extraction Visualization Face Training Mouth Lips Voice activity detection

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The Flag Median and FlagIRLS

The Flag Median and FlagIRLS

引用

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

作者： Mankovich, Nathan King, Emily J. Peterson, Chris Kirby, Michael Colorado State Univ Ft Collins CO 80523 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Finding prototypes (e.g., mean and median) for a dataset is central to a number of common machine learning algorithms. Subspaces have been shown to provide useful, robust representations for datasets of images, videos and more. Since subspaces correspond to points on a Grassmann manifold, one is led to consider the idea of a subspace prototype for a Grassmann-valued dataset. While a number of different subspace prototypes have been described, the calculation of some of these prototypes has proven to be computationally expensive while other prototypes are affected by outliers and produce highly imperfect clustering on noisy data. This work proposes a new subspace prototype, the flag median, and introduces the FlagIRLS algorithm for its calculation. We provide evidence that the flag median is robust to outliers and can be used effectively in algorithms like Linde-Buzo-Grey (LBG) to produce improved clusterings on Grassmannians. Numerical experiments include a synthetic dataset, the MNIST handwritten digits dataset, the Mind's Eye video dataset and the UCF YouTube action dataset. The flag median is compared the other leading algorithms for computing prototypes on the Grassmannian, namely, the l(2)-median and to the flag mean. We find that using FlagIRLS to compute the flag median converges in 4 iterations on a synthetic dataset. We also see that Grassmannian LBG with a codebook size of 20 and using the flag median produces at least a 10% improvement in cluster purity over Grassmannian LBG using the flag mean or l(2) -median on the Mind's Eye dataset.

关键词： Manifolds computer vision Machine learning algorithms Video on demand Pose estimation Prototypes Clustering algorithms

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Progressive Semantic-Guided vision Transformer for Zero-Shot Learning

Progressive Semantic-Guided Vision Transformer for Zero-Shot...

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

作者： Chen, Shiming Hou, Wenjin Khan, Salman Khan, Fahad Shahbaz Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates Huazhong Univ Sci & Technol Wuhan Peoples R China Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden

ISBN: (纸本)9798350353006

Zero-shot learning (ZSL) recognizes the unseen classes by conducting visual-semantic interactions to transfer semantic knowledge from seen classes to unseen ones, supported by semantic information (e.g., attributes). However, existing ZSL methods simply extract visual features using a pre-trained network backbone (i.e., CNN or ViT), which fail to learn matched visual-semantic correspondences for representing semantic-related visual features as lacking of the guidance of semantic information, resulting in undesirable visual-semantic interactions. To tackle this issue, we propose a progressive semantic-guided vision transformer for zero-shot learning (dubbed ZSLViT). ZSLViT mainly considers two properties in the whole network: i) discover the semantic-related visual representations explicitly, and ii) discard the semantic-unrelated visual information. Specifically, we first introduce semantic-embedded token learning to improve the visual-semantic correspondences via semantic enhancement and discover the semantic-related visual tokens explicitly with semantic-guided token attention. Then, we fuse low semantic-visual correspondence visual tokens to discard the semantic-unrelated visual information for visual enhancement. These two operations are integrated into various encoders to progressively learn semantic-related visual representations for accurate visual-semantic interactions in ZSL. The extensive experiments show that our ZSLViT achieves significant performance gains on three popular benchmark datasets, i.e., CUB, SUN, and AWA2.

关键词： zero-shot learning

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GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet

GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet

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

作者： You, Shan Huang, Tao Yang, Mingmin Wang, Fei Qian, Chen Zhang, Changshui SenseTime Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China Huazhong Univ Sci & Technol Sch CST Dian Grp Wuhan Peoples R China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171685

Training a supernet matters for one-shot neural architecture search (NAS) methods since it serves as a basic performance estimator for different architectures (paths). Current methods mainly hold the assumption that a supernet should give a reasonable ranking over all paths. They thus treat all paths equally, and spare much effort to train paths. However, it is harsh for a single supernet to evaluate accurately on such a huge-scale search space (e.g., 7(21)). In this paper, instead of covering all paths, we ease the burden of supernet by encouraging it to focus more on evaluation of those potentially-good ones, which are identified using a surrogate portion of validation data. Concretely, during training, we propose a multi-path sampling strategy with rejection, and greedily filter the weak paths. The training efficiency is thus boosted since the training space has been greedily shrunk from all paths to those potentially-good ones. Moreover;we further adopt an exploration and exploitation policy by introducing an empirical candidate path pool. Our proposed method GreedyNAS is easy-to-follow, and experimental results on ImageNet dataset indicate that it can achieve better Top-1 accuracy under same search space and FLOPs or latency level, but with only similar to 60% of supernet training cost. By searching on a larger space, our GreedyNAS can also obtain new state-of-the-art architectures.

关键词： Training computer architecture Task analysis Graphics processing units Hardware computer vision Estimation

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AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Network

AP-BSN: Self-Supervised Denoising for Real-World Images via ...

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

作者： Lee, Wooseok Son, Sanghyun Lee, Kyoung Mu Seoul Natl Univ Dept 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

Blind-spot network (BSN) and its variants have made significant advances in self-supervised denoising. Nevertheless, they are still bound to synthetic noisy inputs due to less practical assumptions like pixel-wise independent noise. Hence, it is challenging to deal with spatially correlated real-world noise using self-supervised BSN. Recently, pixel-shuffle downsampling (PD) has been proposed to remove the spatial correlation of real-world noise. However, it is not trivial to integrate PD and BSN directly, which prevents the fully self-supervised denoising model on realworld images. We propose an Asymmetric PD (AP) to address this issue, which introduces different PD stride factors for training and inference. We systematically demonstrate that the proposed AP can resolve inherent trade-offs caused by specific PD stride factors and make BSN applicable to practical scenarios. To this end, we develop AP-BSN, a state-of-the-art self-supervised denoising method for realworld sRGB images. We further propose random-replacing refinement, which significantly improves the performance of our AP-BSN without any additional parameters. Extensive studies demonstrate that our method outperforms the other self-supervised and even unpaired denoising methods by a large margin, without using any additional knowledge, e.g., noise level, regarding the underlying unknown noise.

关键词： Training Photography Visualization Image resolution Correlation Noise reduction pattern recognition

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TWIST: Two-Way Inter-label Self-Training for Semi-supervised 3D Instance Segmentation

TWIST: Two-Way Inter-label Self-Training for Semi-supervised...

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

作者： Chu, Ruihang Ye, Xiaoqing Liu, Zhengzhe Tan, Xiao Qi, Xiaojuan Fu, Chi-Wing Jia, Jiaya CUHK Hong Kong Peoples R China Baidu Inc Beijing Peoples R China HKU Hong Kong Peoples R China SHIAE Hong Kong Peoples R China SmartMore Guangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We explore the way to alleviate the label-hungry problem in a semi-supervised setting for 3D instance segmentation. To leverage the unlabeled data to boost model performance, we present a novel Two-Way Inter-label Self-Training framework named TWIST. It exploits inherent correlations between semantic understanding and instance information of a scene. Specifically, we consider two kinds of pseudo labels for semantic- and instance-level supervision. Our key design is to provide object-level information for denoising pseudo labels and make use of their correlation for two-way mutual enhancement, thereby iteratively promoting the pseudo-label qualities. TWIST attains leading performance on both ScanNet and S3DIS, compared to recent 3D pre-training approaches, and can cooperate with them to further enhance performance, e.g., +4.4% AP(50) on 1%-label ScanNet data-efficient benchmark.

关键词： Training Three-dimensional displays Correlation Semantics Noise reduction Data models pattern recognition

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Multi-shot Temporal Event Localization: a Benchmark

Multi-shot Temporal Event Localization: a Benchmark

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

作者： Liu, Xiaolong Hu, Yao Bai, Song Ding, Fei Bai, Xiang Torr, Philip H. S. Huazhong Univ Sci & Technol Wuhan Peoples R China Alibaba Grp Hangzhou Peoples R China Univ Oxford Oxford England

ISBN: (纸本)9781665445092

Current developments in temporal event or action localization usually target actions captured by a single camera. However, extensive events or actions in the wild may be captured as a sequence of shots by multiple cameras at different positions. In this paper, we propose a new and challenging task called multi-shot temporal event localization, and accordingly, collect a large-scale dataset called MUlti-Shot EventS (MUSES). MUSES has 31,477 event instances for a total of 716 video hours. The core nature of MUSES is the frequent shot cuts, for an average of 19 shots per instance and 176 shots per video, which induces large intra-instance variations. Our comprehensive evaluations show that the state-of-the-art method in temporal action localization only achieves an mAP of 13.1% at IoU=0.5. As a minor contribution, we present a simple baseline approach for handling the intra-instance variations, which reports an mAP of 18.9% on MUSES and 56.9% on THUMOS14 at IoU=0.5. To facilitate research in this direction, we release the dataset and the project code at https://***/muses/.

关键词： Location awareness computer vision TV Codes Annotations Benchmark testing Motion pictures

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