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FS6D: Few-Shot 6D Pose Estimation of Novel Objects

FS6D: Few-Shot 6D Pose Estimation of Novel Objects

引用

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

作者： He, Yisheng Wang, Yao Fan, Haoqiang Sun, Jian Chen, Qifeng Hong Kong Univ Sci & Technol Hong Kong Peoples R China Megvii Technol Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

6D object pose estimation networks are limited in their capability to scale to large numbers of object instances due to the close-set assumption and their reliance on high-fidelity object CAD models. In this work, we study a new open set problem;the few-shot 6D object poses estimation: estimating the 6D pose of an unknown object by a few support views without extra training. To tackle the problem, we point out the importance offidly exploring the appearance and geometric relationship between the given support views and query scene patches and propose a dense prototypes matching framework by extracting and matching dense RGBD prototypes with transformers. Moreover, we show that the priors from diverse appearances and shapes are crucial to the generalization capability under the problem setting and thus propose a large-scale RGBD photorealistic dataset (ShapeNet6D) for network pre-training. A simple and effective online texture blending approach is also introduced to eliminate the domain gap from the synthesis dataset, which enriches appearance diversity at a low cost. Finally, we discuss possible solutions to this problem and establish benchmarks on popular datasets to facilitate future research.

关键词： Training Solid modeling Costs Shape Pose estimation Robot vision systems Prototypes

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Learning Adaptive Warping for Real-World Rolling Shutter Correction

Learning Adaptive Warping for Real-World Rolling Shutter Cor...

引用

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

作者： Cao, Mingdeng Zhong, Zhihang Wang, Jiahao Zheng, Yinqiang Yang, Yujiu Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Beijing Peoples R China Univ Tokyo Tokyo Japan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper proposes the first real-world rolling shutter (RS) correction dataset, BS-RSC, and a corresponding model to correct the RS frames in a distorted video. Mobile devices in the consumer market with CMOS-based sensors for video capture often result in rolling shutter effects when relative movements occur during the video acquisition process, calling for RS effect removal techniques. However, current state-of-the-art RS correction methods often fail to remove RS effects in real scenarios since the motions are various and hard to model. To address this issue, we propose a real-world RS correction dataset BS-RSC. Real distorted videos with corresponding ground truth are recorded simultaneously via a well-designed beam-splitterbased acquisition system. BS-RSC contains various motions of both camera and objects in dynamic scenes. Further, an RS correction model with adaptive warping is proposed. Our model can warp the learned RS features into global shutter counterparts adaptively with predicted multiple displacement fields. These warped features are aggregated and then reconstructed into high-quality global shutter frames in a coarse-to-fine strategy. Experimental results demonstrate the effectiveness of the proposed method, and our dataset can improve the model's ability to remove the RS effects in the real world. The project is available at https://***/ljzycmd/BSRSC.

关键词： Adaptation models computer vision Computational modeling Dynamics Predictive models Cameras Mobile handsets

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Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation

Cross-Domain Adaptive Clustering for Semi-Supervised Domain ...

引用

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

作者： Li, Jichang Li, Guanbin Shi, Yemin Yu, Yizhou Univ Hong Kong Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Deepwise AI Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

In semi-supervised domain adaptation, a few labeled samples per class in the target domain guide features of the remaining target samples to aggregate around them. However, the trained model cannot produce a highly discriminative feature representation for the target domain because the training data is dominated by labeled samples from the source domain. This could lead to disconnection between the labeled and unlabeled target samples as well as misalignment between unlabeled target samples and the source domain. In this paper, we propose a novel approach called Cross-domain Adaptive Clustering to address this problem. To achieve both inter-domain and intra-domain adaptation, we first introduce an adversarial adaptive clustering loss to group features of unlabeled target data into clusters and perform cluster-wise feature alignment across the source and target domains. We further apply pseudo labeling to unlabeled samples in the target domain and retain pseudo-labels with high confidence. Pseudo labeling expands the number of "labeled" samples in each class in the target domain, and thus produces a more robust and powerful cluster core for each class to facilitate adversarial learning. Extensive experiments on benchmark datasets, including DomainNet, Office-Home and Office, demonstrate that our proposed approach achieves the state-of-the-art performance in semi-supervised domain adaptation.

关键词： Training Adaptation models computer vision Training data Benchmark testing Data models Adversarial machine learning

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Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning

Rethinking Class Relations: Absolute-relative Supervised and...

引用

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

作者： Zhang, Hongguang Koniusz, Piotr Jian, Songlei Li, Hongdong Torr, Philip H. S. AMS Syst Engn Inst Beijing Peoples R China Australian Natl Univ Canberra ACT Australia Data61 CSIRO Sydney NSW Australia Univ Oxford Oxford England Natl Univ Def Technol Changsha Hunan Peoples R China

ISBN: (纸本)9781665445092

The majority of existing few-shot learning methods describe image relations with binary labels. However, such binary relations are insufficient to teach the network complicated real-world relations, due to the lack of decision smoothness. Furthermore, current few-shot learning models capture only the similarity via relation labels, but they are not exposed to class concepts associated with objects, which is likely detrimental to the classification performance due to underutilization of the available class labels. For instance, children learn the concept of tiger from a few of actual examples as well as from comparisons of tiger to other animals. Thus, we hypothesize that both similarity and class concept learning must be occurring simultaneously. With these observations at hand, we study the fundamental problem of simplistic class modeling in current few-shot learning methods. We rethink the relations between class concepts, and propose a novel Absolute-relative Learning paradigm to fully take advantage of label information to refine the image an relation representations in both supervised and unsupervised scenarios. Our proposed paradigm improves the performance of several state-of-the-art models on publicly available datasets.

关键词： Training Learning systems computer vision Protocols Annotations Animals Semantics

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Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets

Equivariance Allows Handling Multiple Nuisance Variables Whe...

引用

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

作者： Lokhande, Vishnu Suresh Chakraborty, Rudrasis Ravi, Sathya N. Singh, Vikas

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Pooling multiple neuroimaging datasets across institutions often enables improvements in statistical power when evaluating associations (e.g., between risk factors and disease outcomes) that may otherwise be too weak to detect. When there is only a single source of variability (e.g., different scanners), domain adaptation and matching the distributions of representations may suffice in many scenarios. But in the presence of more than one nuisance variable which concurrently influence the measurements, pooling datasets poses unique challenges, e.g., variations in the data can come from both the acquisition method as well as the demographics of participants (gender, age). Invariant representation learning, by itself, is ill-suited to fully model the data generation process. In this paper, we show how bringing recent results on equivariant representation learning (for studying symmetries in neural networks) instantiated on structured spaces together with simple use of classical results on causal inference provides an effective practical solution. In particular, we demonstrate how our model allows dealing with more than one nuisance variable under some assumptions and can enable analysis of pooled scientific datasets in scenarios that would otherwise entail removing a large portion of the samples. Our code is available on https://***/vsingh-group/DatasetPooling

关键词： Representation learning Neuroimaging computer vision Codes Atmospheric measurements Neural networks Particle measurements

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ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning

ResSFL: A Resistance Transfer Framework for Defending Model ...

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

作者： Li, Jingtao Rakin, Adnan Siraj Chen, Xing He, Zhezhi Fan, Deliang Chakrabarti, Chaitali Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85281 USA Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This work aims to tackle Model Inversion (MI) attack on Split Federated Learning (SFL). SFL is a recent distributed training scheme where multiple clients send intermediate activations (i. e., feature map), instead of raw data, to a central server. While such a scheme helps reduce the computational load at the client end, it opens itself to reconstruction of raw data from intermediate activation by the server. Existing works on protecting SFL only consider inference and do not handle attacks during training. So we propose ResSFL, a Split Federated Learning Framework that is designed to be MI-resistant during training. It is based on deriving a resistant feature extractor via attacker-aware training, and using this extractor to initialize the client-side model prior to standard SFL training. Such a method helps in reducing the computational complexity due to use of strong inversion model in client-side adversarial training as well as vulnerability of attacks launched in early training epochs. On CIFAR-100 dataset, our proposed framework successfully mitigates MI attack on a VGG-11 model with a high reconstruction Mean-Square-Error of 0.050 compared to 0.005 obtained by the baseline system. The frame-work achieves 67.5% accuracy (only 1 % accuracy drop) with very low computation overhead. Code is released at: https://***/zlijingtao/ResSFL.

关键词： Training Resistance Federated learning Computational modeling Feature extraction pattern recognition Servers

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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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Visual Atoms: Pre-training vision Transformers with Sinusoidal Waves

Visual Atoms: Pre-training Vision Transformers with Sinusoid...

引用

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

作者： Takashima, Sora Hayamizu, Ryo Inoue, Nakamasa Kataoka, Hirokatsu Yokota, Rio Natl Inst Adv Ind Sci & Technol Tokyo Japan Tokyo Inst Technol Tokyo Japan

ISBN: (纸本)9798350301298

Formula-driven supervised learning (FDSL) has been shown to be an effective method for pre-training vision transformers, where ExFractalDB-21k was shown to exceed the pre-training effect of ImageNet-21k. These studies also indicate that contours mattered more than textures when pre-training vision transformers. However, the lack of a systematic investigation as to why these contour-oriented synthetic datasets can achieve the same accuracy as real datasets leaves much room for skepticism. In the present work, we develop a novel methodology based on circular harmonics for systematically investigating the design space of contour-oriented synthetic datasets. This allows us to efficiently search the optimal range of FDSL parameters and maximize the variety of synthetic images in the dataset, which we found to be a critical factor. When the resulting new dataset VisualAtom-21k is used for pre-training ViTBase, the top-1 accuracy reached 83.7% when fine-tuning on ImageNet-1k. This is only 0.5% difference from the top-1 accuracy (84.2%) achieved by the JFT-300M pre-training, even though the scale of images is 1/14. Unlike JFT-300M which is a static dataset, the quality of synthetic datasets will continue to improve, and the current work is a testament to this possibility. FDSL is also free of the common issues associated with real images, e.g. privacy/copyright issues, labeling costs/errors, and ethical biases.

关键词： Datasets and evaluation

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Scale-Equivalent Distillation for Semi-Supervised Object Detection

Scale-Equivalent Distillation for Semi-Supervised Object Det...

引用

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

作者： Guo, Qiushan Mu, Yao Chen, Jianyu Wang, Tianqi Yu, Yizhou Luo, Ping Univ Hong Kong Hong Kong Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent Semi-Supervised Object Detection (SS-OD) methods are mainly based on self-training, i.e., generating hard pseudo-labels by a teacher model on unlabeled data as supervisory signals. Although they achieved certain success, the limited labeled data in semi-supervised learning scales up the challenges of object detection. We analyze the challenges these methods meet with the empirical experiment results. We find that the massive False Negative samples and inferior localization precision lack consideration. Besides, the large variance of object sizes and class imbalance (i.e., the extreme ratio between background and object) hinder the performance of prior arts. Further, we overcome these challenges by introducing a novel approach, Scale-Equivalent Distillation (SED), which is a simple yet effective end-to-end knowledge distillation framework robust to large object size variance and class imbalance. SED has several appealing benefits compared to the previous works. (1) SED imposes a consistency regularization to handle the large scale variance problem. (2) SED alleviates the noise problem from the False Negative samples and inferior localization precision. (3) A re-weighting strategy can implicitly screen the potential foreground regions of the unlabeled data to reduce the effect of class imbalance. Extensive experiments show that SED consistently outperforms the recent state-of-the-art methods on different datasets with significant margins. For example, it surpasses the supervised counterpart by more than 10 mAP when using 5% and 10% labeled data on MS-COCO.

关键词： Location awareness Estimation Object detection Detectors Semisupervised learning Data models pattern recognition

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Context-aware Pretraining for Efficient Blind Image Decomposition

Context-aware Pretraining for Efficient Blind Image Decompos...

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

作者： Wang, Chao Zheng, Zhedong Quan, Ruijie Sun, Yifan Yang, Yi Zhejiang Univ ReLER CCAI Hangzhou Peoples R China Baidu Inc Beijing Peoples R China Natl Univ Singapore Sch Comp Sea NExT Joint Lab Singapore Singapore

ISBN: (纸本)9798350301298

In this paper, we study Blind Image Decomposition (BID), which is to uniformly remove multiple types of degradation at once without foreknowing the noise type. There remain two practical challenges: (1) Existing methods typically require massive data supervision, making them infeasible to real-world scenarios. (2) The conventional paradigm usually focuses on mining the abnormal pattern of a superimposed image to separate the noise, which de facto conflicts with the primary image restoration task. Therefore, such a pipeline compromises repairing efficiency and authenticity. In an attempt to solve the two challenges in one go, we propose an efficient and simplified paradigm, called Context-aware Pretraining (CP), with two pretext tasks: mixed image separation and masked image reconstruction. Such a paradigm reduces the annotation demands and explicitly facilitates context-aware feature learning. Assuming the restoration process follows a structure-to-texture manner, we also introduce a Context-aware Pretrained network (CPNet). In particular, CPNet contains two transformer-based parallel encoders, one information fusion module, and one multi-head prediction module. The information fusion module explicitly utilizes the mutual correlation in the spatial-channel dimension, while the multi-head prediction module facilitates texture-guided appearance flow. Moreover, a new sampling loss along with an attribute label constraint is also deployed to make use of the spatial context, leading to high-fidelity image restoration. Extensive experiments on both real and synthetic benchmarks show that our method achieves competitive performance for various BID tasks.

关键词： Low-level vision

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