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PTTR: Relational 3D Point Cloud Object Tracking with Transformer

PTTR: Relational 3D Point Cloud Object Tracking with Transfo...

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

作者： Zhou, Changqing Luo, Zhipeng Luo, Yueru Liu, Tianrui Pan, Liang Cai, Zhongang Zhao, Haiyu Lu, Shijian Nanyang Technol Univ Singapore Singapore Nanyang Technol Univ S Lab Singapore Singapore Sensetime Res Hong Kong Peoples R China Sensetime Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in the current search point cloud given a template point cloud. Motivated by the success of transformers, we propose Point Tracking TRansformer (PTTR), which efficiently predicts high-quality 3D tracking results in a coarse-to-fine manner with the help of transformer operations. PTTR consists of three novel designs. 1) Instead of random sampling, we design Relation-Aware Sampling to preserve relevant points to given templates during subsampling. 2) Furthermore, we propose a Point Relation Transformer (PRT) consisting of a self-attention and a cross-attention module. The global self-attention operation captures long-range dependencies to enhance encoded point features for the search area and the template, respectively. Subsequently, we generate the coarse tracking results by matching the two sets of point features via cross-attention. 3) Based on the coarse tracking results, we employ a novel Prediction Refinement Module to obtain the final refined prediction. In addition, we create a large-scale point cloud single object tracking benchmark based on the Waymo Open Dataset. Extensive experiments show that PTTR achieves superior point cloud tracking in both accuracy and efficiency. Our code is available at https://***/Jasonkks/PTTR.

关键词： Point cloud compression computer vision Three-dimensional displays Codes Navigation Transformers Search problems

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AGA : Attribute-Guided Augmentation 30

AGA : Attribute-Guided Augmentation

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30th ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Dixit, Mandar Kwitt, Roland Niethammer, Marc Vasconcelos, Nuno Univ Calif San Diego La Jolla CA 92093 USA Univ Salzburg Salzburg Austria UNC Chapel Hill Chapel Hill NC USA

ISBN: (纸本)9781538604571

We consider the problem of data augmentation, i.e., generating artificial samples to extend a given corpus of training data. Specifically, we propose attributed-guided augmentation (AGA) which learns a mapping that allows synthesis of data such that an attribute of a synthesized sample is at a desired value or strength. This is particularly interesting in situations where little data with no attribute annotation is available for learning, but we have access to an external corpus of heavily annotated samples. While prior works primarily augment in the space of images, we propose to perform augmentation in feature space instead. We implement our approach as a deep encoder-decoder architecture that learns the synthesis function in an end-to-end manner. We demonstrate the utility of our approach on the problems of (1) one-shot object recognition in a transfer-learning setting where we have no prior knowledge of the new classes, as well as (2) object-based one-shot scene recognition. As external data, we leverage 3D depth and pose information from the SUN RGB-D dataset. Our experiments show that attribute-guided augmentation of high-level CNN features considerably improves one-shot recognition performance on both problems.

关键词： Training Three-dimensional displays Object recognition Neural networks Data models Training data

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Robust fine-tuning of zero-shot models

Robust fine-tuning of zero-shot models

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

作者： Wortsman, Mitchell Ilharco, Gabriel Kim, Jong Wook Li, Mike Kornblith, Simon Roelofs, Rebecca Lopes, Raphael Gontijo Hajishirzi, Hannaneh Farhadi, Ali Namkoong, Hongseok Schmidt, Ludwig Univ Washington Seattle WA 98195 USA OpenAI San Francisco CA USA Columbia Univ New York NY USA Google Res Brain Team Toronto ON Canada

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning methods substantially improve accuracy on a given target distribution, they often reduce robustness to distribution shifts. We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-shot and fine-tuned models (WiSE-FT). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution. On ImageNet and five derived distribution shifts, WiSE-FT improves accuracy under distribution shift by 4 to 6 percentage points (pp) over prior work while increasing ImageNet accuracy by 1.6 pp. WiSE-FT achieves similarly large robustness gains (2 to 23 pp) on a diverse set of six further distribution shifts, and accuracy gains of 0.8 to 3.3 pp compared to standard fine-tuning on commonly used transfer learning datasets. These improvements come at no additional computational cost during fine-tuning or inference.

关键词： computer vision Computational modeling computer network reliability Transfer learning Neural networks Robustness Data models

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Embedded Discriminative Attention Mechanism for Weakly Supervised Semantic Segmentation

Embedded Discriminative Attention Mechanism for Weakly Super...

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

作者： Wu, Tong Huang, Junshi Gao, Guangyu Wei, Xiaoming Wei, Xiaolin Luo, Xuan Liu, Chi Harold Beijing Inst Technol Beijing Peoples R China Meituan Beijing Peoples R China

ISBN: (纸本)9781665445092

Weakly Supervised Semantic Segmentation (WSSS) with image-level annotation uses class activation maps from the classifier as pseudo-labels for semantic segmentation. However, such activation maps usually highlight the local discriminative regions rather than the whole object, which deviates from the requirement of semantic segmentation. To explore more comprehensive class-specific activation maps, we propose an Embedded Discriminative Attention Mechanism (EDAM) by integrating the activation map generation into the classification network directly for WSSS. Specifically, a Discriminative Activation (DA) layer is designed to explicitly produce a series of normalized class-specific masks, which are then used to generate class-specific pixellevel pseudo-labels demanded in segmentation. For learning the pseudo-labels, the masks are multiplied with the feature maps after the backbone to generate the discriminative activation maps, each of which encodes the specific information of the corresponding category in the input images. Given such class-specific activation maps, a Collaborative Multi-Attention (CMA) module is proposed to extract the collaborative information of each given category from images in a batch. In inference, we directly use the activation masks from the DA layer as pseudo-labels for segmentation. Based on the generated pseudo-labels, we achieve the mIoU of 70.60% on PASCAL VOC 2012 segmentation test-set, which is the new state-of-the-art, to our best knowledge. Code and pre-trained models are available online soon.

关键词： Image segmentation computer vision Codes Annotations Semantics Collaboration Feature extraction

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DSC-PoseNet: Learning 6DoF Object Pose Estimation via Dual-scale Consistency

DSC-PoseNet: Learning 6DoF Object Pose Estimation via Dual-s...

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

作者： Yang, Zongxin Yu, Xin Yang, Yi Baidu Res Beijing Peoples R China Univ Technol Sydney ReLER Sydney NSW Australia

ISBN: (纸本)9781665445092

Compared to 2D object bounding-box labeling, it is very difficult for humans to annotate 3D object poses, especially when depth images of scenes are unavailable. This paper investigates whether we can estimate the object poses effectively when only RGB images and 2D object annotations are given. To this end, we present a two-step pose estimation framework to attain 6DoF object poses from 2D object bounding-boxes. In the first step, the framework learns to segment objects from real and synthetic data in a weakly-supervised fashion, and the segmentation masks will act as a prior for pose estimation. In the second step, we design a dual-scale pose estimation network, namely DSC-PoseNet, to predict object poses by employing a differential renderer. To be specific, our DSC-PoseNet firstly predicts object poses in the original image scale by comparing the segmentation masks and the rendered visible object masks. Then, we resize object regions to a fixed scale to estimate poses once again. In this fashion, we eliminate large scale variations and focus on rotation estimation, thus facilitating pose estimation. Moreover, we exploit the initial pose estimation to generate pseudo ground-truth to train our DSC-PoseNet in a self-supervised manner. The estimation results in these two scales are ensembled as our final pose estimation. Extensive experiments on widely-used benchmarks demonstrate that our method outperforms state-of-the-art models trained on synthetic data by a large margin and even is on par with several fully-supervised methods.

关键词： Training Image segmentation computer vision Three-dimensional displays Annotations Pose estimation Benchmark testing

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Energy-based Latent Aligner for Incremental Learning

Energy-based Latent Aligner for Incremental Learning

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

作者： Joseph, K. J. Khan, Salman Khan, Fahad Shahbaz Anwer, Rao Muhammad Balasubramanian, Vineeth N. Indian Inst Technol Hyderabad Hyderabad India Mohamed bin Zayed Univ AI Abu Dhabi U Arab Emirates Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden Aalto Univ Espoo Finland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Deep learning models tend to forget their earlier knowledge while incrementally learning new tasks. This behavior emerges because the parameter updates optimized for the new tasks may not align well with the updates suitable for older tasks. The resulting latent representation mismatch causes forgetting. In this work, we propose ELI: Energy-based Latent Aligner for Incremental Learning, which first learns an energy manifold for the latent representations such that previous task latents will have low energy and the current task latents have high energy values. This learned manifold is used to counter the representational shift that happens during incremental learning. The implicit regularization that is offered by our proposed methodology can be used as a plug-and-play module in existing incremental learning methodologies. We validate this through extensive evaluation on CIFAR-100, ImageNet subset, ImageNet 1k and Pascal VOC datasets. We observe consistent improvement when ELI is added to three prominent methodologies in class-incremental learning, across multiple incremental settings. Further, when added to the state-of-the-art incremental object detector, ELI provides over 5% improvement in detection accuracy, corroborating its effectiveness and complementary advantage to the existing art. Code is available at: https://***/JosephKJ/ELI.

关键词： Manifolds Deep learning computer vision Codes Pipelines Object detection Detectors

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Probabilistic Selective Encryption of Convolutional Neural Networks for Hierarchical Services

Probabilistic Selective Encryption of Convolutional Neural N...

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

作者： Tian, Jinyu Zhou, Jiantao Duan, Jia Univ Macau State Key Lab Internet Things Smart City Dept Comp & Informat Sci Taipa Macao Peoples R China JD Explore JD Gauteng South Africa

ISBN: (纸本)9781665445092

Model protection is vital when deploying Convolutional Neural Networks (CNNs) for commercial services, due to the massive costs of training them. In this work, we propose a selective encryption (SE) algorithm to protect CNN models from unauthorized access, with a unique feature of providing hierarchical services to users. Our algorithm firstly selects important model parameters via the proposed Probabilistic Selection Strategy (PSS). It then encrypts the most important parameters with the designed encryption method called Distribution Preserving Random Mask (DPRM), so as to maximize the performance degradation by encrypting only a very small portion of model parameters. We also design a set of access permissions, using which different amount of most important model parameters can be decrypted. Hence, different levels of model performance can be naturally provided for users. Experimental results demonstrate that the proposed scheme could effectively protect the classification model VGG19 by merely encrypting 8% parameters of convolutional layers. We also implement the proposed model protection scheme in the denoising model DnCNN, showcasing the hierarchical denoising services.

关键词： Training Degradation computer vision Costs Computational modeling Noise reduction Probabilistic logic

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Stable Long-Term Recurrent Video Super-Resolution

Stable Long-Term Recurrent Video Super-Resolution

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

作者： Chiche, Benjamin Naoto Woiselle, Arnaud Frontera-Pons, Joana Starck, Jean-Luc Safran Elect & Def F-91344 Massy France Univ Paris Saclay Univ Paris Cite CNRS CEAAIM F-91191 Gif Sur Yvette France Inst Polytech Sci Avancees DR2I F-94200 Ivry France

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recurrent models have gained popularity in deep learning (DL) based video super-resolution (VSR), due to their increased computational efficiency, temporal receptive field and temporal consistency compared to sliding-window based models. However, when inferring on long video sequences presenting low motion (i.e. in which some parts of the scene barely move), recurrent models diverge through recurrent processing, generating high frequency artifacts. To the best of our knowledge, no study about VSR pointed out this instability problem, which can be critical for some real-world applications. Video surveillance is a typical example where such artifacts would occur, as both the camera and the scene stay static for a long time. In this work, we expose instabilities of existing recurrent VSR networks on long sequences with low motion. We demonstrate it on a new long sequence dataset Quasi-Static Video Set, that we have created. Finally, we introduce a new framework of recurrent VSR networks that is both stable and competitive, based on Lipschitz stability theory. We propose a new recurrent VSR network, coined Middle Recurrent Video Super-Resolution (MRVSR), based on this framework. We empirically show its competitive performance on long sequences with low motion.

关键词： Deep learning computer vision Computational modeling Superresolution Video sequences Video surveillance Stability analysis

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Continual Predictive Learning from Videos

Continual Predictive Learning from Videos

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

作者： Chen, Geng Zhang, Wendong Lu, Han Gao, Siyu Wang, Yunbo Long, Mingsheng Yang, Xiaokang Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Tsinghua Univ Sch Software BNRist Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Predictive learning ideally builds the world model of physical processes in one or more given environments. Typical setups assume that we can collect data from all environments at all times. In practice, however, different prediction tasks may arrive sequentially so that the environments may change persistently throughout the training procedure. Can we develop predictive learning algorithms that can deal with more realistic, non-stationary physical environments? In this paper, we study a new continual learning problem in the context of video prediction, and observe that most existing methods suffer from severe catastrophic forgetting in this setup. To tackle this problem, we propose the continual predictive learning (CPI.) approach, which learns a mixture world model via predictive experience replay and performs test-time adaptation with non parametric task inference. We construct two new benchmarks based on RoboNet and KTH, in which different tasks correspond to different physical robotic environments or human actions. Our approach is shown to effectively mitigate forgetting and remarkably outperform the naive combinations of previous art in video prediction and continual learning.

关键词： Training computer vision Adaptation models Art Predictive models Benchmark testing Prediction algorithms

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Stacked U-Nets for Ground Material Segmentation in Remote Sensing Imagery 31

Stacked U-Nets for Ground Material Segmentation in Remote Se...

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

作者： Ghosh, Arthita Ehrlich, Max Shah, Sohil Davis, Larry Chellappa, Rama Univ Maryland College Pk MD 20742 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We present a semantic segmentation algorithm for RGB remote sensing images. Our method is based on the Dilated Stacked U-Nets architecture. This state-of-the-art method has been shown to have good performance in other applications. We perform additional post-processing by blending image tiles and degridding the result. Our method gives competitive results on the DeepGlobe dataset.

关键词： Semantics Image segmentation Remote sensing Convolution Training Satellites computer architecture

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