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3D Object Detection with Pointformer

3D Object Detection with Pointformer

引用

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

作者： Pan, Xuran Xia, Zhuofan Song, Shiji Li, Li Erran Huang, Gao Tsinghua Univ Dept Automat Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China Columbia Univ Amazon Alexa AI New York NY 10027 USA

ISBN: (纸本)9781665445092

Feature learning for 3D object detection from point clouds is very challenging due to the irregularity of 3D point cloud data. In this paper, we propose Pointformer, a Transformer backbone designed for 3D point clouds to learn features effectively. Specifically, a Local Transformer module is employed to model interactions among points in a local region, which learns context-dependent region features at an object level. A Global Transformer is designed to learn context-aware representations at the scene level. To further capture the dependencies among multi-scale representations, we propose Local-Global Transformer to integrate local features with global features from higher resolution. In addition, we introduce an efficient coordinate refinement module to shift down-sampled points closer to object centroids, which improves object proposal generation. We use Pointformer as the backbone for state-of-the-art object detection models and demonstrate significant improvements over original models on both indoor and outdoor datasets.

关键词： Solid modeling Three-dimensional displays Shape Semantics Object detection Transformers pattern recognition

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M²SGD: Learning to Learn ImportantWeights

M<SUP>2</SUP>SGD: Learning to Learn ImportantWeights

引用

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

作者： Kuo, Nicholas I-Hsien Harandi, Mehrtash Fourrier, Nicolas Walder, Christian Ferraro, Gabriela Suominen, Hanna Australian Natl Univ RSCS Canberra ACT Australia Monash Univ ECSE Clayton Vic Australia CSIRO Data61 Canberra ACT Australia Vole Univ Leonard de Vinci Paris France Univ Turku Dept Future Technol Turku Finland

ISBN: (纸本)9781728193601

Meta-learning concerns rapid knowledge acquisition. One popular approach cast optimisation as a learning problem and it has been shown that learnt neural optimisers updated base learners more quickly than their hand-crafted counterparts. In this paper, we learn an optimisation rule that sparsely updates the learner parameters and removes redundant weights. We present Masked Meta-SGD (M2SGD), a neural optimiser which is not only capable of updating learners quickly, but also capable of removing 83.71% weights for ResNet20s. We release our codes at https://***/Nic5472K/CLvision2020_cvpr_M2SGD.

关键词： computer vision

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PCL: Proxy-based Contrastive Learning for Domain Generalization

PCL: Proxy-based Contrastive Learning for Domain Generalizat...

引用

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

作者： Yao, Xufeng Bai, Yang Zhang, Xinyun Zhang, Yuechen Sun, Qi Chen, Ran Li, Ruiyu Yu, Bei Chinese Univ Hong Kong Hong Kong Peoples R China SmartMore Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Domain generalization refers to the problem of training a model from a collection of different source domains that can directly generalize to the unseen target domains. A promising solution is contrastive learning, which attempts to learn domain-invariant representations by exploiting rich semantic relations among sample-to-sample pairs from different domains. A simple approach is to pull positive sample pairs from different domains closer while pushing other negative pairs further apart. In this paper, we find that directly applying contrastive-based methods (e.g., supervised contrastive learning) are not effective in domain generalization. We argue that aligning positive sample-tosample pairs tends to hinder the model generalization due to the significant distribution gaps between different domains. To address this issue, we propose a novel proxy-based contrastive learning method, which replaces the original sample-to-sample relations with proxy-to-sample relations, significantly alleviating the positive alignment issue. Experiments on the four standard benchmarks demonstrate the effectiveness of the proposed method. Furthermore, we also consider a more complex scenario where no ImageNet pre-trained models are provided Our method consistently shows better performance.

关键词： Training Representation learning Learning systems computer vision Computational modeling Semantics Benchmark testing

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Deep Two-View Structure-from-Motion Revisited

Deep Two-View Structure-from-Motion Revisited

引用

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

作者： Wang, Jianyuan Zhong, Yiran Dai, Yuchao Birchfield, Stan Zhang, Kaihao Smolyanskiy, Nikolai Li, Hongdong Australian Natl Univ Canberra ACT Australia Northwestern Polytech Univ Xian Peoples R China NVIDIA Redmond WA USA

ISBN: (纸本)9781665445092

Two-view structure-from-motion (SfM) is the cornerstone of 3D reconstruction and visual SLAM. Existing deep learning-based approaches formulate the problem by either recovering absolute pose scales from two consecutive frames or predicting a depth map from a single image, both of which are ill-posed problems. In contrast, we propose to revisit the problem of deep two-view SfM by leveraging the well-posedness of the classic pipeline. Our method consists of 1) an optical flow estimation network that predicts dense correspondences between two frames;2) a normalized pose estimation module that computes relative camera poses from the 2D optical flow correspondences, and 3) a scale-invariant depth estimation network that leverages epipolar geometry to reduce the search space, refine the dense correspondences, and estimate relative depth maps. Extensive experiments show that our method outperforms all state-of-the-art two-view SfM methods by a clear margin on KITTI depth, KITTI VO, MVS, Scenes11, and SUN3D datasets in both relative pose and depth estimation.

关键词： Geometry Deep learning Visualization computer vision Three-dimensional displays Simultaneous localization and mapping Pose estimation

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Rotation Averaging and Strong Duality 31

Rotation Averaging and Strong Duality

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31st ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Eriksson, Anders Olsson, Carl Kahl, Fredrik Chin, Tat-Jun Queensland Univ Technol Sch Elect Engn & Comp Sci Brisbane Qld Australia Chalmers Univ Technol Dept Elect Engn Gothenburg Sweden Lund Univ Ctr Math Sci Lund Sweden Univ Adelaide Sch Comp Sci Adelaide SA Australia

ISBN: (纸本)9781538664209

In this paper we explore the role of duality principles within the problem of rotation averaging, a fundamental task in a wide range of computer vision applications. In its conventional form, rotation averaging is stated as a minimization over multiple rotation constraints. As these constraints are non-convex, this problem is generally considered challenging to solve globally. We show how to circumvent this difficulty through the use of Lagrangian duality. While such an approach is well-known it is normally not guaranteed to provide a tight relaxation. Based on spectral graph theory, we analytically prove that in many cases there is no duality gap unless the noise levels are severe. This allows us to obtain certifiably global solutions to a class of important non-convex problems in polynomial time. We also propose an efficient, scalable algorithm that outperforms general purpose numerical solvers and is able to handle the large problem instances commonly occurring in structure from motion settings. The potential of this proposed method is demonstrated on a number of different problems, consisting of both synthetic and real-world data.

关键词： Cameras Symmetric matrices computer vision Noise level Structure from motion Eigenvalues and eigenfunctions Standards

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Learning Temporal Consistency for Low Light Video Enhancement from Single Images

Learning Temporal Consistency for Low Light Video Enhancemen...

引用

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

作者： Zhang, Fan Li, Yu You, Shaodi Fu, Ying Beijing Inst Technol Beijing Peoples R China Tencent PCG Appl Res Ctr ARC Mountain View CA USA Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9781665445092

Single image low light enhancement is an important task and it has many practical applications. Most existing methods adopt a single image approach. Although their performance is satisfying on a static single image, we found, however, they suffer serious temporal instability when handling low light videos. We notice the problem is because existing data-driven methods are trained from single image pairs where no temporal information is available. Unfortunately, training from real temporally consistent data is also problematic because it is impossible to collect pixel-wisely paired low and normal light videos under controlled environments in large scale and diversities with noise of identical statistics. In this paper, we propose a novel method to enforce the temporal stability in low light video enhancement with only static images. The key idea is to learn and infer motion field (optical flow) from a single image and synthesize short range video sequences. Our strategy is general and can extend to large scale datasets directly. Based on this idea, we propose our method which can infer motion prior for single image low light video enhancement and enforce temporal consistency. Rigorous experiments and user study demonstrate the state-of-the-art performance of our proposed method. Our code and model will be publicly available at https://***/zkawfanx/StableLLVE.

关键词： Training computer vision Computational modeling Video sequences Stability analysis Planning pattern recognition

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Which images to label for few-shot medical landmark detection?

Which images to label for few-shot medical landmark detectio...

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

作者： Quan, Quan Yao, Qingsong Li, Jun Zhou, S. Kevin Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing Peoples R China Univ Sci & Technol China Hefei Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The success of deep learning methods relies on the availability of well-labeled large-scale datasets. However, for medical images, annotating such abundant training data often requires experienced radiologists and consumes their limited time. Few-shot learning is developed to alleviate this burden, which achieves competitive performances with only several labeled data. However, a crucial yet previously overlooked problem in few-shot learning is about the selection of template images for annotation before learning, which affects the final performance. We herein propose a novel Sample Choosing Policy (SCP) to select "the most worthy" images for annotation, in the context of few-shot medical landmark detection. SCP consists of three parts: 1) Self-supervised training for building a pre-trained deep model to extract features from radiological images, 2) Key Point Proposal for localizing informative patches, and 3) Representative Score Estimation for searching the most representative samples or templates. The advantage of SCP is demonstrated by various experiments on three widely-used public datasets. For one-shot medical landmark detection, its use reduces the mean radial errors on Cephalometric and HandXray datasets by 14.2% (from 3.595mm to 3.083mm) and 35.5% (4.114mm to 2.653mm), respectively.

关键词： Training Deep learning computer vision Annotations Training data Estimation Feature extraction

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Learning the viewpoint manifold for action recognition

Learning the viewpoint manifold for action recognition

引用

ieee conference on computer vision and pattern recognition

作者： Souvenir, Richard Babbs, Justin Univ N Carolina Dept Comp Sci Charlotte NC 28223 USA

ISBN: (纸本)9781424422425

Researchers are increasingly interested in providing video-based, view-invariant action recognition for human motion. Addressing this problem will lead to more accurate modeling and analysis of the type of unconstrained video commonly collected in the areas of athletics and medicine. Previous viewpoint-invariant methods use multiple cameras in both the training and testing phases of action recognition or require storing many examples of a single action from multiple viewpoints. In this paper, we present a framework for learning a compact representation of primitive actions (e.g., walk, punch, kick, sit) that can be used for video obtained from a single camera for simultaneous action recognition and viewpoint estimation. Using our method, which models the low-dimensional structure of these actions relative to viewpoint, we show recognition rates on a publicly available data set previously only acheieved using multiple simultaneous views.

关键词： Cameras

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Align Representations with Base: A New Approach to Self-Supervised Learning

Align Representations with Base: A New Approach to Self-Supe...

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

作者： Zhang, Shaofeng Qiu, Lyn Zhu, Feng Yan, Junchi Zhang, Hengrui Zhao, Rui Li, Hongyang Yang, Xiaokang Shanghai Jiao Tong Univ Artificial Intelligence Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China SenseTime Res Hong Kong Peoples R China Shanghai Jiao Tong Univ Qing Yuan Res Inst Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Existing symmetric contrastive learning methods suffer from collapses (complete and dimensional) or quadratic complexity of objectives. Departure from these methods which maximize mutual information of two generated views, along either instance or feature dimension, the proposed paradigm introduces intermediate variables at the feature level, and maximizes the consistency between variables and representations of each view. Spec(fically, the proposed intermediate variables are the nearest group of base vectors to representations. Hence, we call the proposed method ARB (Align Representations with Base). Compared with other symmetric approaches, ARB 1) does not require negative pairs, which leads the complexity of the overall objective function is in linear order, 2) reduces feature redundancy, increasing the information density of training samples, 3) is more robust to output dimension size, which outperforms previous feature-wise arts over 28% Top-1 accuracy on ImageNet-100 under low-dimension settings.

关键词： Training Representation learning Redundancy Self-supervised learning Linear programming Complexity theory pattern recognition

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Learning with Dataset Bias in Latent Subcategory Models

Learning with Dataset Bias in Latent Subcategory Models

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ieee conference on computer vision and pattern recognition (cvpr)

作者： Stamos, Dimitris Martelli, Samuele Nabi, Moin McDonald, Andrew Murino, Vittorio Pontil, Massimiliano UCL Dept Comp Sci London WC1E 6BT England Ist Italiano Tecnol Pattern Anal & Comp Vis Genoa Italy Univ Verona Dipartimento Informat I-37100 Verona Italy

ISBN: (纸本)9781467369640

Latent subcategory models (LSMs) offer significant improvements over training linear support vector machines (SVMs). Training LSMs is a challenging task due to the potentially large number of local optima in the objective function and the increased model complexity which requires large training set sizes. Often, larger datasets are available as a collection of heterogeneous datasets. However, previous work has highlighted the possible danger of simply training a model from the combined datasets, due to the presence of bias. In this paper, we present a model which jointly learns an LSM for each dataset as well as a compound LSM. The method provides a means to borrow statistical strength from the datasets while reducing their inherent bias. In experiments we demonstrate that the compound LSM, when tested on PASCAL, LabelMe, Caltech101 and SUNO9 in a leave-one-dataset-out fashion, achieves an average improvement of over 6.5% over a previous SVM-based undoing bias approach and an average improvement of over 8.5% over a standard LSM trained on the concatenation of the datasets.

关键词： Dataset Linux Security Module Bias Bias Bias Statistical Biases bias(electronics) Functional training Support Vector Network Objective function Learning train stock

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