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DeepI2P: Image-to-Point Cloud Registration via Deep Classification

DeepI2P: Image-to-Point Cloud Registration via Deep Classifi...

引用

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

作者： Li, Jiaxin Lee, Gim Hee Bytedance Beijing Peoples R China Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781665445092

This paper presents DeepI2P: a novel approach for cross-modality registration between an image and a point cloud. Given an image (e.g. from a rgb-camera) and a general point cloud (e.g. from a 3D Lidar scanner) captured at different locations in the same scene, our method estimates the relative rigid transformation between the coordinate frames of the camera and Lidar. Learning common feature descriptors to establish correspondences for the registration is inherently challenging due to the lack of appearance and geometric correlations across the two modalities. We circumvent the difficulty by converting the registration problem into a classification and inverse camera projection optimization problem. A classification neural network is designed to label whether the projection of each point in the point cloud is within or beyond the camera frustum. These labeled points are subsequently passed into a novel inverse camera projection solver to estimate the relative pose. Extensive experimental results on Oxford Robotcar and KITTI datasets demonstrate the feasibility of our approach. Our source code is available at https://***/lijx10/DeepI2P.

关键词： computer vision Laser radar Three-dimensional displays Correlation Codes Robot vision systems Neural networks

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Graph-based High-Order Relation Discovery for Fine-grained recognition

Graph-based High-Order Relation Discovery for Fine-grained R...

引用

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

作者： Zhao, Yifan Yan, Ke Huang, Feiyue Li, Jia Beihang Univ State Key Lab Virtual Real Technol & Syst SCSE Beijing Peoples R China Tencent Youtu Lab Shanghai Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Fine-grained object recognition aims to learn effective features that can identify the subtle differences between visually similar objects. Most of the existing works tend to amplify discriminative part regions with attention mechanisms. Besides its unstable performance under complex backgrounds, the intrinsic interrelationship between different semantic features is less explored. Toward this end, we propose an effective graph-based relation discovery approach to build a contextual understanding of high-order relationships. In our approach, a high-dimensional feature bank is first formed and jointly regularized with semantic- and positional-aware high-order constraints, endowing rich attributes to feature representations. Second, to overcome the high-dimension curse, we propose a graph-based semantic grouping strategy to embed this high-order tensor bank into a low-dimensional space. Meanwhile, a group-wise learning strategy is proposed to regularize the features focusing on the cluster embedding center. With the collaborative learning of three modules, our module is able to grasp the stronger contextual details of fine-grained objects. Experimental evidence demonstrates our approach achieves new state-of-the-art on 4 widely-used fine-grained object recognition benchmarks.

关键词： Training Manifolds computer vision Tensors Semantics Focusing Collaborative work

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Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion

Deep RGB-D Saliency Detection with Depth-Sensitive Attention...

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

作者： Sun, Peng Zhang, Wenhu Wang, Huanyu Li, Songyuan Li, Xi Zhejiang Univ Coll Comp Sci & Technol Shanghai Inst Adv Study Hangzhou Peoples R China

ISBN: (纸本)9781665445092

RGB-D salient object detection (SOD) is usually formulated as a problem of classification or regression over two modalities, i.e., RGB and depth. Hence, effective RGB-D feature modeling and multi-modal feature fusion both play a vital role in RGB-D SOD. In this paper, we propose a depth-sensitive RGB feature modeling scheme using the depth-wise geometric prior of salient objects. In principle, the feature modeling scheme is carried out in a depth-sensitive attention module, which leads to the RGB feature enhancement as well as the background distraction reduction by capturing the depth geometry prior. Moreover, to perform effective multi-modal feature fusion, we further present an automatic architecture search approach for RGB-D SOD, which does well in finding out a feasible architecture from our specially designed multi-modal multi-scale search space. Extensive experiments on seven standard benchmarks demonstrate the effectiveness of the proposed approach against the state-of-the-art.

关键词： Geometry Visualization computer vision computer architecture Object detection Benchmark testing pattern recognition

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Learning by Aligning Videos in Time

Learning by Aligning Videos in Time

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

作者： Haresh, Sanjay Kumar, Sateesh Coskun, Huseyin Syed, Shahram N. Konin, Andrey Zia, M. Zeeshan Quoc-Huy Tran Retrocausal Inc Seattle WA 98052 USA

ISBN: (纸本)9781665445092

We present a self-supervised approach for learning video representations using temporal video alignment as a pretext task, while exploiting both frame-level and video-level information. We leverage a novel combination of temporal alignment loss and temporal regularization terms, which can be used as supervision signals for training an encoder network. Specifically, the temporal alignment loss (i.e., Soft-DTW) aims for the minimum cost for temporally aligning videos in the embedding space. However, optimizing solely for this term leads to trivial solutions, particularly, one where all frames get mapped to a small cluster in the embedding space. To overcome this problem, we propose a temporal regularization term (i.e., Contrastive-IDM) which encourages different frames to be mapped to different points in the embedding space. Extensive evaluations on various tasks, including action phase classification, action phase progression, and fine-grained frame retrieval, on three datasets, namely Pouring, Penn Action, and IKEA ASM, show superior performance of our approach over state-of-the-art methods for self-supervised representation learning from videos. In addition, our method provides significant performance gain where labeled data is lacking.

关键词： Training computer vision Costs Performance gain pattern recognition Task analysis

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Densely connected multidilated convolutional networks for dense prediction tasks

Densely connected multidilated convolutional networks for de...

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

作者： Takahashi, Naoya Mitsufuji, Yuki Sony Corp Tokyo Japan

ISBN: (纸本)9781665445092

Tasks that involve high-resolution dense prediction require a modeling of both local and global patterns in a large input field. Although the local and global structures often depend on each other and their simultaneous modeling is important, many convolutional neural network (CNN)based approaches interchange representations in different resolutions only a few times. In this paper, we claim the importance of a dense simultaneous modeling of multiresolution representation and propose a novel CNN architecture called densely connected multidilated DenseNet (D3Net). D3Net involves a novel multidilated convolution that has different dilation factors in a single layer to model different resolutions simultaneously. By combining the multidilated convolution with the DenseNet architecture, D3Net incorporates multiresolution learning with an exponentially growing receptive field in almost all layers, while avoiding the aliasing problem that occurs when we naively incorporate the dilated convolution in DenseNet. Experiments on the image semantic segmentation task using Cityscapes and the audio source separation task using MUSDB18 show that the proposed method has superior performance over state-of-the-art methods.

关键词： Image segmentation Image resolution Source separation Convolution Semantics computer architecture Topology

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A Method of Moments Embedding Constraint and its Application to Semi-Supervised Learning

A Method of Moments Embedding Constraint and its Application...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Michael Majurski Sumeet Menon Parniyan Farvardin David Chapman Information Technology Lab NIST University of Maryland University of Maryland University of Miami

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Discriminative deep learning models with a linear+softmax final layer have a problem: the latent space only predicts the conditional probabilities p(Y |X) but not the full joint distribution p(Y,X), which necessitates a generative approach. The conditional probability cannot detect outliers, causing outlier sensitivity in softmax networks. This exacerbates model over-confidence impacting many problems, such as hallucinations, confounding biases, and dependence on large datasets. To address this we introduce a novel embedding constraint based on the Method of Moments (MoM). We investigate the use of polynomial moments ranging from 1st through 4th order hyper-covariance matrices. Furthermore, we use this embedding constraint to train an Axis-Aligned Gaussian Mixture Model (AAGMM) final layer, which learns not only the conditional, but also the joint distribution of the latent space. We apply this method to the domain of semi-supervised image classification by extending FlexMatch with our technique. We find our MoM constraint with the AAGMM layer is able to match the reported FlexMatch accuracy, while also modeling the joint distribution, thereby reducing outlier sensitivity. We also present a preliminary outlier detection strategy based on Mahalanobis distance and discuss future improvements to this strategy. Code is available at: https://***/mmajurski/ssl-gmm

关键词： Deep learning Sensitivity Semisupervised learning Predictive models Polynomials Distance measurement pattern recognition

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Real-Time Selfie Video Stabilization

Real-Time Selfie Video Stabilization

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

作者： Yu, Jiyang Ramamoorthi, Ravi Cheng, Keli Sarkis, Michel Bi, Ning Univ Calif San Diego La Jolla CA 92093 USA Qualcomm Technol Inc San Diego CA USA JD AI Res Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

We propose a novel real-time selfie video stabilization method. Our method is completely automatic and runs at 26 fps. We use a 1D linear convolutional network to directly infer the rigid moving least squares warping which implicitly balances between the global rigidity and local flexibility. Our network structure is specifically designed to stabilize the background and foreground at the same time, while providing optional control of stabilization focus (relative importance of foreground vs. background) to the users. To train our network, we collect a selfie video dataset with 1005 videos, which is significantly larger than previous selfie video datasets. We also propose a grid approximation to the rigid moving least squares that enables the real-time frame warping. Our method is fully automatic and produces visually and quantitatively better results than previous real-time general video stabilization methods. Compared to previous offline selfie video methods, our approach produces comparable quality with a speed improvement of orders of magnitude. Our code and selfie video dataset is available at https://***/jiy173/selfievideostabilization.

关键词： Convolutional codes computer vision Streaming media Real-time systems Rigidity pattern recognition

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Leveraging Line-point Consistence to Preserve Structures forWide Parallax Image Stitching

Leveraging Line-point Consistence to Preserve Structures for...

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

作者： Jia, Qi Li, ZhengJun Fan, Xin Zhao, Haotian Teng, Shiyu Ye, Xinchen Latecki, Longin Jan Dalian Univ Technol Int Sch Informat Sci & Engn Dalian Peoples R China Temple Univ Comp & Informat Sci Philadelphia PA 19122 USA

ISBN: (纸本)9781665445092

Generating high-quality stitched images with natural structures is a challenging task in computer vision. In this paper, we succeed in preserving both local and global geometric structures for wide parallax images, while reducing artifacts and distortions. A projective invariant, Characteristic Number, is used to match co-planar local sub-regions for input images. The homography between these well-matched sub-regions produces consistent line and point pairs, suppressing artifacts in overlapping areas. We explore and introduce global collinear structures into an objective function to specify and balance the desired characters for image warping, which can preserve both local and global structures while alleviating distortions. We also develop comprehensive measures for stitching quality to quantify the collinearity of points and the discrepancy of matched line pairs by considering the sensitivity to linear structures for human vision. Extensive experiments demonstrate the superior performance of the proposed method over the state-of-the-art by presenting sharp textures and preserving prominent natural structures in stitched images. Especially, our method not only exhibits lower errors but also the least divergence across all test images. Code is available at https://***/dut-media-lab/ImageStitching.

关键词： computer vision Sensitivity Codes Distortion Linear programming pattern recognition Distortion measurement

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Learning by Watching

Learning by Watching

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

作者： Zhang, Jimuyang Ohn-Bar, Eshed Boston Univ Boston MA 02215 USA

ISBN: (纸本)9781665445092

When in a new situation or geographical location, human drivers have an extraordinary ability to watch others and learn maneuvers that they themselves may have never performed. In contrast, existing techniques for learning to drive preclude such a possibility as they assume direct access to an instrumented ego-vehicle with fully known observations and expert driver actions. However, such measurements cannot be directly accessed for the non-ego vehicles when learning by watching others. Therefore, in an application where data is regarded as a highly valuable asset, current approaches completely discard the vast portion of the training data that can be potentially obtained through indirect observation of surrounding vehicles. Motivated by this key insight, we propose the Learning by Watching (LbW) framework which enables learning a driving policy without requiring full knowledge of neither the state nor expert actions. To increase its data, i.e., with new perspectives and maneuvers, LbW makes use of the demonstrations of other vehicles in a given scene by (1) transforming the ego-vehicle's observations to their points of view, and (2) inferring their expert actions. Our LbW agent learns more robust driving policies while enabling data-efficient learning, including quick adaptation of the policy to rare and novel scenarios. In particular, LbW drives robustly even with a fraction of available driving data required by existing methods, achieving an average success rate of 92% on the original CARLA benchmark with only 30 minutes of total driving data and 82% with only 10 minutes.

关键词： computer vision Instruments Current measurement Training data Benchmark testing pattern recognition Noise measurement

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SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation

SRWarp: Generalized Image Super-Resolution under Arbitrary T...

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

作者： Son, Sanghyun Lee, Kyoung Mu Seoul Natl Univ Dept ECE ASRI Seoul South Korea

ISBN: (纸本)9781665445092

Deep CNNs have achieved significant successes in image processing and its applications, including single image super-resolution (SR). However, conventional methods still resort to some predetermined integer scaling factors, e.g., x 2 or x 4. Thus, they are difficult to be applied when arbitrary target resolutions are required. Recent approaches extend the scope to real-valued upsampling factors, even with varying aspect ratios to handle the limitation. In this paper, we propose the SRWarp framework to further generalize the SR tasks toward an arbitrary image transformation. We interpret the traditional image warping task, specifically when the input is enlarged, as a spatially-varying SR problem. We also propose several novel formulations, including the adaptive warping layer and multiscale blending, to reconstruct visually favorable results in the transformation process. Compared with previous methods, we do not constrain the SR model on a regular grid but allow numerous possible deformations for flexible and diverse image editing. Extensive experiments and ablation studies justify the necessity and demonstrate the advantage of the proposed SRWarp method under various transformations.

关键词： Deformable models Adaptation models Visualization Shape Superresolution pattern recognition Task analysis

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