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HybridCR: Weakly-Supervised 3D Point Cloud Semantic Segmentation via Hybrid Contrastive Regularization

HybridCR: Weakly-Supervised 3D Point Cloud Semantic Segmenta...

引用

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

作者： Li, Mengtian Xie, Yuan Shen, Yunhang Ke, Bo Qiao, Ruizhi Ren, Bo Lin, Shaohui Ma, Lizhuang East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China Tencent Youtu Lab Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

To address the huge labeling cost in large-scale point cloud semantic segmentation, we propose a novel hybrid contrastive regularization (HybridCR) framework in weakly-supervised setting, which obtains competitive performance compared to its fully-supervised counterpart. Specifically, HybridCR is the first framework to leverage both point consistency and employ contrastive regularization with pseudo labeling in an end-to-end manner. Fundamentally, HybridCR explicitly and effectively considers the semantic similarity between local neighboring points and global characteristics of 3D classes. We further design a dynamic point cloud augmentor to generate diversity and robust sample views, whose transformation parameter is jointly optimized with model training. Through extensive experiments, HybridCR achieves significant performance improvement against the SOTA methods on both indoor and outdoor datasets, e.g., S3DIS, ScanNet-V2, Semantic3D, and SemanticKITTI.

关键词： Point cloud compression Training computer vision Three-dimensional displays Costs Shape Computational modeling

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Weakly Supervised Rotation-Invariant Aerial Object Detection Network

Weakly Supervised Rotation-Invariant Aerial Object Detection...

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

作者： Feng, Xiaoxu Yao, Xiwen Cheng, Gong Han, Junwei Northwestern Polytech Univ Sch Automat Xian Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Object rotation is among long-standing, yet still unexplored, hard issues encountered in the task of weakly supervised object detection (WSOD) from aerial images. Existing predominant WSOD approaches built on regular CNNs which are not inherently designed to tackle object rotations without corresponding constraints, thereby leading to rotation-sensitive object detector. Meanwhile, current solutions have been prone to fall into the issue with unstable detectors, as they ignore lower-scored instances and may regard them as backgrounds. To address these issues, in this paper, we construct a novel end-to-end weakly supervised Rotation-Invariant aerial object detection Network (RINet). It is implemented with a flexible multi-branch online detector refinement, to be naturally more rotation-perceptive against oriented objects. Specifically, RINet first performs label propagating from the predicted instances to their rotated ones in a progressive refinement manner. Meanwhile, we propose to couple the predicted instance labels among different rotation perceptive branches for generating rotation-consistent supervision and meanwhile pursuing all possible instances. With the rotation-consistent supervisions, RINet enforces and encourages consistent yet complementary feature learning for WSOD without additional annotations and hyper-parameters. On the challenging NWPU VHR-10.v2 and DIOR datasets, extensive experiments clearly demonstrate that we significantly boost existing WSOD methods to a new state-of-the-art performance.

关键词： Representation learning Training Couplings computer vision Codes Detectors Object detection

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ER3: A Unified Framework for Event Retrieval, recognition and Recounting 30

ER3: A Unified Framework for Event Retrieval, Recognition an...

引用

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

作者： Gao, Zhanning Hua, Gang Zhang, Dongqing Jojic, Nebojsa Wang, Le Xue, Jianru Zheng, Nanning Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Shaanxi Peoples R China Microsoft Res Redmond WA USA

ISBN: (纸本)9781538604571

We develop a unified framework for complex event retrieval, recognition and recounting. The framework is based on a compact video representation that exploits the temporal correlations in image features. Our feature alignment procedure identifies and removes the feature redundancies across frames and outputs an intermediate tensor representation we call video imprint. The video imprint is then fed into a reasoning network, whose attention mechanism parallels that of memory networks used in language modeling. The reasoning network simultaneously recognizes the event category and locates the key pieces of evidence for event recounting. In event retrieval tasks, we show that the compact video representation aggregated from the video imprint achieves significantly better retrieval accuracy compared with existing methods. We also set new state of the art results in event recognition tasks with an additional benefit: The latent structure in our reasoning network highlights the areas of the video imprint and can be directly used for event recounting. As video imprint maps back to locations in the video frames, the network allows not only the identification of key frames but also specific areas inside each frame which are most influential to the decision process.

关键词： Feature extraction Cognition pattern recognition Neural networks Layout computer vision

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Dynamic-OFA: Runtime DNN Architecture Switching for Performance Scaling on Heterogeneous Embedded Platforms

Dynamic-OFA: Runtime DNN Architecture Switching for Performa...

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

作者： Lou, Wei Xun, Lei Sabet, Amin Bi, Jia Hare, Jonathon Merrett, Geoff, V Univ Southampton Southampton Hants England

ISBN: (纸本)9781665448994

Mobile and embedded platforms are increasingly required to efficiently execute computationally demanding DNNs across heterogeneous processing elements. At runtime, the available hardware resources to DNNs can vary considerably due to other concurrently running applications. The performance requirements of the applications could also change under different scenarios. To achieve the desired performance, dynamic DNNs have been proposed in which the number of channels/layers can be scaled in real time to meet different requirements under varying resource constraints. However, the training process of such dynamic DNNs can be costly, since platform-aware models of different deployment scenarios must be retrained to become dynamic. This paper proposes Dynamic-OFA, a novel dynamic DNN approach for state-of-the-art platform-aware NAS models (i.e. Once-for-all network (OFA)). Dynamic-OFA pre-samples a family of sub-networks from a static OFA backbone model, and contains a runtime manager to choose different sub-networks under different runtime environments. As such, Dynamic-OFA does not need the traditional dynamic DNN training pipeline. Compared to the state-of-the-art, our experimental results using ImageNet on a Jetson Xavier NX show that the approach is up to 3.5x (CPU), 2.4x (GPU) faster for similar Top-1 accuracy, or 3.8% (CPU), 5.1% (GPU) higher accuracy at similar latency.

关键词： Training Three-dimensional displays Computational modeling Pipelines Graphics processing units computer architecture Switches

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RGBD Objects in the Wild: Scaling Real-World 3D Object Learning from RGB-D Videos

RGBD Objects in the Wild: Scaling Real-World 3D Object Learn...

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

作者： Xia, Hongchi Fu, Yang Liu, Sifei Wang, Xiaolong Shanghai Jiao Tong Univ Shanghai Peoples R China Univ Calif San Diego San Diego CA USA NVIDIA Santa Clara CA USA

ISBN: (纸本)9798350353006

We introduce a new RGB-D object dataset captured in the wild called WildRGB-D. Unlike most existing real-world object-centric datasets which only come with RGB capturing, the direct capture of the depth channel allows better 3D annotations and broader downstream applications. WildRGB-D comprises large-scale category-level RGB-D object videos, which are taken using an iPhone to go around the objects in 360 degrees. It contains around 8500 recorded objects and nearly 20000 RGB-D videos across 46 common object categories. These videos are taken with diverse cluttered backgrounds with three setups to cover as many real-world scenarios as possible: (i) a single object in one video;(ii) multiple objects in one video;and (iii) an object with a static hand in one video. The dataset is annotated with object masks, real-world scale camera poses, and reconstructed aggregated point clouds from RGBD videos. We benchmark four tasks with WildRGB-D including novel view synthesis, camera pose estimation, object 6d pose estimation, and object surface reconstruction. Our experiments show that the large-scale capture of RGB-D objects provides a large potential to advance 3D object learning. Our project page is https://***/.

关键词： computer vision

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Capturing Humans in Motion: Temporal-Attentive 3D Human Pose and Shape Estimation from Monocular Video

Capturing Humans in Motion: Temporal-Attentive 3D Human Pose...

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

作者： Wei, Wen-Li Lin, Jen-Chun Liu, Tyng-Luh Liao, Hong-Yuan Mark Acad Sinica Inst Informat Sci New Taipei Taiwan Providence Univ Taichung Taiwan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Learning to capture human motion is essential to 3D human pose and shape estimation from monocular video. However, the existing methods mainly rely on recurrent or convolutional operation to model such temporal information, which limits the ability to capture non-local context relations of human motion. To address this problem, we propose a motion pose and shape network (MPS-Net) to effectively capture humans in motion to estimate accurate and temporally coherent 3D human pose and shape from a video. Specifically, we first propose a motion continuity attention (MoCA) module that leverages visual cues observed from human motion to adaptively recalibrate the range that needs attention in the sequence to better capture the motion continuity dependencies. Then, we develop a hierarchical attentive feature integration (HAFI) module to effectively combine adjacent past and future feature representations to strengthen temporal correlation and refine the feature representation of the current frame. By coupling the MoCA and HAFI modules, the proposed MPS-Net excels in estimating 3D human pose and shape in the video. Though conceptually simple, our MPS-Net not only outperforms the state-of-the-art methods on the 3DPW, MPI-INF-3DHP, and Human3.6M benchmark datasets, but also uses fewer network parameters.

关键词： Couplings Visualization computer vision Three-dimensional displays Correlation Shape Computational modeling

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Estimating Egocentric 3D Human Pose in the Wild with External Weak Supervision

Estimating Egocentric 3D Human Pose in the Wild with Externa...

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

作者： Wang, Jian Liu, Lingjie Xu, Weipeng Sarkar, Kripasindhu Luvizon, Diogo Theobalt, Christian MPI Informat Saarbrucken Germany Saarland Informat Campus Saarbrucken Germany Facebook Real Labs Menlo Pk CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Egocentric 3D human pose estimation with a single fish-eye camera has drawn a significant amount of attention recently. However, existing methods struggle with pose estimation from in-the-wild images, because they can only be trained on synthetic data due to the unavailability of large-scale in-the-wild egocentric datasets. Furthermore, these methods easily fail when the body parts are occluded by or interacting with the surrounding scene. To address the shortage of in-the-wild data, we collect a large-scale in-the-wild egocentric dataset called Egocentric Poses in the Wild (EgoPW). This dataset is captured by a head-mounted fish-eye camera and an auxiliary external camera, which provides an additional observation of the human body from a third-person perspective during training. We present a new egocentric pose estimation method, which can be trained on the new dataset with weak external supervision. Specifically, we first generate pseudo labels for the EgoPW dataset with a spatio-temporal optimization method by incorporating the external-view supervision. The pseudo labels are then used to train an egocentric pose estimation network. To facilitate the network training, we propose a novel learning strategy to supervise the egocentric features with the high-quality features extracted by a pretrained external-view pose estimation model. The experiments show that our method predicts accurate 3D poses from a single in-the wild egocentric image and outperforms the state-of-the-art methods both quantitatively and qualitatively.

关键词： Training computer vision Three-dimensional displays Pose estimation Optimization methods Cameras Feature extraction

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Expanding Low-Density Latent Regions for Open-Set Object Detection

Expanding Low-Density Latent Regions for Open-Set Object Det...

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

作者： Han, Jiaming Ren, Yuqiang Ding, Jian Pan, Xingjia Yan, Ke Xia, Gui-Song Wuhan Univ NERCMS Sch Comp Sci Wuhan Peoples R China Wuhan Univ State Key Lab LIESMARS Wuhan Peoples R China Tencent YouTu Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Modern object detectors have achieved impressive progress under the close-set setup. However, open-set object detection (OSOD) remains challenging since objects of unknown categories are often misclassified to existing known classes. In this work, we propose to identify unknown objects by separating high/low-density regions in the latent space, based on the consensus that unknown objects are usually distributed in low-density latent regions. As traditional threshold-based methods only maintain limited low-density regions, which cannot cover all unknown objects, we present a novel Open-set Detector (OpenDet) with expanded low-density regions. To this aim, we equip OpenDet with two learners, Contrastive Feature Learner (CFL) and Unknown Probability Learner (UPL). CFL performs instance-level contrastive learning to encourage compact features of known classes, leaving more low-density regions for unknown classes;UPL optimizes unknown probability based on the uncertainty of predictions, which further divides more low-density regions around the cluster of known classes. Thus, unknown objects in low-density regions can be easily identified with the learned unknown probability. Extensive experiments demonstrate that our method can significantly improve the OSOD performance, e.g., OpenDet reduces the Absolute Open-Set Errors by 25%-35% on six OSOD benchmarks.

关键词： Measurement computer vision Uncertainty Codes Detectors Object detection Benchmark testing

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Towards Efficient Data Free Black-box Adversarial Attack

Towards Efficient Data Free Black-box Adversarial Attack

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

作者： Zhang, Jie Li, Bo Xu, Jianghe Wu, Shuang Ding, Shouhong Zhang, Lei Wu, Chao Zhejiang Univ Hangzhou Peoples R China Tencent Youtu Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Classic black-box adversarial attacks can take advantage of transferable adversarial examples generated by a similar substitute model to successfully fool the target model. However, these substitute models need to be trained by target models' training data, which is hard to acquire due to privacy or transmission reasons. Recognizing the limited availability of real data for adversarial queries, recent works proposed to train substitute models in a data-free black-box scenario. However, their generative adversarial networks (GANs) based framework suffers from the convergence failure and the model collapse, resulting in low efficiency. In this paper, by rethinking the collaborative relationship between the generator and the substitute model, we design a novel black-box attack framework. The proposed method can efficiently imitate the target model through a small number of queries and achieve high attack success rate. The comprehensive experiments over six datasets demonstrate the effectiveness of our method against the state-of-the-art attacks. Especially, we conduct both label-only and probability-only attacks on the Microsoft Azure online model, and achieve a 100% attack success rate with only 0.46% query budget of the SOTA method [49].

关键词： Data privacy computer vision Computational modeling Training data Machine learning Generative adversarial networks Data models

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Learning from the Master: Distilling Cross-modal Advanced Knowledge for Lip Reading

Learning from the Master: Distilling Cross-modal Advanced Kn...

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

作者： Ren, Sucheng Du, Yong Lv, Jianming Han, Guoqiang He, Shengfeng South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China Ocean Univ China Dept Comp Sci & Technol Qingdao Peoples R China

ISBN: (纸本)9781665445092

Lip reading aims to predict the spoken sentences from silent lip videos. Due to the fact that such a vision task usually performs worse than its counterpart speech recognition, one potential scheme is to distill knowledge from a teacher pretrained by audio signals. However, the latent domain gap between the cross-modal data could lead to a learning ambiguity and thus limits the performance of lip reading. In this paper, we propose a novel collaborative framework for lip reading, and two aspects of issues are considered: 1) the teacher should understand bi-modal knowledge to possibly bridge the inherent cross-modal gap;2) the teacher should adjust teaching contents adaptively with the evolution of the student. To these ends, we introduce a trainable "master" network which ingests both audio signals and silent lip videos instead of a pretrained teacher. The master produces logits from three modalities of features: audio modality, video modality, and their combination. To further provide an interactive strategy to fuse these knowledge organically, we regularize the master with the task-specific feedback from the student, in which the requirement of the student is implicitly embedded. Meanwhile, we involve a couple of "tutor" networks into our system as guidance for emphasizing the fruitful knowledge flexibly. In addition, we incorporate a curriculum learning design to ensure a better convergence. Extensive experiments demonstrate that the proposed network outperforms the state-of-the-art methods on several benchmarks, including in both word-level and sentence-level scenarios.

关键词： Knowledge engineering computer vision Fuses Lips Computational modeling Education Speech recognition

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