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SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation

SCF-Net: Learning Spatial Contextual Features for Large-Scal...

引用

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

作者： Fan, Siqi Dong, Qiulei Zhu, Fenghua Lv, Yisheng Ye, Peijun Wang, Fei-Yue CASIA State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China CASIA Natl Lab Pattern Recognit Beijing Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

How to learn effective features from large-scale point clouds for semantic segmentation has attracted increasing attention in recent years. Addressing this problem, we propose a learnable module that learns Spatial Contextual Features from large-scale point clouds, called SCF in this paper. The proposed module mainly consists of three blocks, including the local polar representation block, the dual-distance attentive pooling block, and the global contextual feature block. For each 3D point, the local polar representation block is firstly explored to construct a spatial representation that is invariant to the z-axis rotation, then the dual-distance attentive pooling block is designed to utilize the representations of its neighbors for learning more discriminative local features according to both the geometric and feature distances among them, and finally, the global contextual feature block is designed to learn a global context for each 3D point by utilizing its spatial location and the volume ratio of the neighborhood to the global point cloud. The proposed module could be easily embedded into various network architectures for point cloud segmentation, naturally resulting in a new 3D semantic segmentation network with an encoder-decoder architecture, called SCF-Net in this work. Extensive experimental results on two public datasets demonstrate that the proposed SCF-Net performs better than several state-of-the-art methods in most cases.

关键词： computer vision Three-dimensional displays Semantics computer architecture Network architecture pattern recognition

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VideoSAGE: Video Summarization with Graph Representation Learning

VideoSAGE: Video Summarization with Graph Representation Lea...

引用

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

作者： Jose M. Rojas Chaves Subarna Tripathi Intel Corporation Intel Labs

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We propose a graph-based representation learning framework for video summarization. First, we convert an input video to a graph where nodes correspond to each of the video frames. Then, we impose sparsity on the graph by connecting only those pairs of nodes that are within a specified temporal distance. We then formulate the video summarization task as a binary node classification problem, precisely classifying video frames whether they should belong to the output summary video. A graph constructed this way aims to capture long-range interactions among video frames, and the sparsity ensures the model trains without hitting the memory and compute bottleneck. Experiments on two datasets(SumMe and TVSum) demonstrate the effectiveness of the proposed nimble model compared to existing state-of-the-art summarization approaches while being one order of magnitude more efficient in compute time and memory.

关键词： Representation learning computer vision Computational modeling conferences Memory management pattern recognition

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ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning

ORDisCo: Effective and Efficient Usage of Incremental Unlabe...

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

作者： Wang, Liyuan Yang, Kuo Li, Chongxuan Hong, Lanqing Li, Zhenguo Zhu, Jun Tsinghua Univ BNRist Ctr Inst AI Dept Comp Sci & TechTHBI Lab Beijing Peoples R China Huawei Noahs Ark Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Continual learning usually assumes the incoming data are fully labeled, which might not be applicable in real applications. In this work, we consider semi-supervised continual learning (SSCL) that incrementally learns from partially labeled data. Observing that existing continual learning methods lack the ability to continually exploit the unlabeled data, we propose deep Online Replay with Discriminator Consistency (ORDisCo) to interdependently learn a classifier with a conditional generative adversarial network (GAN), which continually passes the learned data distribution to the classifier. In particular, ORDisCo replays data sampled from the conditional generator to the classifier in an online manner, exploiting unlabeled data in a time- and storage-efficient way. Further, to explicitly overcome the catastrophic forgetting of unlabeled data, we selectively stabilize parameters of the discriminator that are important for discriminating the pairs of old unlabeled data and their pseudo-labels predicted by the classifier. We extensively evaluate ORDisCo on various semi-supervised learning benchmark datasets for SSCL, and show that ORDisCo achieves significant performance improvement on SVHN, CIFAR10 and Tiny-ImageNet, compared to strong baselines.

关键词： Deep learning computer vision Systematics Semisupervised learning Benchmark testing Generative adversarial networks Generators

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Cleaning Noisy Labels by Negative Ensemble Learning for Source-Free Unsupervised Domain Adaptation 22

Cleaning Noisy Labels by Negative Ensemble Learning for Sour...

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22nd ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Ahmed, Waqar Morerio, Pietro Murino, Vittorio Ist Italiano Tecnol Pattern Anal & Amp Comp Vis Genoa Italy Univ Genoa Dipartimento Ingn Navale Elettr Elettron & Teleco Genoa Italy Univ Verona Dipartimento Informat Verona Italy

ISBN: (纸本)9781665409155

Conventional Unsupervised Domain Adaptation (UDA) methods presume source and target domain data to be simultaneously available during training. Such an assumption may not hold in practice, as source data is often inaccessible (e.g., due to privacy reasons). On the contrary, a pre-trained source model is usually available, which performs poorly on target due to the well-known domain shift problem. This translates into a significant amount of mis-classifications, which can be interpreted as structured noise affecting the inferred target pseudo-labels. In this work, we cast UDA as a pseudo-label refinery problem in the challenging source-free scenario. We propose Negative Ensemble Learning (NEL) technique, a unified method for adaptive noise filtering and progressive pseudo-label refinement. NEL is devised to tackle noisy pseudo-labels by enhancing diversity in ensemble members with different stochastic (i) input augmentation and (ii) feedback. The latter is achieved by leveraging the novel concept of Disjoint Residual Labels, which allow propagating diverse information to the different members. Eventually, a single model is trained with the refined pseudo-labels, which leads to a robust performance on the target domain. Extensive experiments show that the proposed method achieves state-of-the-art performance on major UDA benchmarks, such as Digit5, PACS, Visda-C, and DomainNet, without using source data samples at all.

关键词： Training Data privacy computer vision Filtering Stochastic processes Benchmark testing Cleaning

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D-NeRF: Neural Radiance Fields for Dynamic Scenes

D-NeRF: Neural Radiance Fields for Dynamic Scenes

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

作者： Pumarola, Albert Corona, Enric Pons-Moll, Gerard Moreno-Noguer, Francesc UPC Inst Robbt & Informat Ind CSIC Barcelona Spain Univ Tubingen Tubingen Germany Max Planck Inst Informat Saarbrucken Germany

ISBN: (纸本)9781665445092

Neural rendering techniques combining machine learning with geometric reasoning have arisen as one of the most promising approaches for synthesizing novel views of a scene from a sparse set of images. Among these, stands out the Neural radiance fields (NeRF) [31], which trains a deep network to map 5D input coordinates (representing spatial location and viewing direction) into a volume density and view-dependent emitted radiance. However, despite achieving an unprecedented level of photorealism on the generated images, NeRF is only applicable to static scenes, where the same spatial location can be queried from different images. In this paper we introduce D-NeRF, a method that extends neural radiance fields to a dynamic domain, allowing to reconstruct and render novel images of objects under rigid and non-rigid motions from a single camera moving around the scene. For this purpose we consider time as an additional input to the system, and split the learning process in two main stages: one that encodes the scene into a canonical space and another that maps this canonical representation into the deformed scene at a particular time. Both mappings are simultaneously learned using fully-connected networks. Once the networks are trained, D-NeRF can render novel images, controlling both the camera view and the time variable, and thus, the object movement. We demonstrate the effectiveness of our approach on scenes with objects under rigid, articulated and non-rigid motions. Code, model weights and the dynamic scenes dataset will be available at [1].

关键词： Photorealism computer vision Three-dimensional displays Dynamics Machine learning Cameras Rendering (computer graphics)

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SimPoE: Simulated Character Control for 3D Human Pose Estimation

SimPoE: Simulated Character Control for 3D Human Pose Estima...

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

作者： Yuan, Ye Wei, Shih-En Simon, Tomas Kitani, Kris Saragih, Jason Carnegie Mellon Univ Pittsburgh PA 15213 USA Facebook Real Labs Redmond WA USA

ISBN: (纸本)9781665445092

Accurate estimation of 3D human motion from monocular video requires modeling both kinematics (body motion without physical forces) and dynamics (motion with physical forces). To demonstrate this, we present SimPoE, a Simulation-based approach for 3D human Pose Estimation, which integrates image-based kinematic inference and physics-based dynamics modeling. SimPoE learns a policy that takes as input the current frame pose estimate and the next image frame to control a physically-simulated character to output the next-frame pose estimate. The policy contains a learnable kinematic pose refinement unit that uses 2D keypoints to iteratively refine its kinematic pose estimate of the next frame. Based on this refined kinematic pose, the policy learns to compute dynamics-based control (e.g., joint torques) of the character to advance the current frame pose estimate to the pose estimate of the next frame. This design couples the kinematic pose refinement unit with the dynamics-based control generation unit, which are learned jointly with reinforcement learning to achieve accurate and physically-plausible pose estimation. Furthermore, we propose a meta-control mechanism that dynamically adjusts the character's dynamics parameters based on the character state to attain more accurate pose estimates. Experiments on large-scale motion datasets demonstrate that our approach establishes the new state of the art in pose accuracy while ensuring physical plausibility.

关键词： Solid modeling computer vision Three-dimensional displays Pose estimation Dynamics Kinematics Reinforcement learning

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Learning to Fuse Asymmetric Feature Maps in Siamese Trackers

Learning to Fuse Asymmetric Feature Maps in Siamese Trackers

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

作者： Han, Wencheng Dong, Xingping Khan, Fahad Shahbaz Shao, Ling Shen, Jianbing Beijing Inst Technol Beijing Peoples R China Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9781665445092

Recently, Siamese-based trackers have achieved promising performance in visual tracking. Most recent Siamese-based trackers typically employ a depth-wise cross-correlation (DW-XCorr) to obtain multi-channel correlation information from the two feature maps (target and search region). However, DW-XCorr has several limitations within Siamese-based tracking: it can easily be fooled by distractors, has fewer activated channels and provides weak discrimination of object boundaries. Further, DW-XCorr is a handcrafted parameter-free module and cannot fully benefit from offline learning on large-scale data. We propose a learnable module, called the asymmetric convolution (ACM), which learns to better capture the semantic correlation information in offline training on largescale data. Different from DW-XCorr and its predecessor (XCorr), which regard a single feature map as the convolution kernel, our ACM decomposes the convolution operation on a concatenated feature map into two mathematically equivalent operations, thereby avoiding the need for the feature maps to be of the same size (width and height) during concatenation. Our ACM can incorporate useful prior information, such as bounding-box size, with standard visual features. Furthermore, ACM can easily be integrated into existing Siamese trackers based on DW-XCorr or XCorr. To demonstrate its generalization ability, we integrate ACM into three representative trackers: SiamFC, SiamRPN++ and SiamBAN. Our experiments reveal the benefits of the proposed ACM, which outperforms existing methods on six tracking benchmarks. On the LaSOT test set, our ACM-based tracker obtains a significant improvement of 5.8% in terms of success (AUC), over the baseline.

关键词： Training Visualization Target tracking Correlation Convolution Fuses pattern recognition

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MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization

MultiBodySync: Multi-Body Segmentation and Motion Estimation...

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

作者： Huang, Jiahui Wang, He Birdal, Tolga Sung, Minhyuk Arrigoni, Federica Hu, Shi-Min Guibas, Leonidas Tsinghua Univ Beijing Peoples R China Stanford Univ Stanford CA 94305 USA Korea Adv Inst Sci & Technol Daejeon South Korea Univ Trento Trento TN Italy

ISBN: (纸本)9781665445092

We present MultiBodySync, a novel, end-to-end trainable multi-body motion segmentation and rigid registration framework for multiple input 3D point clouds. The two non-trivial challenges posed by this multi-scan multibody setting that we investigate are: (i) guaranteeing correspondence and segmentation consistency across multiple input point clouds capturing different spatial arrangements of bodies or body parts;and (ii) obtaining robust motion-based rigid body segmentation applicable to novel object categories. We propose an approach to address these issues that incorporates spectral synchronization into an iterative deep declarative network, so as to simultaneously recover consistent correspondences as well as motion segmentation. At the same time, by explicitly disentangling the correspondence and motion segmentation estimation modules, we achieve strong generalizability across different object categories. Our extensive evaluations demonstrate that our method is effective on various datasets ranging from rigid parts in articulated objects to individually moving objects in a 3D scene, be it single-view or full point clouds. Code at https: //***/huangjh-pub/multibody-sync.

关键词： computer vision Three-dimensional displays Motion segmentation Scalability Motion estimation Pipelines Dynamics

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Bottom-Up Shift and Reasoning for Referring Image Segmentation

Bottom-Up Shift and Reasoning for Referring Image Segmentati...

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

作者： Yang, Sibei Xia, Meng Li, Guanbin Zhou, Hong-Yu Yu, Yizhou ShanghaiTech Univ Shanghai Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Univ Hong Kong Hong Kong Peoples R China Deepwise AI Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

Referring image segmentation aims to segment the referent that is the corresponding object or stuff referred by a natural language expression in an image. Its main challenge lies in how to effectively and efficiently differentiate between the referent and other objects of the same category as the referent. In this paper, we tackle the challenge by jointly performing compositional visual reasoning and accurate segmentation in a single stage via the proposed novel Bottom-Up Shift (BUS) and Bidirectional Attentive Refinement (BIAR) modules. Specifically, BUS progressively locates the referent along hierarchical reasoning steps implied by the expression. At each step, it locates the corresponding visual region by disambiguating between similar regions, where the disambiguation bases on the relationships between regions. By the explainable visual reasoning, BUS explicitly aligns linguistic components with visual regions so that it can identify all the mentioned entities in the expression. BIAR fuses multi-level features via a twoway attentive message passing, which captures the visual details relevant to the referent to refine segmentation results. Experimental results demonstrate that the proposed method consisting of BUS and BIAR modules, can not only consistently surpass all existing state-of-the-art algorithms across common benchmark datasets but also visualize interpretable reasoning steps for stepwise segmentation. Code is available at https://***/incredibleXM/BUSNet.

关键词： Location awareness Visualization Image segmentation computer vision Fuses Message passing Computational modeling

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Improving OCR-based Image Captioning by Incorporating Geometrical Relationship

Improving OCR-based Image Captioning by Incorporating Geomet...

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

作者： Wang, Jing Tang, Jinhui Yang, Mingkun Bai, Xiang Luo, Jiebo Nanjing Univ Sci & Technol Nanjing Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China Univ Rochester Rochester NY 14627 USA

ISBN: (纸本)9781665445092

OCR-based image captioning aims to automatically describe images based on all the visual entities (both visual objects and scene text) in images. Compared with conventional image captioning, the reasoning of scene text is required for OCR-based image captioning since the generated descriptions often contain multiple OCR tokens. Existing methods attempt to achieve this goal via encoding the OCR tokens with rich visual and semantic representations. However, strong correlations between OCR tokens may not be established with such limited representations. In this paper, we propose to enhance the connections between OCR tokens from the viewpoint of exploiting the geometrical relationship. We comprehensively consider the height, width, distance, IoU and orientation relations between the OCR tokens for constructing the geometrical relationship. To integrate the learned relation as well as the visual and semantic representations into a unified framework, a Long Short-Term Memory plus Relation-aware pointer network (LSTM-R) architecture is presented in this paper. Under the guidance of the geometrical relationship between OCR tokens, our LSTM-R capitalizes on a newly-devised relation-aware pointer network to select OCR tokens from the scene text for OCR-based image captioning. Extensive experiments demonstrate the effectiveness of our LSTM-R. More remarkably, LSTM-R achieves state-of-the-art performance on TextCaps, with the CIDEr-D score being increased from 98.0% to 109.3%.

关键词： Training Visualization computer vision Correlation Computational modeling Semantics computer architecture

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