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Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation

Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dy...

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

作者： Yao, Lewei Pi, Renjie Xu, Hang Zhang, Wei Li, Zhenguo Zhang, Tong Hong Kong Univ Sci & Technol Hong Kong Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China

ISBN: (纸本)9781665445092

We propose Joint-DetNAS, a unified NAS framework for object detection, which integrates 3 key components: Neural Architecture Search, pruning, and Knowledge Distillation. Instead of naively pipelining these techniques, our Joint-DetNAS optimizes them jointly. The algorithm consists of two core processes: student morphism optimizes the student's architecture and removes the redundant parameters, while dynamic distillation aims to find the optimal matching teacher. For student morphism, weight inheritance strategy is adopted, allowing the student to flexibly update its architecture while fully utilize the predecessor's weights, which considerably accelerates the search;To facilitate dynamic distillation, an elastic teacher pool is trained via integrated progressive shrinking strategy, from which teacher detectors can be sampled without additional cost in subsequent searches. Given a base detector as the input, our algorithm directly outputs the derived student detector with high performance without additional training. Experiments demonstrate that our Joint-DetNAS outperforms the naive pipelining approach by a great margin. Given a classic R101-FPN as the base detector, Joint-DetNAS is able to boost its mAP from 41.4 to 43.9 on MS COCO and reduce the latency by 47%, which is on par with the SOTA EfficientDet while requiring less search cost. We hope our proposed method can provide the community with a new way of jointly optimizing NAS, KD and pruning.

关键词： Training Costs Heuristic algorithms Detectors Object detection computer architecture Search problems

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Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks

Neural Parts: Learning Expressive 3D Shape Abstractions with...

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

作者： Paschalidou, Despoina Katharopoulos, Angelos Geiger, Andreas Fidler, Sanja Max Planck Inst Intelligent Syst Tubingen Tubingen Germany Univ Tubingen Tubingen Germany Idiap Res Inst Martigny Switzerland Ecole Polytech Fed Lausanne EPFL Lausanne Switzerland Max Planck ETH Ctr Learning Syst Zurich Switzerland NVIDIA Santa Clara CA 95051 USA Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (纸本)9781665445092

Impressive progress in 3D shape extraction led to representations that can capture object geometries with high fidelity. In parallel, primitive-based methods seek to represent objects as semantically consistent part arrangements. However, due to the simplicity of existing primitive representations, these methods fail to accurately reconstruct 3D shapes using a small number of primitives/parts. We address the trade-off between reconstruction quality and number of parts with Neural Parts, a novel 3D primitive representation that defines primitives using an Invertible Neural Network (INN) which implements homeomorphic mappings between a sphere and the target object. The INN allows us to compute the inverse mapping of the homeomorphism, which in turn, enables the efficient computation of both the implicit surface function of a primitive and its mesh, without any additional post-processing. Our model learns to parse 3D objects into semantically consistent part arrangements without any part-level supervision. Evaluations on ShapeNet, D-FAUST and FreiHAND demonstrate that our primitives can capture complex geometries and thus simultaneously achieve geometrically accurate as well as interpretable reconstructions using an order of magnitude fewer primitives than state-of-the-art shape abstraction methods.

关键词： Geometry Solid modeling computer vision Three-dimensional displays Shape Computational modeling Neural networks

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SelfSAGCN: Self-Supervised Semantic Alignment for Graph Convolution Network

SelfSAGCN: Self-Supervised Semantic Alignment for Graph Conv...

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

作者： Yang, Xu Deng, Cheng Dang, Zhiyuan Wei, Kun Yan, Junchi Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Shanghai Jiao Tong Univ Dept CSE Shanghai Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781665445092

Graph convolution networks (GCNs) are a powerful deep learning approach and have been successfully applied to representation learning on graphs in a variety of real-world applications. Despite their success, two fundamental weaknesses of GCNs limit their ability to represent graph-structured data: poor performance when labeled data are severely scarce and indistinguishable features when more layers are stacked. In this paper, we propose a simple yet effective Self-Supervised Semantic Alignment Graph Convolution Network (SelfSAGCN), which consists of two crux techniques: Identity Aggregation and Semantic Alignment, to overcome these weaknesses. The behind basic idea is the node features in the same class but learned from semantic and graph structural aspects respectively, are expected to be mapped nearby. Specifically, the Identity Aggregation is applied to extract semantic features from labeled nodes, the Semantic Alignment is utilized to align node features obtained from different aspects using the class central similarity. In this way, the over-smoothing phenomenon is alleviated, while the similarities between the unlabeled features and labeled ones from the same class are enhanced. Experimental results on five popular datasets show that the proposed SelfSAGCN outperforms state-of-the-art methods on various classification tasks.

关键词： Deep learning computer vision Convolution Computational modeling Semantics Benchmark testing Feature extraction

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ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring

ARVo: Learning All-Range Volumetric Correspondence for Video...

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

作者： Li, Dongxu Xu, Chenchen Zhang, Kaihao Yu, Xin Zhong, Yiran Ren, Wenqi Suominen, Hanna Li, Hongdong Australian Natl Univ Canberra ACT Australia DATA61 CSIRO Sydney NSW Australia UTS Sydney NSW Australia Chinese Acad Sci IIE Beijing Peoples R China Univ Turku Turku Finland

ISBN: (纸本)9781665445092

Video deblurring models exploit consecutive frames to remove blurs from camera shakes and object motions. In order to utilize neighboring sharp patches, typical methods rely mainly on homography or optical flows to spatially align neighboring blurry frames. However, such explicit approaches are less effective in the presence of fast motions with large pixel displacements. In this work, we propose a novel implicit method to learn spatial correspondence among blurry frames in the feature space. To construct distant pixel correspondences, our model builds a correlation volume pyramid among all the pixel-pairs between neighboring frames. To enhance the features of the reference frame, we design a correlative aggregation module that maximizes the pixel-pair correlations with its neighbors based on the volume pyramid. Finally, we feed the aggregated features into a reconstruction module to obtain the restored frame. We design a generative adversarial paradigm to optimize the model progressively. Our proposed method is evaluated on the widely-adopted DVD dataset, along with a newly collected High-Frame-Rate (1000 fps) Dataset for Video Deblurring (HFR-DVD). Quantitative and qualitative experiments show that our model performs favorably on both datasets against previous state-of-the-art methods, confirming the benefit of modeling all-range spatial correspondence for video deblurring.

关键词： Training Solid modeling computer vision Correlation DVD Computational modeling Cameras

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Learning a Self-Expressive Network for Subspace Clustering

Learning a Self-Expressive Network for Subspace Clustering

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

作者： Zhang, Shangzhi You, Chong Vidal, Rene Li, Chun-Guang Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China Univ Calif Berkeley Dept EECS Berkeley CA 94720 USA Johns Hopkins Univ Math Inst Data Sci Baltimore MD USA

ISBN: (纸本)9781665445092

State-of-the-art subspace clustering methods are based on the self-expressive model, which represents each data point as a linear combination of other data points. However, such methods are designed for a finite sample dataset and lack the ability to generalize to out-of-sample data. Moreover, since the number of self-expressive coefficients grows quadratically with the number of data points, their ability to handle large-scale datasets is often limited. In this paper, we propose a novel framework for subspace clustering, termed Self-Expressive Network (SENet), which employs a properly designed neural network to learn a self-expressive representation of the data. We show that our SENet can not only learn the self-expressive coefficients with desired properties on the training data, but also handle out-of-sample data. Besides, we show that SENet can also be leveraged to perform subspace clustering on large-scale datasets. Extensive experiments conducted on synthetic data and real world benchmark data validate the effectiveness of the proposed method. In particular, SENet yields highly competitive performance on MNIST, Fashion MNIST and Extended MNIST and state-of-the-art performance on CIFAR-10.

关键词： Training computer vision Computational modeling Clustering methods Neural networks Training data Stochastic processes

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Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation

Non-Salient Region Object Mining for Weakly Supervised Seman...

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

作者： Yao, Yazhou Chen, Tao Xie, Guo-Sen Zhang, Chuanyi Shen, Fumin Wu, Qi Tang, Zhenmin Zhang, Jian Nanjing Univ Sci & Technol Nanjing Peoples R China Mohamed bin Zayed Univ AI Abu Dhabi U Arab Emirates Univ Elect Sci & Technol China Chengdu Peoples R China Univ Adelaide Adelaide SA Australia Univ Technol Sydney Sydney NSW Australia

ISBN: (纸本)9781665445092

Semantic segmentation aims to classify every pixel of an input image. Considering the difficulty of acquiring dense labels, researchers have recently been resorting to weak labels to alleviate the annotation burden of segmentation. However, existing works mainly concentrate on expanding the seed of pseudo labels within the image's salient region. In this work, we propose a non-salient region object mining approach for weakly supervised semantic segmentation. We introduce a graph-based global reasoning unit to strengthen the classification network's ability to capture global relations among disjoint and distant regions. This helps the network activate the object features outside the salient area. To further mine the non-salient region objects, we propose to exert the segmentation network's self-correction ability. Specifically, a potential object mining module is proposed to reduce the false-negative rate in pseudo labels. Moreover, we propose a non-salient region masking module for complex images to generate masked pseudo labels. Our non-salient region masking module helps further discover the objects in the non-salient region. Extensive experiments on the PASCAL VOC dataset demonstrate state-of-the-art results compared to current methods.

关键词： Image segmentation computer vision Codes Annotations Image edge detection Semantics Cognition

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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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Delving into Localization Errors for Monocular 3D Object Detection

Delving into Localization Errors for Monocular 3D Object Det...

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

作者： Ma, Xinzhu Zhang, Yinmin Xu, Dan Zhou, Dongzhan Yi, Shuai Li, Haojie Ouyang, Wanli Univ Sydney Sydney NSW Australia Hong Kong Univ Sci & Technol Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Dalian Univ Technol Dalian Peoples R China

ISBN: (纸本)9781665445092

Estimating 3D bounding boxes from monocular images is an essential component in autonomous driving, while accurate 3D object detection from this kind of data is very challenging. In this work, by intensive diagnosis experiments, we quantify the impact introduced by each sub-task and found the 'localization error' is the vital factor in restricting monocular 3D detection. Besides, we also investigate the underlying reasons behind localization errors, analyze the issues they might bring, and propose three strategies. First, we revisit the misalignment between the center of the 2D bounding box and the projected center of the 3D object, which is a vital factor leading to low localization accuracy. Second, we observe that accurately localizing distant objects with existing technologies is almost impossible, while those samples will mislead the learned network. To this end, we propose to remove such samples from the training set for improving the overall performance of the detector. Lastly, we also propose a novel 3D IoU oriented loss for the size estimation of the object, which is not affected by 'localization error'. We conduct extensive experiments on the KITTI dataset, where the proposed method achieves real-time detection and outperforms previous methods by a large margin. The code will be made available at: https://***/xinzhuma/monodle.

关键词： Location awareness Training computer vision Three-dimensional displays Estimation Object detection Detectors

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Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation

Cross-Domain Adaptive Clustering for Semi-Supervised Domain ...

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

作者： Li, Jichang Li, Guanbin Shi, Yemin Yu, Yizhou Univ Hong Kong Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Deepwise AI Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

In semi-supervised domain adaptation, a few labeled samples per class in the target domain guide features of the remaining target samples to aggregate around them. However, the trained model cannot produce a highly discriminative feature representation for the target domain because the training data is dominated by labeled samples from the source domain. This could lead to disconnection between the labeled and unlabeled target samples as well as misalignment between unlabeled target samples and the source domain. In this paper, we propose a novel approach called Cross-domain Adaptive Clustering to address this problem. To achieve both inter-domain and intra-domain adaptation, we first introduce an adversarial adaptive clustering loss to group features of unlabeled target data into clusters and perform cluster-wise feature alignment across the source and target domains. We further apply pseudo labeling to unlabeled samples in the target domain and retain pseudo-labels with high confidence. Pseudo labeling expands the number of "labeled" samples in each class in the target domain, and thus produces a more robust and powerful cluster core for each class to facilitate adversarial learning. Extensive experiments on benchmark datasets, including DomainNet, Office-Home and Office, demonstrate that our proposed approach achieves the state-of-the-art performance in semi-supervised domain adaptation.

关键词： Training Adaptation models computer vision Training data Benchmark testing Data models Adversarial machine learning

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Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation

Pose-Controllable Talking Face Generation by Implicitly Modu...

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

作者： Zhou, Hang Sun, Yasheng Wu, Wayne Loy, Chen Change Wang, Xiaogang Liu, Ziwei Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Tokyo Inst Technol Tokyo Japan Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9781665445092

While accurate lip synchronization has been achieved for arbitrary-subject audio-driven talking face generation, the problem of how to efficiently drive the head pose remains. Previous methods rely on pre-estimated structural information such as landmarks and 3D parameters, aiming to generate personalized rhythmic movements. However, the inaccuracy of such estimated information under extreme conditions would lead to degradation problems. In this paper, we propose a clean yet effective framework to generate posecontrollable talking faces. We operate on non-aligned raw face images, using only a single photo as an identity reference. The key is to modularize audio-visual representations by devising an implicit low-dimension pose code. Substantially, both speech content and head pose information lie in a joint non-identity embedding space. While speech content information can be defined by learning the intrinsic synchronization between audio-visual modalities, we identify that a pose code will be complementarily learned in a modulated convolution-based reconstruction framework. Extensive experiments show that our method generates accurately lip-synced talking faces whose poses are controllable by other videos. Moreover, our model has multiple advanced capabilities including extreme view robustness and talking face frontalization.(1)

关键词： Three-dimensional displays Speech coding Face recognition Lips Speech recognition Aerospace electronics Robustness

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