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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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Squeeze-and-Excitation Networks 31

Squeeze-and-Excitation Networks

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31st IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Hu, Jie Shen, Li Sun, Gang Momenta Beijing Peoples R China Univ Oxford Dept Engn Sci Oxford England

ISBN: (纸本)9781538664209

Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel- wise information together within local receptive fields. In order to boost the representational power of a network, several recent approaches have shown the benefit of enhancing spatial encoding. In this work, we focus on the channel relationship and propose a novel architectural unit, which we term the " Squeezeand- Excitation" ( SE) block, that adaptively recalibrates channel- wise feature responses by explicitly modelling interdependencies between channels. We demonstrate that by stacking these blocks together we can construct SENet architectures that generalise extremely well across challenging datasets. Crucially, we find that SE blocks produce significant performance improvements for existing state- ofthe- art deep architectures at minimal additional computational cost. SENets formed the foundation of our ILSVRC 2017 classification submission which won first place and significantly reduced the top- 5 error to 2.251%, achieving a- 25% relative improvement over the winning entry of 2016. Code and models are available at https : //***/hujie-frank/SENet.

关键词： computer architecture Computational modeling Convolution Task analysis Convolutional codes Adaptation models Stacking

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General Incremental Learning with Domain-aware Categorical Representations

General Incremental Learning with Domain-aware Categorical R...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Xie, Jiangwei Yan, Shipeng He, Xuming ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Continual learning is an important problem for achieving human-level intelligence in real-world applications as an agent must continuously accumulate knowledge in response to streaming data/tasks. In this work, we consider a general and yet under-explored incremental learning problem in which both the class distribution and class-specific domain distribution change over time. In addition to the typical challenges in class incremental learning, this setting also faces the intra-class stability-plasticity dilemma and intra-class domain imbalance problems. To address above issues, we develop a novel domain-aware continual learning method based on the EM framework. Specifically, we introduce a flexible class representation based on the von Mises-Fisher mixture model to capture the intra-class structure, using an expansion-and-reduction strategy to dynamically increase the number of components according to the class complexity. Moreover, we design a bi-level balanced memory to cope with data imbalances within and across classes, which combines with a distillation loss to achieve better inter- and intra-class stability-plasticity trade-off. We conduct exhaustive experiments on three benchmarks: iDigits, iDomainNet and iCIFAR-20. The results show that our approach consistently outperforms previous methods by a significant margin, demonstrating its superiority.

关键词： Learning systems computer vision Mixture models Benchmark testing pattern recognition Complexity theory Faces

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Hard Mixtures of Experts for Large ScaleWeakly Supervised vision 30

Hard Mixtures of Experts for Large ScaleWeakly Supervised Vi...

引用

30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Gross, Sam Ranzato, Marc'Aurelio Szlam, Arthur Facebook AI Res Menlo Pk CA USA

ISBN: (纸本)9781538604571

Training convolutional networks (CNN's) that fit on a single GPU with minibatch stochastic gradient descent has become effective in practice. However, there is still no effective method for training large CNN's that do not fit in the memory of a few GPU cards, or for parallelizing CNN training. In this work we show that a simple hard mixture of experts model can be efficiently trained to good effect on large scale hashtag (multilabel) prediction tasks. Mixture of experts models are not new [7, 3], but in the past, researchers have had to devise sophisticated methods to deal with data fragmentation. We show empirically that modern weakly supervised data sets are large enough to support naive partitioning schemes where each data point is assigned to a single expert. Because the experts are independent, training them in parallel is easy, and evaluation is cheap for the size of the model. Furthermore, we show that we can use a single decoding layer for all the experts, allowing a unified feature embedding space. We demonstrate that it is feasible (and in fact relatively painless) to train far larger models than could be practically trained with standard CNN architectures, and that the extra capacity can be well used on current datasets.

关键词： Professional personnel model size Dataset Functional training GRAPPER PICK UP Graphics Processing Unit expert model

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Geometric Transformer for Fast and Robust Point Cloud Registration

Geometric Transformer for Fast and Robust Point Cloud Regist...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Qin, Zheng Yu, Hao Wang, Changjian Guo, Yulan Peng, Yuxing Xu, Kai Natl Univ Def Technol Changsha Peoples R China Tech Univ Munich Munich Germany Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We study the problem of extracting accurate correspondences for point cloud registration. Recent keypoint free methods bypass the detection of repeatable keypoints which is difficult in low-overlap scenarios, showing great potential in registration. They seek correspondences over downsampled superpoints, which are then propagated to dense points. Superpoints are matched based on whether their neighboring patches overlap. Such sparse and loose matching requires contextual features capturing the geometric structure of the point clouds. We propose Geometric Transformer to learn geometric feature for robust superpoint matching. It encodes pair-wise distances and triplet-wise angles, making it robust in low-overlap cases and invariant to rigid transformation. The simplistic design attains surprisingly high matching accuracy such that no RANSAC is required in the estimation of alignment transformation, leading to 100 times acceleration. Our method improves the inlier ratio by 17 similar to 30 percentage points and the registration recall by over 7 points on the challenging 3DLoMatch benchmark.

关键词： Point cloud compression computer vision Codes Estimation Benchmark testing Transformers Encoding

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Fourier Contour Embedding for Arbitrary-Shaped Text Detection

Fourier Contour Embedding for Arbitrary-Shaped Text Detectio...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhu, Yiqin Chen, Jianyong Liang, Lingyu Kuang, Zhanghui Jin, Lianwen Zhang, Wayne South China Univ Technol Guangzhou Peoples R China SenseTime Res Shanghai Peoples R China Pazhou Lab Guangzhou Peoples R China Shanghai Jiao Tong Univ Qing Yuan Res Inst Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9781665445092

One of the main challenges for arbitrary-shaped text detection is to design a good text instance representation that allows networks to learn diverse text geometry variances. Most of existing methods model text instances in image spatial domain via masks or contour point sequences in the Cartesian or the polar coordinate system. However, the mask representation might lead to expensive post-processing, while the point sequence one may have limited capability to model texts with highly-curved shapes. To tackle these problems, we model text instances in the Fourier domain and propose one novel Fourier Contour Embedding (FCE) method to represent arbitrary shaped text contours as compact signatures. We further construct FCENet with a backbone, feature pyramid networks (FPN) and a simple post-processing with the Inverse Fourier Transformation (IFT) and Non-Maximum Suppression (N-MS). Different from previous methods, FCENet first predicts compact Fourier signatures of text instances, and then reconstructs text contours via IFT and NMS during test. Extensive experiments demonstrate that FCE is accurate and robust to fit contours of scene texts even with highly-curved shapes, and also validate the effectiveness and the good generalization of FCENet for arbitrary-shaped text detection. Furthermore, experimental results show that our FCENet is superior to the state-of-the-art (SOTA) methods on CTW1500 and Total-Text, especially on challenging highly-curved text subset.

关键词： Training Geometry computer vision Shape pattern recognition Image reconstruction

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Causality Inspired Representation Learning for Domain Generalization

Causality Inspired Representation Learning for Domain Genera...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Lv, Fangrui Liang, Jian Li, Shuang Zang, Bin Liu, Chi Harold Wang, Ziteng Liu, Di Beijing Inst Technol Beijing Peoples R China Alibaba Grp Hangzhou Peoples R China Yizhun Med AI Co Ltd Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Domain generalization (DG) is essentially an out-of-distribution problem, aiming to generalize the knowledge learned from multiple source domains to an unseen target domain. The mainstream is to leverage statistical models to model the dependence between data and labels, intending to learn representations independent of domain. Nevertheless, the statistical models are superficial descriptions of reality since they are only required to model dependence instead of the intrinsic causal mechanism. When the dependence changes with the target distribution, the statistic models may fail to generalize. In this regard, we introduce a general structural causal model to formalize the DG problem. Specifically, we assume that each input is constructed from a mix of causal factors (whose relationship with the label is invariant across domains) and non-causal factors (category-independent), and only the former cause the classification judgments. Our goal is to extract the causal factors from inputs and then reconstruct the invariant causal mechanisms. However, the theoretical idea is far from practical of DG since the required causal/non-causal factors are unobserved. We highlight that ideal causal factors should meet three basic properties: separated from the non-causal ones, jointly independent, and causally sufficient for the classification. Based on that, we propose a Causality Inspired Representation Learning (CIRL) algorithm that enforces the representations to satisfy the above properties and then uses them to simulate the causal factors, which yields improved generalization ability. Extensive experimental results on several widely used datasets verify the effectiveness of our approach.

关键词： Representation learning computer vision Data models pattern recognition Classification algorithms

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Task Decoupled Framework for Reference-based Super-Resolution

Task Decoupled Framework for Reference-based Super-Resolutio...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Huang, Yixuan Zhang, Xiaoyun Fu, Yu Chen, Siheng Zhang, Ya Wang, Yanfeng He, Dazhi Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Reference-based super-resolution(RefSR) has achieved impressive progress on the recovery of high-frequency details thanks to an additional reference high-resolution(HR) image input. Although the superiority compared with Single-Image Super-Resolution(SISR), existing RefSR methods easily result in the reference-underuse issue and the reference-misuse as shown in Fig.1. In this work, we deeply investigate the cause of the two issues and further propose a novel framework to mitigate them. Our studies find that the issues are mostly due to the improper coupled framework design of current methods. Those methods conduct the super-resolution task of the input low-resolution(LR) image and the texture transfer task from the reference image together in one module, easily introducing the interference between LR and reference features. Inspired by this finding, we propose a novel framework, which decouples the two tasks of RefSR, eliminating the interference between the LR image and the reference image. The super-resolution task upsamples the LR image leveraging only the LR image itself The texture transfer task extracts and transfers abundant textures from the reference image to the coarsely upsampled result of the super-resolution task Extensive experiments demonstrate clear improvements in both quantitative and qualitative evaluations over state-of-the-art methods.

关键词： computer vision Design methodology Superresolution Interference Benchmark testing Feature extraction pattern recognition

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Voxel Field Fusion for 3D Object Detection

Voxel Field Fusion for 3D Object Detection

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Li, Yanwei Qi, Xiaojuan Chen, Yukang Wang, Liwei Li, Zeming Sun, Jian Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China Univ Hong Kong Hong Kong Peoples R China MEGVII Technol Beijing Peoples R China SmartMore Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this work, we present a conceptually simple yet effective framework for cross-modality 3D object detection, named voxel field fusion. The proposed approach aims to maintain cross-modality consistency by representing and fusing augmented image features as a ray in the voxel field. To this end, the learnable sampler is first designed to sample vital features from the image plane that are projected to the voxel grid in a point-to-ray manner, which maintains the consistency in feature representation with spatial context. In addition, ray-wise fusion is conducted to fuse features with the supplemental context in the constructed voxel field. We further develop mixed augmentor to align feature-variant transformations, which bridges the modality gap in data augmentation. The proposed framework is demonstrated to achieve consistent gains in various benchmarks and outperforms previous fusion-based methods on KITTI and nuScenes datasets. Code is made available at https://***/dvlab-research/VFF.(1)

关键词： Bridges computer vision Three-dimensional displays Codes Fuses Computational modeling Object detection

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Sketching without Worrying: Noise-Tolerant Sketch-Based Image Retrieval

Sketching without Worrying: Noise-Tolerant Sketch-Based Imag...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Bhunia, Ayan Kumar Koley, Subhadeep Khilji, Abdullah Faiz Ur Rahman Sain, Aneeshan Chowdhury, Pinaki Nath Xiang, Tao Song, Yi-Zhe Univ Surrey SketchX CVSSP Guildford Surrey England iFlyTek Surrey Joint Res Ctr Artificial Intellige Hefei Anhui Peoples R China SketchX Guildford Surrey England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Sketching enables many exciting applications, notably, image retrieval. The fear-to-sketch problem (i.e., "I can't sketch") has however proven to be fatal for its widespread adoption. This paper tackles this "fear" head on, and for the first time, proposes an auxiliary module for existing retrieval models that predominantly lets the users sketch without having to worry. We first conducted a pilot study that revealed the secret lies in the existence of noisy strokes, but not so much of the "I can't sketch". We consequently design a stroke subset selector that detects noisy strokes, leaving only those which make a positive contribution towards successful retrieval. Our Reinforcement Learning based formulation quantifies the importance of each stroke present in a given subset, based on the extent to which that stroke contributes to retrieval. When combined with pre-trained retrieval models as a pre-processing module, we achieve a significant gain of 8%40% over standard baselines and in turn report new state-of-the-art performance. Last but not least, we demonstrate the selector once trained, can also be used in a plug-and-play manner to empower various sketch applications in ways that were not previously possible.

关键词： computer vision Head Computational modeling Image retrieval Reinforcement learning Boosting pattern recognition

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DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation

DANNet: A One-Stage Domain Adaptation Network for Unsupervis...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wu, Xinyi Wu, Zhenyao Guo, Hao Ju, Lili Wang, Song Univ South Carolina Columbia SC 29208 USA Farsee2 Technol Ltd Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781665445092

Semantic segmentation of nighttime images plays an equally important role as that of daytime images in autonomous driving, but the former is much more challenging due to poor illuminations and arduous human annotations. In this paper, we propose a novel domain adaptation network (DANNet) for nighttime semantic segmentation without using labeled nighttime image data. It employs an adversarial training with a labeled daytime dataset and an unlabeled dataset that contains coarsely aligned day-night image pairs. Specifically, for the unlabeled day-night image pairs, we use the pixel-level predictions of static object categories on a daytime image as a pseudo supervision to segment its counterpart nighttime image. We further design a re-weighting strategy to handle the inaccuracy caused by misalignment between day-night image pairs and wrong predictions of daytime images, as well as boost the prediction accuracy of small objects. The proposed DANNet is the first one-stage adaptation framework for nighttime semantic segmentation, which does not train additional day-night image transfer models as a separate pre-processing stage. Extensive experiments on Dark Zurich and Nighttime Driving datasets show that our method achieves state-of-the-art performance for nighttime semantic segmentation.

关键词： Training Bridges Image segmentation computer vision Annotations Semantics Neural networks

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