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Positional Encoding as Spatial Inductive Bias in GANs

Positional Encoding as Spatial Inductive Bias in GANs

引用

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

作者： Xu, Rui Wang, Xintao Chen, Kai Zhou, Bolei Loy, Chen Change Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China Nanyang Technol Univ S Lab Singapore Singapore Tencent PCG Appl Res Ctr Shenzhen Peoples R China SenseTime Res Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9781665445092

SinGAN shows impressive capability in learning internal patch distribution despite its limited effective receptive field. We are interested in knowing how such a translation-invariant convolutional generator could capture the global structure with just a spatially i.i.d. input. In this work, taking SinGAN and StyleGAN2 as examples, we show that such capability, to a large extent, is brought by the implicit positional encoding when using zero padding in the generators. Such positional encoding is indispensable for generating images with high fidelity. The same phenomenon is observed in other generative architectures such as DCGAN and PGGAN. We further show that zero padding leads to an unbalanced spatial bias with a vague relation between locations. To offer a better spatial inductive bias, we investigate alternative positional encodings and analyze their effects. Based on a more flexible positional encoding explicitly, we propose a new multi-scale training strategy and demonstrate its effectiveness in the state-of-the-art unconditional generator StyleGAN2. Besides, the explicit spatial inductive bias substantially improves SinGAN for more versatile image manipulation.

关键词： Training computer vision Image coding Image synthesis computer architecture Generators Encoding

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Multi-Stage Progressive Image Restoration

Multi-Stage Progressive Image Restoration

引用

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

作者： Zamir, Syed Waqas Arora, Aditya Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Yang, Ming-Hsuan Shao, Ling Incept Inst AI Abu Dhabi U Arab Emirates Mohamed bin Zayed Univ AI Abu Dhabi U Arab Emirates Monash Univ Clayton Vic Australia Univ Calif Merced Merced CA USA Yonsei Univ Seoul South Korea Google Res Mountain View CA USA

ISBN: (纸本)9781665445092

Image restoration tasks demand a complex balance between spatial details and high-level contextualized information while recovering images. In this paper, we propose a novel synergistic design that can optimally balance these competing goals. Our main proposal is a multi-stage architecture, that progressively learns restoration functions for the degraded inputs, thereby breaking down the overall recovery process into more manageable steps. Specifically, our model first learns the contextualized features using encoder-decoder architectures and later combines them with a high-resolution branch that retains local information. At each stage, we introduce a novel per-pixel adaptive design that leverages in-situ supervised attention to reweight the local features. A key ingredient in such a multi-stage architecture is the information exchange between different stages. To this end, we propose a twofaceted approach where the information is not only exchanged sequentially from early to late stages, but lateral connections between feature processing blocks also exist to avoid any loss of information. The resulting tightly interlinked multi-stage architecture, named as MPRNet, delivers strong performance gains on ten datasets across a range of tasks including image deraining, deblurring, and denoising. The source code and pre-trained models are available at https://***/swz30/MPRNet.

关键词： Runtime Computational modeling Noise reduction computer architecture Performance gain Image restoration pattern recognition

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Robust Instance Segmentation through Reasoning about Multi-Object Occlusion

Robust Instance Segmentation through Reasoning about Multi-O...

引用

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

作者： Yuan, Xiaoding Kortylewski, Adam Sun, Yihong Yuille, Alan Tongji Univ Shanghai Peoples R China Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9781665445092

Analyzing complex scenes with Deep Neural Networks is a challenging task, particularly when images contain multiple objects that partially occlude each other. Existing approaches to image analysis mostly process objects independently and do not take into account the relative occlusion of nearby objects. In this paper, we propose a deep network for multi-object instance segmentation that is robust to occlusion and can be trained from bounding box supervision only. Our work builds on Compositional Networks, which learn a generative model of neural feature activations to locate occluders and to classify objects based on their non-occluded parts. We extend their generative model to include multiple objects and introduce a framework for efficient inference in challenging occlusion scenarios. In particular, we obtain feed-forward predictions of the object classes and their instance and occluder segmentations. We introduce an Occlusion Reasoning Module (ORM) that locates erroneous segmentations and estimates the occlusion order to correct them. The improved segmentation masks are, in turn, integrated into the network in a top-down manner to improve the image classification. Our experiments on the KITTI INStance dataset (KINS) and a synthetic occlusion dataset demonstrate the effectiveness and robustness of our model at multi-object instance segmentation under occlusion. Code is publically available at https://***/XD7479/Multi-Object-Occlusion.

关键词： Deep learning Image segmentation computer vision Codes computer architecture Cognition Robustness

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Coreset Selection for Object Detection

Coreset Selection for Object Detection

引用

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

作者： Hojun Lee Suyoung Kim Junhoo Lee Jaeyoung Yoo Nojun Kwak Seoul National University NAVER WEBTOON AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Coreset selection is a method for selecting a small, representative subset of an entire dataset. It has been primarily researched in image classification, assuming there is only one object per image. However, coreset selection for object detection is more challenging as an image can contain multiple objects. As a result, much research has yet to be done on this topic. Therefore, we introduce a new approach, Coreset Selection for Object Detection (CSOD). CSOD generates imagewise and classwise representative feature vectors for multiple objects of the same class within each image. Subsequently, we adopt submodular optimization for considering both representativeness and diversity and utilize the representative vectors in the submodular optimization process to select a subset. When we evaluated CSOD on the Pascal VOC dataset, CSOD outperformed random selection by +6.4%p in AP 50 when selecting 200 images.

关键词： computer vision conferences Object detection Vectors pattern recognition Optimization Image classification

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Unsupervised Multi-source Domain Adaptation Without Access to Source Data

Unsupervised Multi-source Domain Adaptation Without Access t...

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

作者： Ahmed, Sk Miraj Raychaudhuri, Dripta S. Paul, Sujoy Oymak, Samet Roy-Chowdhury, Amit K. Univ Calif Riverside Riverside CA 92521 USA Google Res Mountain View CA USA UC Riverside Riverside CA USA

ISBN: (纸本)9781665445092

Unsupervised Domain Adaptation (UDA) aims to learn a predictor model for an unlabeled domain by transferring knowledge from a separate labeled source domain. However, most of these conventional UDA approaches make the strong assumption of having access to the source data during training, which may not be very practical due to privacy, security and storage concerns. A recent line of work addressed this problem and proposed an algorithm that transfers knowledge to the unlabeled target domain from a single source model without requiring access to the source data. However, for adaptation purposes, if there are multiple trained source models available to choose from, this method has to go through adapting each and every model individually, to check for the best source. Thus, we ask the question: can we find the optimal combination of source models, with no source data and without target labels, whose performance is no worse than the single best source? To answer this, we propose a novel and efficient algorithm which automatically combines the source models with suitable weights in such a way that it performs at least as good as the best source model. We provide intuitive theoretical insights to justify our claim. Furthermore, extensive experiments are conducted on several benchmark datasets to show the effectiveness of our algorithm, where in most cases, our method not only reaches best source accuracy but also outperforms it.

关键词： Training Adaptation models Data privacy computer vision Predictive models Benchmark testing Data models

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ChaLearn LAP Large Scale Signer Independent Isolated Sign Language recognition Challenge: Design, Results and Future Research

ChaLearn LAP Large Scale Signer Independent Isolated Sign La...

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

作者： Sincan, Ozge Mercanoglu Jacques Junior, Julio C. S. Escalera, Sergio Keles, Hacer Yalim Ankara Univ Ankara Turkey Comp Vis Ctr Barcelona Spain Univ Barcelona Barcelona Spain

ISBN: (纸本)9781665448994

The performances of Sign Language recognition (SLR) systems have improved considerably in recent years. However, several open challenges still need to be solved to allow SLR to be useful in practice. The research in the field is in its infancy in regards to the robustness of the models to a large diversity of signs and signers, and to fairness of the models to performers from different demographics. This work summarises the ChaLearn LAP Large Scale Signer Independent Isolated SLR Challenge, organised at CVPR 2021 with the goal of overcoming some of the aforementioned challenges. We analyse and discuss the challenge design, top winning solutions and suggestions for future research. The challenge attracted 132 participants in the RGB track and 59 in the RGB+Depth track, receiving more than 1.5K submissions in total. Participants were evaluated using a new large-scale multi-modal Turkish Sign Language (AUTSL) dataset, consisting of 226 sign labels and 36,302 isolated sign video samples performed by 43 different signers. Winning teams achieved more than 96% recognition rate, and their approaches benefited from pose/hand/face estimation, transfer learning, external data, fusion/ensemble of modalities and different strategies to model spatio-temporal information. However, methods still fail to distinguish among very similar signs, in particular those sharing similar hand trajectories.

关键词： computer vision Tracking Assistive technology Computational modeling Transfer learning Semantics Gesture recognition

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LCCNet: LiDAR and Camera Self-Calibration using Cost Volume Network

LCCNet: LiDAR and Camera Self-Calibration using Cost Volume ...

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

作者： Lv, Xudong Wang, Boya Dou, Ziwen Ye, Dong Wang, Shuo Harbin Inst Technol Sch Instrumentat Sci & Engn Harbin Peoples R China

ISBN: (纸本)9781665448994

Multi-sensor fusion is for enhancing environment perception and 3D reconstruction in self-driving and robot navigation. Calibration between sensors is the precondition of effective multi-sensor fusion. Laborious manual works and complex environment settings exist in old-fashioned calibration techniques for Light Detection and Ranging (LiDAR) and camera. We propose an online LiDAR-Camera Self-calibration Network (LCCNet), different from the previous CNN-based methods. LCCNet can be trained end-to-end and predict the extrinsic parameters in real-time. In the LCCNet, we exploit the cost volume layer to express the correlation between the RGB image features and the depth image projected from point clouds. Besides using the smooth L1-Loss of the predicted extrinsic calibration parameters as a supervised signal, an additional self-supervised signal, point cloud distance loss, is applied during training. Instead of directly regressing the extrinsic parameters, we predict the decalibrated deviation from initial calibration to the ground truth. The calibration error decreases further with iterative refinement and the temporal filtering approach in the inference stage. The execution time of the calibration process is 24ms for each iteration on a single GPU. LCCNet achieves a mean absolute calibration error of 0.297cm in translation and 0.017. in rotation with miscalibration magnitudes of up to +/- 1.5m and +/- 20. on the KITTI-odometry dataset, which is better than the state-of-the-art CNN-based calibration methods. The code will be publicly available at https://***/LvXudong-HIT/LCCNet

关键词： Training Three-dimensional displays Laser radar Robot vision systems Sensor fusion Cameras Feature extraction

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Learning Calibrated Medical Image Segmentation via Multi-rater Agreement Modeling

Learning Calibrated Medical Image Segmentation via Multi-rat...

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

作者： Ji, Wei Yu, Shuang Wu, Junde Ma, Kai Bian, Cheng Bi, Qi Li, Jingjing Liu, Hanruo Cheng, Li Zheng, Yefeng Tencent Jarvis Lab Shenzhen Peoples R China Univ Alberta Edmonton AB Canada Capital Med Univ Beijing Tongren Hosp Beijing Peoples R China

ISBN: (纸本)9781665445092

In medical image analysis, it is typical to collect multiple annotations, each from a different clinical expert or rater, in the expectation that possible diagnostic errors could be mitigated. Meanwhile, from the computer vision practitioner viewpoint, it has been a common practice to adopt the ground-truth labels obtained via either the majorityvote or simply one annotation from a preferred rater. This process, however, tends to overlook the rich information of agreement or disagreement ingrained in the raw multirater annotations. To address this issue, we propose to explicitly model the multi-rater (dis-)agreement, dubbed MRNet, which has two main contributions. First, an expertise-aware inferring module or EIM is devised to embed the expertise level of individual raters as prior knowledge, to form high-level semantic features. Second, our approach is capable of reconstructing multi-rater gradings from coarse predictions, with the multi-rater (dis-)agreement cues being further exploited to improve the segmentation performance. To our knowledge, our work is the first in producing calibrated predictions under different expertise levels for medical image segmentation. Extensive empirical experiments are conducted across five medical segmentation tasks of diverse imaging modalities. In these experiments, superior performance of our MRNet is observed comparing to the state-of-the-arts, indicating the effectiveness and applicability of our MRNet toward a wide range of medical segmentation tasks.

关键词： Image segmentation computer vision Image analysis Annotations Semantics Predictive models pattern recognition

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Diverse Branch Block: Building a Convolution as an Inception-like Unit

Diverse Branch Block: Building a Convolution as an Inception...

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

作者： Ding, Xiaohan Zhang, Xiangyu Han, Jungong Ding, Guiguang Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China Tsinghua Univ Sch Software Beijing Peoples R China MEGVII Technol Beijing Peoples R China Aberystwyth Univ Comp Sci Dept Aberystwyth SY23 3FL Dyfed Wales MEGVII Beijing Peoples R China

ISBN: (纸本)9781665445092

We propose a universal building block of Convolutional Neural Network (ConvNet) to improve the performance without any inference-time costs. The block is named Diverse Branch Block (DBB), which enhances the representational capacity of a single convolution by combining diverse branches of different scales and complexities to enrich the feature space, including sequences of convolutions, multi-scale convolutions, and average pooling. After training, a DBB can be equivalently converted into a single conv layer for deployment. Unlike the advancements of novel ConvNet architectures, DBB complicates the training-time microstructure while maintaining the macro architecture, so that it can be used as a drop-in replacement for regular conv layers of any architecture. In this way, the model can be trained to reach a higher level of performance and then transformed into the original inference-time structure for inference. DBB improves ConvNets on image classification (up to 1.9% higher top-1 accuracy on ImageNet), object detection and semantic segmentation. The PyTorch code and models are released at https:// ***/ DingXiaoH/DiverseBranchBlock.

关键词： Training Image segmentation Costs Convolution Semantics computer architecture Object detection

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UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles

UAV-Human: A Large Benchmark for Human Behavior Understandin...

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

作者： Li, Tianjiao Liu, Jun Zhang, Wei Ni, Yun Wang, Wenqian Li, Zhiheng Singapore Univ Technol & Design Informat Syst Technol & Design Singapore Singapore Shandong Univ Sch Control Sci & Engn Jinan Shandong Peoples R China

ISBN: (纸本)9781665445092

Human behavior understanding with unmanned aerial vehicles (UAVs) is of great significance for a wide range of applications, which simultaneously brings an urgent demand of large, challenging, and comprehensive benchmarks for the development and evaluation of UAV-based models. However, existing benchmarks have limitations in terms of the amount of captured data, types of data modalities, categories of provided tasks, and diversities of subjects and environments. Here we propose a new benchmark - UAV-Human - for human behavior understanding with UAVs, which contains 67,428 multi-modal video sequences and 119 subjects for action recognition, 22,476 frames for pose estimation, 41,290 frames and 1,144 identities for person re-identification, and 22,263 frames for attribute recognition. Our dataset was collected by a flying UAV in multiple urban and rural districts in both daytime and night-time over three months, hence covering extensive diversities w.r.t subjects, backgrounds, illuminations, weathers, occlusions, camera motions, and UAV flying attitudes. Such a comprehensive and challenging benchmark shall be able to promote the research of UAV-based human behavior understanding, including action recognition, pose estimation, re-identification, and attribute recognition. Furthermore, we propose a fisheye-based action recognition method that mitigates the distortions in fisheye videos via learning unbounded transformations guided by flat RGB videos. Experiments show the efficacy of our method on the UAV-Human dataset.

关键词： computer vision Pose estimation Video sequences Lighting Benchmark testing Distortion Unmanned aerial vehicles

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