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Learning Compositional Representation for 4D Captures with Neural ODE

Learning Compositional Representation for 4D Captures with N...

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

作者： Jiang, Boyan Zhang, Yinda Wei, Xingkui Xue, Xiangyang Fu, Yanwei Fudan Univ Sch Comp Sci Shanghai Peoples R China Fudan Univ Shanghai Peoples R China Google Mountain View CA 94043 USA Fudan Univ Sch Data Sci MOE Frontiers Ctr Brain Sci Shanghai Peoples R China Fudan Univ Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China

ISBN: (纸本)9781665445092

Learning based representation has become the key to the success of many computer vision systems. While many 3D representations have been proposed, it is still an unaddressed problem how to represent a dynamically changing 3D object. In this paper, we introduce a compositional representation for 4D captures, i.e. a deforming 3D object over a temporal span, that disentangles shape, initial state, and motion respectively. Each component is represented by a latent code via a trained encoder. To model the motion, a neural Ordinary Differential Equation (ODE) is trained to update the initial state conditioned on the learned motion code, and a decoder takes the shape code and the updated state code to reconstruct the 3D model at each time stamp. lb this end, we propose an Identity Exchange Training (IET) strategy to encourage the network to learn effectively decoupling each component. Extensive experiments demonstrate that the proposed method outperforms existing state-of-the-art deep learning based methods on 4D reconstruction, and significantly improves on various tasks, including motion transfer and completion.

关键词： Training Geometry Solid modeling computer vision Three-dimensional displays Codes Shape

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Pseudo-IoU: Improving Label Assignment in Anchor-Free Object Detection

Pseudo-IoU: Improving Label Assignment in Anchor-Free Object...

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

作者： Li, Jiachen Cheng, Bowen Feris, Rogerio Xiong, Jinjun Huang, Thomas S. Hwu, Wen-Mei Shi, Humphrey UIUC Champaign IL 61820 USA MIT IBM Watson AI Lab Cambridge MA USA IBM TJ Watson Res Ctr Ossining NY USA NVIDIA Santa Clara CA USA Univ Oregon Eugene OR 97403 USA Picsart AI Res PAIR Champaign IL USA

ISBN: (纸本)9781665448994

Current anchor-free object detectors are quite simple and effective yet lack accurate label assignment methods, which limits their potential in competing with classic anchor-based models that are supported by well-designed assignment methods based on the Intersection-over-Union (IoU) metric. In this paper, we present Pseudo-Intersection-over-Union (Pseudo-IoU): a simple metric that brings more standardized and accurate assignment rule into anchor-free object detection frameworks without any additional computational cost or extra parameters for training and testing, making it possible to further improve anchor-free object detection by utilizing training samples of good quality under effective assignment rules that have been previously applied in anchor-based methods. By incorporating Pseudo-IoU metric into an end-to-end single-stage anchor-free object detection framework, we observe consistent improvements in their performance on general object detection benchmarks such as PASCAL VOC and MSCOCO. Our method (single-model and single-scale) also achieves comparable performance to other recent state-of-the-art anchor-free methods without bells and whistles. Our code is based on mmdetection toolbox and will be made publicly available at https://***/SHILabs/Pseudo-IoU-for-Anchor-Free-Object-Detection.

关键词： Measurement Training Location awareness computer vision Computational modeling conferences Object detection

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Memory Oriented Transfer Learning for Semi-Supervised Image Deraining

Memory Oriented Transfer Learning for Semi-Supervised Image ...

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

作者： Huang, Huaibo Yu, Aijing He, Ran Univ Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Ctr Res Intelligent Percept & Comp Natl Lab Pattern RecognitCASIACAS Beijing Peoples R China

ISBN: (纸本)9781665445092

Deep learning based methods have shown dramatic improvements in image rain removal by using large-scale paired data of synthetic datasets. However, due to the various appearances of real rain streaks that may differ from those in the synthetic training data, it is challenging to directly extend existing methods to the real-world scenes. To address this issue, we propose a memory-oriented semi-supervised (MOSS) method which enables the network to explore and exploit the properties of rain streaks from both synthetic and real data. The key aspect of our method is designing an encoder-decoder neural network that is augmented with a self-supervised memory module, where items in the memory record the prototypical patterns of rain degradations and are updated in a self-supervised way. Consequently, the rainy styles can be comprehensively derived from synthetic or real-world degraded images with- out the need for clean labels. Furthermore, we present a self-training mechanism that attempts to transfer deraining knowledge from supervised rain removal to unsupervised cases. An additional target network, which is updated with an exponential moving average of the online deraining network, is utilized to produce pseudo-labels for unlabeled rainy images. Meanwhile, the deraining network is optimized with supervised objectives on both synthetic paired data and pseudo-paired noisy data. Extensive experiments show that the proposed method achieves more appealing results not only on limited labeled data but also on unlabeled real-world images than recent state-of-the-art methods.

关键词： Degradation Rain Transfer learning Neural networks Training data Memory modules pattern recognition

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One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking

One-Shot Neural Ensemble Architecture Search by Diversity-Gu...

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

作者： Chen, Minghao Fu, Jianlong Ling, Haibin SUNY Stony Brook Stony Brook NY 11794 USA Microsoft Res Asia Beijing Peoples R China Microsoft Beijing Peoples R China

ISBN: (纸本)9781665445092

Despite remarkable progress achieved, most neural architecture search (NAS) methods focus on searching for one single accurate and robust architecture. To further build models with better generalization capability and performance, model ensemble is usually adopted and performs better than stand-alone models. Inspired by the merits of model ensemble, we propose to search for multiple diverse models simultaneously as an alternative way to find powerful models. Searching for ensembles is non-trivial and has two key challenges: enlarged search space and potentially more complexity for the searched model. In this paper, we propose a one-shot neural ensemble architecture search (NEAS) solution that addresses the two challenges. For the first challenge, we introduce a novel diversity-based metric to guide search space shrinking, considering both the potentiality and diversity of candidate operators. For the second challenge, we enable a new search dimension to learn layer sharing among different models for efficiency purposes. The experiments on ImageNet clearly demonstrate that our solution can improve the supernet's capacity of ranking ensemble architectures, and further lead to better search results. The discovered architectures achieve superior performance compared with state-of-the-arts such as MobileNetV3 and EfficientNet families under aligned settings. Moreover, we evaluate the generalization ability and robustness of our searched architecture on the COCO detection benchmark and achieve a 3.1% improvement on AP compared with MobileNetV3. Codes and models are available here.

关键词： computer vision Codes computer architecture Benchmark testing Extraterrestrial measurements Robustness Complexity theory

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(ASNA) An Attention-based Siamese-Difference Neural Network with Surrogate Ranking Loss function for Perceptual Image Quality Assessment

(ASNA) An Attention-based Siamese-Difference Neural Network ...

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

作者： Ayyoubzadeh, Seyed Mehdi Royat, Ali McMaster Univ Hamilton ON Canada Sharif Univ Technol Tehran Iran

ISBN: (纸本)9781665448994

Recently, deep convolutional neural networks (DCNN) that leverage the adversarial training framework for image restoration and enhancement have significantly improved the processed images' sharpness. Surprisingly, although these DCNNs produced crispier images than other methods visually, they may get a lower quality score when popular measures are employed for evaluating them. Therefore it is necessary to develop a quantitative metric to reflect their performances, which is well-aligned with the perceived quality of an image. Famous quantitative metrics such as Peak signal-to-noise ratio (PSNR), The structural similarity index measure (SSIM), and Perceptual Index (PI) are not well-correlated with the mean opinion score (MOS) for an image, especially for the neural networks trained with adversarial loss functions. This paper has proposed a convolutional neural network using an extension architecture of the traditional Siamese network so-called Siamese-Difference neural network. We have equipped this architecture with the spatial and channel-wise attention mechanism to increase our method's performance. Finally, we employed an auxiliary loss function to train our model. The suggested additional cost function surrogates ranking loss to increase Spearman's rank correlation coefficient while it is differentiable concerning the neural network parameters. Our method achieved superior performance in NTIRE 2021 Perceptual Image Quality Assessment Challenge. The implementations of our proposed method are publicly available.(1 2)

关键词： Image quality Training Visualization Technological innovation PSNR Neural networks computer architecture

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Training Generative Adversarial Networks in One Stage

Training Generative Adversarial Networks in One Stage

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

作者： Shen, Chengchao Yin, Youtan Wang, Xinchao Li, Xubin Song, Jie Song, Mingli Zhejiang Univ Hangzhou Peoples R China Natl Univ Singapore Singapore Singapore Alibaba Grp Hangzhou Peoples R China Zhejiang Lab Hangzhou Peoples R China Stevens Inst Technol Hoboken NJ 07030 USA

ISBN: (纸本)9781665445092

Generative Adversarial Networks (GANs) have demonstrated unprecedented success in various image generation tasks. The encouraging results, however, come at the price of a cumbersome training process, during which the generator and discriminator are alternately updated in two stages. In this paper, we investigate a general training scheme that enables training GANs efficiently in only one stage. Based on the adversarial losses of the generator and discriminator, we categorize GANs into two classes, Symmetric GANs and Asymmetric GANs, and introduce a novel gradient decomposition method to unify the two, allowing us to train both classes in one stage and hence alleviate the training effort. We also computationally analyze the efficiency of the proposed method, and empirically demonstrate that, the proposed method yields a solid 1.5x acceleration across various datasets and network architectures. Furthermore, we show that the proposed method is readily applicable to other adversarial-training scenarios, such as data-free knowledge distillation. The code is available at https://***/zju-vipa/OSGAN.

关键词： Training computer vision Image synthesis Network architecture Generative adversarial networks Solids Generators

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Uncertainty-Aware Camera Pose Estimation from Points and Lines

Uncertainty-Aware Camera Pose Estimation from Points and Lin...

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

作者： Vakhitov, Alexander Ferraz Colomina, Luis Agudo, Antonio Moreno-Noguer, Francesc SLAMCore Ltd London England Kognia Sports Intelligence Barcelona Spain CSIC UPC Inst Robot & Informat Ind Barcelona Spain

ISBN: (纸本)9781665445092

Perspective-n-Point-and-Line (PnPL) algorithms aim at fast, accurate, and robust camera localization with respect to a 3D model from 2D-3D feature correspondences, being a major part of modern robotic and AR/VR systems. Current point-based pose estimation methods use only 2D feature detection uncertainties, and the line-based methods do not take uncertainties into account. In our setup, both 3D coordinates and 2D projections of the features are considered uncertain. We propose PnP(L) solvers based on EPnP [20] and DLS [14] for the uncertainty-aware pose estimation. We also modify motion-only bundle adjustment to take 3D uncertainties into account. We perform exhaustive synthetic and real experiments on two different visual odometry datasets. The new PnP(L) methods outperform the state-of-the-art on real data in isolation, showing an increase in mean translation accuracy by 18% on a representative subset of KITTI, while the new uncertain refinement improves pose accuracy for most of the solvers, e.g. decreasing mean translation error for the EPnP by 16% compared to the standard refinement on the same dataset. The code is available at https://***/uncertain-pnp/.

关键词： Solid modeling Uncertainty Feature detection Pose estimation Robot vision systems Cameras Robustness

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CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching

CFNet: Cascade and Fused Cost Volume for Robust Stereo Match...

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

作者： Shen, Zhelun Dai, Yuchao Rao, Zhibo Northwestern Polytech Univ Xian Peoples R China Peking Univ Beijing Peoples R China

ISBN: (纸本)9781665445092

Recently, the ever-increasing capacity of large-scale annotated datasets has led to profound progress in stereo matching. However, most of these successes are limited to a specific dataset and cannot generalize well to other datasets. The main difficulties lie in the large domain differences and unbalanced disparity distribution across a variety of datasets, which greatly limit the real-world applicability of current deep stereo matching models. In this paper, we propose CFNet, a Cascade and Fused cost volume based network to improve the robustness of the stereo matching network. First, we propose a fused cost volume representation to deal with the large domain difference. By fusing multiple low-resolution dense cost volumes to enlarge the receptive field, we can extract robust structural representations for initial disparity estimation. Second, we propose a cascade cost volume representation to alleviate the unbalanced disparity distribution. Specifically, we employ a variance-based uncertainty estimation to adaptively adjust the next stage disparity search space, in this way driving the network progressively prune out the space of unlikely correspondences. By iteratively narrowing down the disparity search space and improving the cost volume resolution, the disparity estimation is gradually refined in a coarse-to-fine manner. When trained on the same training images and evaluated on KITTI, ETH3D, and Middlebury datasets with the fixed model parameters and hyperparameters, our proposed method achieves the state-of-the-art overall performance and obtains the 1st place on the stereo task of Robust vision Challenge 2020.

关键词： Training computer vision Costs Uncertainty Image resolution Codes Estimation

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Curriculum Learning for Data-Efficient vision-Language Alignment

Curriculum Learning for Data-Efficient Vision-Language Align...

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

作者： Tejas Srinivasan Xiang Ren Jesse Thomason University of Southern California

Aligning image and text encoders from scratch using contrastive learning requires large amounts of paired image-text data. We alleviate this need by aligning individually pre-trained language and vision representation models using a much smaller amount of paired data with a curriculum learning algorithm to learn fine-grained vision-language alignments. TOnICS (Training with Ontology-Informed Contrastive Sampling) initially samples minibatches whose image-text pairs contain a wide variety of objects to learn object-level vision-language alignment, and progressively samples minibatches where all image-text pairs contain the same object to learn finer-grained contextual alignment. Aligning pre-trained BERT and VinVL-OD models to each other using TOnICS outperforms CLIP on downstream zero-shot image retrieval using < 1% as much training data.

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Region-Adaptive Deformable Network for Image Quality Assessment

Region-Adaptive Deformable Network for Image Quality Assessm...

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

作者： Shi, Shuwei Bai, Qingyan Cao, Mingdeng Xia, Weihao Wang, Jiahao Chen, Yifan Yang, Yujiu Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9781665448994

Image quality assessment (IQA) aims to assess the perceptual quality of images. The outputs of the IQA algorithms are expected to be consistent with human subjective perception. In image restoration and enhancement tasks, images generated by generative adversarial networks (GAN) can achieve better visual performance than traditional CNN-generated images, although they have spatial shift and texture noise. Unfortunately, the existing IQA methods have unsatisfactory performance on the GAN-based distortion partially because of their low tolerance to spatial misalignment. To this end, we propose the reference-oriented deformable convolution, which can improve the performance of an IQA network on GAN-based distortion by adaptively considering this misalignment. We further propose a patch-level attention module to enhance the interaction among different patch regions, which are processed independently in previous patch-based methods. The modified residual block is also proposed by applying modifications to the classic residual block to construct a patch-region-based baseline called WResNet. Equipping this baseline with the two proposed modules, we further propose Region-Adaptive Deformable Network (RADN). The experiment results on the NTIRE 2021 Perceptual Image Quality Assessment Challenge dataset show the superior performance of RADN, and the ensemble approach won fourth place in the final testing phase of the challenge.

关键词： Image quality Visualization Convolution conferences Generative adversarial networks Distortion pattern recognition

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