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View Generalization for Single Image Textured 3D Models

View Generalization for Single Image Textured 3D Models

引用

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

作者： Bhattad, Anand Dundar, Aysegul Liu, Guilin Tao, Andrew Catanzaro, Bryan Univ Illinois Urbana IL 61801 USA Bilkent Univ Ankara Turkey NVIDIA Santa Clara CA USA

ISBN: (纸本)9781665445092

Humans can easily infer the underlying 3D geometry and texture of an object only from a single 2D image. Current computer vision methods can do this, too, but suffer from view generalization problems - the models inferred tend to make poor predictions of appearance in novel views. As for generalization problems in machine learning, the difficulty is balancing single-view accuracy (cf training error;bias) with novel view accuracy (cf test error;variance). We describe a class of models whose geometric rigidity is easily controlled to manage this tradeoff. We describe a cycle consistency loss that improves view generalization (roughly, a model from a generated view should predict the original view well). View generalization of textures requires that models share texture information, so a car seen from the back still has headlights because other cars have headlights. We describe a cycle consistency loss that encourages model textures to be aligned, so as to encourage sharing. We compare our method against the state-of-the-art method and show both qualitative and quantitative improvements.

关键词： Training Solid modeling computer vision Adaptation models Three-dimensional displays Shape Predictive models

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Single Image Reflection Removal with Absorption Effect

Single Image Reflection Removal with Absorption Effect

引用

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

作者： Zheng, Qian Shi, Boxin Chen, Jinnan Jiang, Xudong Duan, Ling-Yu Kot, Alex C. Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Peking Univ Dept Comp Sci & Technol NELVT Beijing Peoples R China Peking Univ Inst Artificial Intelligence Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

In this paper, we consider the absorption effect for the problem of single image reflection removal. We show that the absorption effect can be numerically approximated by the average of refractive amplitude coefficient map. We then reformulate the image formation model and propose a two-step solution that explicitly takes the absorption effect into account. The first step estimates the absorption effect from a reflection-contaminated image, while the second step recovers the transmission image by taking a reflection-contaminated image and the estimated absorption effect as the input. Experimental results on four public datasets show that our two-step solution not only successfully removes reflection artifact, but also faithfully restores the intensity distortion caused by the absorption effect. Our ablation studies further demonstrate that our method achieves superior performance on the recovery of overall intensity and has good model generalization capacity. The code is available at https://***/q-zh/absorption.

关键词： computer vision Absorption Lighting Estimation Distortion Reflection Data models

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Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation

Improving Weakly Supervised Visual Grounding by Contrastive ...

引用

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

作者： Wang, Liwei Huang, Jing Li, Yin Xu, Kun Yang, Zhengyuan Yu, Dong Chinese Univ Hong Kong Hong Kong Peoples R China Univ Illinois Champaign IL USA Univ Wisconsin Madison Madison WI USA Tencent AI Lab Bellevue WA USA Univ Rochester Rochester NY 14627 USA

ISBN: (纸本)9781665445092

Weakly supervised phrase grounding aims at learning region-phrase correspondences using only image-sentence pairs. A major challenge thus lies in the missing links between image regions and sentence phrases during training. To address this challenge, we leverage a generic object detector at training time, and propose a contrastive learning framework that accounts for both region-phrase and image-sentence matching. Our core innovation is the learning of a region-phrase score function, based on which an image-sentence score function is further constructed. Importantly, our region-phrase score function is learned by distilling from soft matching scores between the detected object names and candidate phrases within an image-sentence pair, while the image-sentence score function is supervised by ground-truth image-sentence pairs. The design of such score functions removes the need of object detection at test time, thereby significantly reducing the inference cost. Without bells and whistles, our approach achieves state-of-the-art results on visual phrase grounding, surpassing previous methods that require expensive object detectors at test time.

关键词： Training Visualization Technological innovation computer vision Costs Grounding Detectors

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Understanding ReLU Network Robustness Through Test Set Certification Performance

Understanding ReLU Network Robustness Through Test Set Certi...

引用

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

作者： Nicola Franco Jeanette Miriam Lorenz Karsten Roscher Stephan Günnemann Fraunhofer Institute for Cognitive Systems IKS Munich Germany Dept. of Computer Science & Munich Data Science Institute Technical Univ. of Munich Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural networks can be vulnerable to small changes in input within their learning distribution, and this vulnerability increases for distributional shifts or input completely outside their training distribution. To ensure networks are used safely, robustness certificates offer formal assurances about the stability of their predictions in a pre-defined range around the input. However, the relationship between correctness and certified robustness remains unclear. In this work, we investigate the unexpected outcomes of verification methods applied to piecewise linear classifiers for clean, perturbed, in- and out-of-distribution samples. In our experiments focused on image classification, we observed that introducing a modest stability margin around the input sample leads to an important reduction in misclassified samples — approximately a 75% decrease — compared to the roughly 11% for samples that are correctly classified. This finding emphasizes the value of formal verification methods as an extra layer of safety, illustrating their effectiveness in enhancing accuracy for data that falls within the distribution. On the other hand, we provide a theoretical demonstration that formal verification methods robustly certify samples sufficiently far from the training distribution. These results are integrated with an experimental analysis and demonstrate their limitations compared to standard out-of-distribution detection methods.

关键词： Accuracy Perturbation methods Neural networks Reliability theory Robustness Stability analysis Safety

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Improving Unsupervised Image Clustering With Robust Learning

Improving Unsupervised Image Clustering With Robust Learning

引用

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

作者： Park, Sungwon Han, Sungwon Kim, Sundong Kim, Danu Park, Sungkyu Hong, Seunghoon Cha, Meeyoung Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea Inst for Basic Sci Korea Data Sci Grp Daejeon South Korea

ISBN: (纸本)9781665445092

Unsupervised image clustering methods often introduce alternative objectives to indirectly train the model and are subject to faulty predictions and overconfident results. To overcome these challenges, the current research proposes an innovative model RUC that is inspired by robust learning. RUC's novelty is at utilizing pseudo-labels of existing image clustering models as a noisy dataset that may include misclassified samples. Its retraining process can revise misaligned knowledge and alleviate the overconfidence problem in predictions. The model's flexible structure makes it possible to be used as an add-on module to other clustering methods and helps them achieve better performance on multiple datasets. Extensive experiments show that the proposed model can adjust the model confidence with better calibration and gain additional robustness against adversarial noise.

关键词： Training computer vision Clustering methods Computational modeling Predictive models Robustness Calibration

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Transferable Semantic Augmentation for Domain Adaptation

Transferable Semantic Augmentation for Domain Adaptation

引用

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

作者： Li, Shuang Xie, Mixue Gong, Kaixiong Liu, Chi Harold Wang, Yulin Li, Wei Beijing Inst Technol Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Inceptio Tech Fremont CA USA

ISBN: (纸本)9781665445092

Domain adaptation has been widely explored by transferring the knowledge from a label-rich source domain to a related but unlabeled target domain. Most existing domain adaptation algorithms attend to adapting feature representations across two domains with the guidance of a shared source-supervised classifier. However, such classifier limits the generalization ability towards unlabeled target recognition. To remedy this, we propose a Transferable Semantic Augmentation (TSA) approach to enhance the classifier adaptation ability through implicitly generating source features towards target semantics. Specifically, TSA is inspired by the fact that deep feature transformation towards a certain direction can be represented as meaningful semantic altering in the original input space. Thus, source features can be augmented to effectively equip with target semantics to train a more transferable classifier. To achieve this, for each class, we first use the inter-domain feature mean difference and target intra-class feature covariance to construct a multivariate normal distribution. Then we augment source features with random directions sampled from the distribution class-wisely. Interestingly, such source augmentation is implicitly implemented through an expected transferable cross-entropy loss over the augmented source distribution, where an upper bound of the expected loss is derived and minimized, introducing negligible computational overhead. As a light-weight and general technique, TSA can be easily plugged into various domain adaptation methods, bringing remarkable improvements. Comprehensive experiments on cross-domain benchmarks validate the efficacy of TSA.

关键词： computer vision Upper bound Target recognition Semantics Gaussian distribution Benchmark testing pattern recognition

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PhD Learning: Learning with Pompeiu-hausdorff Distances for Video-based Vehicle Re-Identification

PhD Learning: Learning with Pompeiu-hausdorff Distances for ...

引用

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

作者： Zhao, Jianan Qi, Fengliang Ren, Guangyu Xu, Lin Shanghai Em Data Technol Co Ltd Shanghai Peoples R China Imperial Coll London London England

ISBN: (纸本)9781665445092

Vehicle re-identification (re-ID) is of great significance to urban operation, management, security and has gained more attention in recent years. However, two critical challenges in vehicle re-ID have primarily been underestimated, i.e., 1): how to make full use of raw data, and 2): how to learn a robust re-ID model with noisy data. In this paper, we first create a video vehicle re-ID evaluation benchmark called VVeRI-901 and verify the performance of video-based re-ID is far better than static image-based one. Then we propose a new Pompeiu-hausdorff distance (PhD) learning method for video-to-video matching. It can alleviate the data noise problem caused by the occlusion in videos and thus improve re-ID performance significantly. Extensive empirical results on video-based vehicle and person reID datasets, i.e., VVeRI-901, MARS and PRID2011, demonstrate the superiority of the proposed method.

关键词： Learning systems Visualization Surveillance Resists Benchmark testing Data models pattern recognition

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Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression

Bottom-Up Human Pose Estimation Via Disentangled Keypoint Re...

引用

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

作者： Geng, Zigang Sun, Ke Xiao, Bin Zhang, Zhaoxiang Wang, Jingdong Univ Sci & Technol China Beijing Peoples R China Chinese Acad Sci Univ Chinese Acad Sci Inst Automat Ctr Artificial Intelligence & RobotHKISI Beijing Peoples R China Microsoft Corp Redmond WA 98052 USA

ISBN: (纸本)9781665445092

In this paper, we are interested in the bottom-up paradigm of estimating human poses from an image. We study the dense keypoint regression framework that is previously inferior to the keypoint detection and grouping framework. Our motivation is that regressing keypoint positions accurately needs to learn representations that focus on the keypoint regions. We present a simple yet effective approach, named disentangled keypoint regression (DEKR). We adopt adaptive convolutions through pixel-wise spatial transformer to activate the pixels in the keypoint regions and accordingly learn representations from them. We use a multi-branch structure for separate regression: each branch learns a representation with dedicated adaptive convolutions and regresses one keypoint. The resulting disentangled representations are able to attend to the keypoint regions, respectively, and thus the keypoint regression is spatially more accurate. We empirically show that the proposed direct regression method outperforms keypoint detection and grouping methods and achieves superior bottom-up pose estimation results on two benchmark datasets, COCO and Crowd-Pose. The code and models are available at https: //***/HRNet/DEKR.

关键词： Convolutional codes Location awareness computer vision Pose estimation Focusing Object detection Benchmark testing

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Lite-HRNet: A Lightweight High-Resolution Network

Lite-HRNet: A Lightweight High-Resolution Network

引用

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

作者： Yu, Changqian Xiao, Bin Gao, Changxin Yuan, Lu Zhang, Lei Sang, Nong Wang, Jingdong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Huazhong Peoples R China Microsoft Redmond WA 98052 USA

ISBN: (纸本)9781665445092

We present an efficient high-resolution network, Lite-HRNet, for human pose estimation. We start by simply applying the efficient shuffle block in ShuffleNet to HRNet (high-resolution network), yielding stronger performance over popular lightweight networks, such as MobileNet, ShuffleNet, and Small HRNet. We find that the heavily-used pointwise (1 x 1) convolutions in shuffle blocks become the computational bottleneck. We introduce a lightweight unit, conditional channel weighting, to replace costly pointwise (1 x 1) convolutions in shuffle blocks. The complexity of channel weighting is linear w.r.t the number of channels and lower than the quadratic time complexity for pointwise convolutions. Our solution learns the weights from all the channels and over multiple resolutions that are readily available in the parallel branches in HRNet. It uses the weights as the bridge to exchange information across channels and resolutions, compensating the role played by the pointwise (1 x 1) convolution. Lite-HRNet demonstrates superior results on human pose estimation over popular lightweight networks. Moreover, Lite-HRNet can be easily applied to semantic segmentation task in the same lightweight manner. The code and models have been publicly available at https://***/HRNet/Lite-HRNet.

关键词： Convolutional codes Bridges computer vision Computational modeling Pose estimation Semantics pattern recognition

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AdderSR: Towards Energy Efficient Image Super-Resolution

AdderSR: Towards Energy Efficient Image Super-Resolution

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

作者： Song, Dehua Wang, Yunhe Chen, Hanting Xu, Chang Xu, Chunjing Tao, Dacheng Huawei Technol Noahs Ark Lab Shenzhen Peoples R China Peking Univ Beijing Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9781665445092

This paper studies the single image super-resolution problem using adder neural networks (AdderNets). Compared with convolutional neural networks, AdderNets utilize additions to calculate the output features thus avoid massive energy consumptions of conventional multiplications. However, it is very hard to directly inherit the existing success of AdderNets on large-scale image classification to the image super-resolution task due to the different calculation paradigm. Specifically, the adder operation cannot easily learn the identity mapping, which is essential for image processing tasks. In addition, the functionality of high-pass filters cannot be ensured by AdderNets. To this end, we thoroughly analyze the relationship between an adder operation and the identity mapping and insert shortcuts to enhance the performance of SR models using adder networks. Then, we develop a learnable power activation for adjusting the feature distribution and refining details. Experiments conducted on several benchmark models and datasets demonstrate that, our image super-resolution models using AdderNets can achieve comparable performance and visual quality to that of their CNN baselines with an about 2.5x reduction on the energy consumption. The codes are available at: https://***/huawei-noah/AdderNet.

关键词： Energy consumption Visualization Computational modeling Superresolution Refining Neural networks pattern recognition

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