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Globally Optimal Relative Pose Estimation with Gravity Prior

Globally Optimal Relative Pose Estimation with Gravity Prior

引用

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

作者： Ding, Yaqing Barath, Daniel Yang, Jian Kong, Hui Kukelova, Zuzana Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Czech Tech Univ Fac Elect Engn Visual Recognit Grp Prague Czech Republic SZTAKI MPLab Budapest Hungary Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland

ISBN: (纸本)9781665445092

Smartphones, tablets and camera systems used, e.g., in cars and UAVs, are typically equipped with IMUs (inertial measurement units) that can measure the gravity vector accurately. Using this additional information, the y-axes of the cameras can be aligned, reducing their relative orientation to a single degree-of-freedom. With this assumption, we propose a novel globally optimal solver, minimizing the algebraic error in the least squares sense, to estimate the relative pose in the over-determined case. Based on the epipolar constraint, we convert the optimization problem into solving two polynomials with only two unknowns. Also, a fast solver is proposed using the first-order approximation of the rotation. The proposed solvers are compared with the state-of-the-art ones on four real-world datasets with approx. 50000 image pairs in total. Moreover, we collected a dataset, by a smartphone, consisting of 10933 image pairs, gravity directions and ground truth 3D reconstructions.

关键词： computer vision Measurement units Three-dimensional displays Codes Pose estimation Cameras pattern recognition

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Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations

Right for the Right Concept: Revising Neuro-Symbolic Concept...

引用

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

作者： Stammer, Wolfgang Schramowski, Patrick Kersting, Kristian Tech Univ Darmstadt Comp Sci Dept Darmstadt Germany Tech Univ Darmstadt Ctr Cognit Sci Darmstadt Germany Hessian Ctr AI Hessian AI Darmstadt Germany

ISBN: (纸本)9781665445092

Most explanation methods in deep learning map importance estimates for a model's prediction back to the original input space. These "visual" explanations are often insufficient, as the model's actual concept remains elusive. Moreover, without insights into the model's semantic concept, it is difficult -if not impossible- to intervene on the model's behavior via its explanations, called Explanatory Interactive Learning. Consequently, we propose to intervene on a Neuro-Symbolic scene representation, which allows one to revise the model on the semantic level, e.g. "never focus on the color to make your decision". We compiled a novel confounded visual scene data set, the CLEVR-Hans data set, capturing complex compositions of different objects. The results of our experiments on CLEVR-Hans demonstrate that our semantic explanations, i.e. compositional explanations at a per-object level, can identify confounders that are not identifiable using "visual" explanations only. More importantly, feedback on this semantic level makes it possible to revise the model from focusing on these factors.

关键词： Training Deep learning Visualization computer vision Image color analysis Semantics Focusing

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Efficient Conditional GAN Transfer with Knowledge Propagation across Classes

Efficient Conditional GAN Transfer with Knowledge Propagatio...

引用

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

作者： Shahbazi, Mohamad Huang, Zhiwu Paudel, Danda Pani Chhatkuli, Ajad Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Katholieke Univ Leuven PSI Leuven Belgium

ISBN: (纸本)9781665445092

Generative adversarial networks (GANs) have shown impressive results in both unconditional and conditional image generation. In recent literature, it is shown that pretrained GANs, on a different dataset, can be transferred to improve the image generation from a small target data. The same, however, has not been well-studied in the case of conditional GANs (cGANs), which provides new opportunities for knowledge transfer compared to unconditional setup. In particular, the new classes may borrow knowledge from the related old classes, or share knowledge among themselves to improve the training. This motivates us to study the problem of efficient conditional GAN transfer with knowledge propagation across classes. To address this problem, we introduce a new GAN transfer method to explicitly propagate the knowledge from the old classes to the new classes. The key idea is to enforce the popularly used conditional batch normalization (BN) to learn the class-specific information of the new classes from that of the old classes, with implicit knowledge sharing among the new ones. This allows for an efficient knowledge propagation from the old classes to the new ones, with the BN parameters increasing linearly with the number of new classes. The extensive evaluation demonstrates the clear superiority of the proposed method over state-of-the-art competitors for efficient conditional GAN transfer tasks. The code is available at: https://***/mshahbazi72/cGANTransfer

关键词： Training computer vision Codes Image synthesis Generative adversarial networks pattern recognition Task analysis

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Unpaired Image-to-Image Translation via Latent Energy Transport

Unpaired Image-to-Image Translation via Latent Energy Transp...

引用

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

作者： Zhao, Yang Chen, Changyou Univ Buffalo SUNY Buffalo NY 14260 USA

ISBN: (纸本)9781665445092

Image-to-image translation aims to preserve source contents while translating to discriminative target styles between two visual domains. Most works apply adversarial learning in the ambient image space, which could be computationally expensive and challenging to train. In this paper, we propose to deploy an energy-based model (EBM) in the latent space of a pretrained autoencoder for this task. The pretrained autoencoder serves as both a latent code extractor and an image reconstruction worker. Our model, LETIT1, is based on the assumption that two domains share the same latent space, where latent representation is implicitly decomposed as a content code and a domain-specific style code. Instead of explicitly extracting the two codes and applying adaptive instance normalization to combine them, our latent EBM can implicitly learn to transport the source style code to the target style code while preserving the content code, an advantage over existing image translation methods. This simplified solution is also more efficient in the one-sided unpaired image translation setting. Qualitative and quantitative comparisons demonstrate superior translation quality and faithfulness for content preservation. Our model is the first to be applicable to 1024x1024-resolution unpaired image translation to the best of our knowledge. Code is available at https://***/YangNaruto/latentenergy-transport.

关键词： Visualization computer vision Adaptation models Codes Computational modeling Scalability Adversarial machine learning

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QPIC: Query-Based Pairwise Human-Object Interaction Detection with Image-Wide Contextual Information

QPIC: Query-Based Pairwise Human-Object Interaction Detectio...

引用

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

作者： Tamura, Masato Ohashi, Hiroki Yoshinaga, Tomoaki Hitachi Ltd Lumada Data Sci Lab Tokyo Japan Hitachi Ltd Ctr Technol Innovat Artificial Intelligence Tokyo Japan

ISBN: (纸本)9781665445092

We propose a simple, intuitive yet powerful method for human-object interaction (HOI) detection. HOIs are so diverse in spatial distribution in an image that existing CNN-based methods face the following three major drawbacks;they cannot leverage image-wide features due to CNN's locality, they rely on a manually defined location-of-interest for the feature aggregation, which sometimes does not cover contextually important regions, and they cannot help but mix up the features for multiple HOI instances if they are located closely. To overcome these drawbacks, we propose a transformer-based feature extractor, in which an attention mechanism and query-based detection play key roles. The attention mechanism is effective in aggregating contextually important information image-wide, while the queries, which we design in such a way that each query captures at most one human-object pair, can avoid mixing up the features from multiple instances. This transformer-based feature extractor produces so effective embeddings that the subsequent detection heads may be fairly simple and intuitive. The extensive analysis reveals that the proposed method successfully extracts contextually important features, and thus outperforms existing methods by large margins (5.37 mAP on HICO-DET, and 5.6 mAP on V-COCO).

关键词： computer vision Graphical models Codes Aggregates Detectors Feature extraction Transformers

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CT-Net: Complementary Transfering Network for Garment Transfer with Arbitrary Geometric Changes

CT-Net: Complementary Transfering Network for Garment Transf...

引用

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

作者： Yang, Fan Lin, Guosheng Nanyang Technol Univ S Lab Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

ISBN: (纸本)9781665445092

Garment transfer shows great potential in realistic applications with the goal of transfering outfits across different people images. However, garment transfer between images with heavy misalignments or severe occlusions still remains as a challenge. In this work, we propose Complementary Transfering Network (CT-Net) to adaptively model different levels of geometric changes and transfer outfits between different people. In specific, CT-Net consists of three modules: i) A complementary warping module first estimates two complementary warpings to transfer the desired clothes in different granularities. ii) A layout prediction module is proposed to predict the target layout, which guides the preservation or generation of the body parts in the synthesized images. iii) A dynamic fusion module adaptively combines the advantages of the complementary warpings to render the garment transfer results. Extensive experiments conducted on DeepFashion dataset demonstrate that our network synthesizes high-quality garment transfer images and significantly outperforms the state-of-art methods both qualitatively and quantitatively. Our source code will be available online.

关键词： Adaptation models computer vision Codes Computational modeling Clothing Layout pattern recognition

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Interactive Self-Training with Mean Teachers for Semi-supervised Object Detection

Interactive Self-Training with Mean Teachers for Semi-superv...

引用

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

作者： Yang, Qize Wei, Xihan Wang, Biao Hua, Xian-Sheng Zhang, Lei Alibaba Grp Hangzhou Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9781665445092

The goal of semi-supervised object detection is to learn a detection model using only a few labeled data and large amounts of unlabeled data, thereby reducing the cost of data labeling. Although a few studies have proposed various self-training-based methods or consistency regularization-based methods, they ignore the discrepancies among the detection results in the same image that occur during different training iterations. Additionally, the predicted detection results vary among different detection models. In this paper, we propose an interactive form of self-training using mean teachers for semi-supervised object detection. Specifically, to alleviate the instability among the detection results in different iterations, we propose using nonmaximum suppression to fuse the detection results from different iterations. Simultaneously, we use multiple detection heads that predict pseudo labels for each other to provide complementary information. Furthermore, to avoid different detection heads collapsing to each other, we use a mean teacher model instead of the original detection model to predict the pseudo labels. Thus, the object detection model can be trained on both labeled and unlabeled data. Extensive experimental results verify the effectiveness of our proposed method.

关键词： Training computer vision Head Costs Fuses Object detection Predictive models

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Shadow Removal via Global Residual Free Unet and Shadow Generation

Shadow Removal via Global Residual Free Unet and Shadow Gene...

引用

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

作者： Dong Li Xin Lu Yurui Zhu Xi Wang Jie Xiao Yunpeng Zhang Xueyang Fu Zheng-Jun Zha University of Science and Technology of China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Existing shadow removal methods face challenges when confronted with real-world scenes, particularly when dealing with complex background information and high-resolution images. To address these issues, we present a shadow removal framework based on shadow generation and Global Residual Free Unet (GRFUnet), which improves shadow removal from both data and network perspectives. For data enhancement, we train a generative adversarial network for producing shadow masks, which are then multiplied with clean images to obtain new shadow images. The additional shadow data produced by this structured generation approach allows for ample constraining of the network in color aspects, thereby enhancing color consistency in the images. In terms of network architecture, we design the Global Residual Free Unet that employs a convolutional framework to sequentially conduct Spatial Interaction and Channel Evolution for effective feature extraction. Moreover, we eliminate the commonly used Global residual connections in image restoration, as we discern their in-effectiveness for the non-additive task of shadow removal. Through this methodology, we achieve excellent shadow removal results, both qualitatively and quantitatively. Our method achieves the highest PSNR in the NTIRE24-Image Shadow Removal Challenge and achieves commendable and balanced outcomes across two tracks—placing third in the fidelity track and fourth in the perceptual track.

关键词： computer vision Image color analysis conferences computer architecture Network architecture Feature extraction Generative adversarial networks

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A Dual Iterative Refinement Method for Non-rigid Shape Matching

A Dual Iterative Refinement Method for Non-rigid Shape Match...

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

作者： Xiang, Rui Lai, Rongjie Zhao, Hongkai UC Dept Math Irvine CA 92697 USA Rensselaer Polytech Inst Dept Math Troy NY 12181 USA Duke Univ Dept Math Durham NC 27706 USA

ISBN: (纸本)9781665445092

In this work, a robust and efficient dual iterative refinement (DIR) method is proposed for dense correspondence between two nearly isometric shapes. The key idea is to use dual information, such as spatial and spectral, or local and global features, in a complementary and effective way, and extract more accurate information from current iteration to use for the next iteration. In each DIR iteration, starting from current correspondence, a zoom-in process at each point is used to select well matched anchor pairs by a local mapping distortion criterion. These selected anchor pairs are then used to align spectral features (or other appropriate global features) whose dimension adaptively matches the capacity of the selected anchor pairs. Thanks to the effective combination of complementary information in a data-adaptive way, DIR is not only efficient but also robust to render accurate results within a few iterations. By choosing appropriate dual features, DIR has the flexibility to handle patch and partial matching as well. Our comprehensive experiments on various data sets demonstrate the superiority of DIR over other state-of-the-art methods in terms of both accuracy and efficiency.

关键词： computer vision Image analysis Shape Stability criteria Feature extraction Distortion Iterative methods

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Patch-VQ: 'Patching Up' the Video Quality Problem

Patch-VQ: 'Patching Up' the Video Quality Problem

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

作者： Ying, Zhenqiang Mandal, Maniratnam Ghadiyaram, Deepti Bovik, Alan Univ Texas Austin Austin TX 78712 USA Facebook AI Menlo Pk CA USA

ISBN: (纸本)9781665445092

No-reference (NR) perceptual video quality assessment (VQA) is a complex, unsolved, and important problem for social and streaming media applications. Efficient and accurate video quality predictors are needed to monitor and guide the processing of billions of shared, often imperfect, user-generated content (UGC). Unfortunately, current NR models are limited in their prediction capabilities on real-world, "in-the-wild" UGC video data. To advance progress on this problem, we created the largest (by far) subjective video quality dataset, containing 38, 811 real-world distorted videos and 116, 433 space-time localized video patches ('v-patches'), and 5.5M human perceptual quality annotations. Using this, we created two unique NR-VQA models: (a) a local-to-global region-based NR VQA architecture (called PVQ) that learns to predict global video quality and achieves state-of-the-art performance on 3 UGC datasets, and (b) a first-of-a-kind space-time video quality mapping engine (called PVQ Mapper) that helps localize and visualize perceptual distortions in space and time. The entire dataset and prediction models are freely available at https://***/***..

关键词： User-generated content Streaming media Predictive models Distortion Data models Quality assessment pattern recognition

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