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Weak Multi-View Supervision for Surface Mapping Estimation

Weak Multi-View Supervision for Surface Mapping Estimation

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

作者： Rai, Nishant Liaudanskas, Aidas Rao, Srinivas Cayon, Rodrigo Ortiz Munaro, Matteo Holzer, Stefan Fyusion Inc San Francisco CA 94105 USA Stanford Univ Stanford CA 94305 USA Fyusion San Francisco CA USA

ISBN: (纸本)9781665448994

We propose a weakly-supervised multi-view learning approach to learn category-specific surface mapping without dense annotations. We learn the underlying surface geometry of common categories, such as human faces, cars, and airplanes, given instances from those categories. While traditional approaches solve this problem using extensive supervision in the form of pixel-level annotations, we take advantage of the fact that pixel-level UV and mesh predictions can be combined with 3D reprojections to form consistency cycles. As a result of exploiting these cycles, we can establish a dense correspondence mapping between image pixels and the mesh acting as a self-supervisory signal, which in turn helps improve our overall estimates. Our approach leverages information from multiple views of the object to establish additional consistency cycles, thus improving surface mapping understanding without the need for explicit annotations. We also propose the use of deformation fields for predictions of an instance specific mesh. Given the lack of datasets providing multiple images of similar object instances from different viewpoints, we generate and release a multi-view ShapeNet Cars and Airplanes dataset created by rendering ShapeNet meshes using a 360ffi camera trajectory around the mesh. For the human faces category, we process and adapt an existing dataset to a multi-view setup. Through experimental evaluations, we show that, at test time, our method can generate accurate variations away from the mean shape, is multi-view consistent, and performs comparably to fully supervised approaches.

关键词： Deformable models Surface reconstruction Airplanes Annotations Rendering (computer graphics) Trajectory Automobiles

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Self-supervised multi-image super-resolution for push-frame satellite images

Self-supervised multi-image super-resolution for push-frame ...

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

作者： Ngoc Long Nguyen Anger, Jeremy Davy, Axel Arias, Pablo Facciolo, Gabriele Univ Paris Saclay Ctr Borelli ENS Paris Saclay CNRS Gif Sur Yvette France Kayrros SAS Paris France

ISBN: (纸本)9781665448994

Recent constellations of optical satellites are adopting multi-image super-resolution (MISR) from bursts of push-frame images as a way to increase the resolution and reduce the noise of their products while maintaining a lower cost of operation. Most MISR techniques are currently based on the aggregation of samples from registered low resolution images. A promising research trend aimed at incorporating natural image priors in MISR consists in using data-driven neural networks. However, due to the unavailability of ground truth high resolution data, these networks cannot be trained on real satellite images. In this paper, we present a framework for training MISR algorithms from bursts of satellite images without requiring high resolution ground truth. This is achieved by adapting the recently proposed frame-to-frame framework to process bursts of satellite images. In addition we propose an architecture based on feature aggregation that allows to fuse a variable number of frames and is capable of handling degenerate samplings while also reducing noise. On synthetic datasets, the proposed self-supervision strategy attains results on par with those obtained with a supervised training. We applied our framework to real SkySat satellite image bursts leading to results that are more resolved and less noisy than the L1B product from Planet.

关键词： Training Satellites Planets Superresolution Neural networks computer architecture Optical imaging

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Visual Query Answering by Entity-Attribute Graph Matching and Reasoning 32

Visual Query Answering by Entity-Attribute Graph Matching an...

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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Xiong, Peixi Zhan, Huayi Wang, Xin Sinha, Baivab Wu, Ying Northwestern Univ Evanston IL 60208 USA Southwest Jiaotong Univ Chengdu Peoples R China Sichuan Changhong Elect Co Ltd Mianyang Sichuan Peoples R China

ISBN: (纸本)9781728132938

Visual Query Answering (VQA) is of great significance in offering people convenience: one can raise a question for details of objects, or high-level understanding about the scene, over an image. This paper proposes a novel method to address the VQA problem. In contrast to prior works, our method that targets single scene VQA, replies on graph-based techniques and involves reasoning. In a nutshell, our approach is centered on three graphs. The first graph, referred to as inference graph G(I) , is constructed via learning over labeled data. The other two graphs, referred to as query graph Q and entity-attribute graph G(EA), are generated from natural language query Q(nl) and image Img, that are issued from users, respectively. As GEA often does not take sufficient information to answer Q, we develop techniques to infer missing information of G(EA) with G(I) . Based on G(EA) and Q, we provide techniques to find matches of Q in G(EA), as the answer of Q(nl) in Img. Unlike commonly used VQA methods that are based on end-to-end neural networks, our graph-based method shows well-designed reasoning capability, and thus is highly interpretable. We also create a dataset on soccer match (Soccer-VQA)(1) with rich annotations. The experimental results show that our approach outperforms the state-of-the-art method and has high potential for future investigation.

关键词： Datasets and Evaluation vision + Graphics vision + Language

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Dual Pixel Exploration: Simultaneous Depth Estimation and Image Restoration

Dual Pixel Exploration: Simultaneous Depth Estimation and Im...

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

作者： Pan, Liyuan Chowdhury, Shah Hartley, Richard Liu, Miaomiao Zhang, Hongguang Li, Hongdong Australian Natl Univ Canberra ACT Australia CSIRO A&F Canberra ACT Australia CSIRO Data61 Canberra ACT Australia SE Inst AMS Amsterdam Netherlands

ISBN: (纸本)9781665445092

The dual-pixel (DP) hardware works by splitting each pixel in half and creating an image pair in a single snapshot. Several works estimate depth/inverse depth by treating the DP pair as a stereo pair. However, dual-pixel disparity only occurs in image regions with the defocus blur. The heavy defocus blur in DP pairs affects the performance of matching-based depth estimation approaches. Instead of removing the blur effect blindly, we study the formation of the DP pair which links the blur and the depth information. In this paper, we propose a mathematical DP model which can benefit depth estimation by the blur. These explorations motivate us to propose an end-to-end DDDNet (DP-based Depth and Deblur Network) to jointly estimate the depth and restore the image. Moreover, we define a reblur loss, which reflects the relationship of the DP image formation process with depth information, to regularise our depth estimate in training. To meet the requirement of a large amount of data for learning, we propose the first DP image simulator which allows us to create datasets with DP pairs from any existing RGBD dataset. As a side contribution, we collect a real dataset for further research. Extensive experimental evaluation on both synthetic and real datasets shows that our approach achieves competitive performance compared to state-of-the-art approaches.

关键词： Training computer vision Computational modeling Buildings Estimation Cameras Mathematical models

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Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction

Direct Voxel Grid Optimization: Super-fast Convergence for R...

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

作者： Sun, Cheng Sun, Min Chen, Hwann-Tzong Natl Tsing Hua Univ Hsinchu Taiwan ASUS AICS Dept Singapore Singapore Joint Res Ctr AI Technol & All Vista Healthcare Taipei Taiwan Aeolus Robot Taipei Taiwan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present a super-fast convergence approach to reconstructing the per-scene radiance field from a set of images that capture the scene with known poses. This task, which is often applied to novel view synthesis, is recently revolutionized by Neural Radiance Field (NeRF) for its state-of-the-art quality and flexibility. However, NeRF and its variants require a lengthy training time ranging from hours to days for a single scene. In contrast, our approach achieves NeRF-comparable quality and converges rapidly from scratch in less than 15 minutes with a single GPU. We adopt a representation consisting of a density voxel grid for scene geometry and a feature voxel grid with a shallow network for complex view-dependent appearance. Modeling with explicit and discretized volume representations is not new, but we propose two simple yet non-trivial techniques that contribute to fast convergence speed and high-quality output. First, we introduce the post-activation interpolation on voxel density, which is capable of producing sharp surfaces in lower grid resolution. Second, direct voxel density optimization is prone to suboptimal geometry solutions, so we robustify the optimization process by imposing several priors. Finally, evaluation on five inward-facing benchmarks shows that our method matches, if not surpasses, NeRF's quality, yet it only takes about 15 minutes to train from scratch for a new scene. Code: https:// ***/sunset1995/DirectVoxGO.

关键词： Training Geometry Solid modeling Interpolation pattern recognition Task analysis Optimization

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Augmentation Strategies for Learning with Noisy Labels

Augmentation Strategies for Learning with Noisy Labels

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

作者： Nishi, Kento Ding, Yi Rich, Alex Hollerer, Tobias Lynbrook High Sch San Jose CA 95129 USA Univ Calif Santa Barbara Santa Barbara CA 93106 USA

ISBN: (纸本)9781665445092

Imperfect labels are ubiquitous in real-world datasets. Several recent successful methods for training deep neural networks (DNNs) robust to label noise have used two primary techniques: filtering samples based on loss during a warm-up phase to curate an initial set of cleanly labeled samples, and using the output of a network as a pseudo-label for subsequent loss calculations. In this paper, we evaluate different augmentation strategies for algorithms tackling the "learning with noisy labels" problem. We propose and examine multiple augmentation strategies and evaluate them using synthetic datasets based on CIFAR-10 and CIFAR-100, as well as on the real-world dataset ClothinglM. Due to several commonalities in these algorithms, we find that using one set of augmentations for loss modeling tasks and another set for learning is the most effective, improving results on the state-of-the-art and other previous methods. Furthermore, we find that applying augmentation during the warm-up period can negatively impact the loss convergence behavior of correctly versus incorrectly labeled samples. We introduce this augmentation strategy to the state-of-the-art technique and demonstrate that we can improve performance across all evaluated noise levels. In particular, we improve accuracy on the CIFAR-10 benchmark at 90% symmetric noise by more than 15% in absolute accuracy, and we also improve performance on the Clothing1M dataset.

关键词： Training Deep learning Machine learning algorithms Filtering Neural networks pattern recognition Noise measurement

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Towards Semi-Supervised Deep Facial Expression recognition with An Adaptive Confidence Margin

Towards Semi-Supervised Deep Facial Expression Recognition w...

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

作者： Li, Hangyu Wang, Nannan Yang, Xi Wang, Xiaoyu Gao, Xinbo Xidian Univ Xian Peoples R China Chinese Univ Hong Kong Shenzhen Shenzhen Peoples R China Chongqing Univ Posts & Telecommun Chongqing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Only parts of unlabeled data are selected to train models for most semi-supervised learning methods, whose confidence scores are usually higher than the pre-defined threshold (i.e., the confidence margin). We argue that the recognition performance should be further improved by making MI use of all unlabeled data. In this paper, we learn an Adaptive Confidence Margin (Ada-CM) to fully leverage all unlabeled data for semi-supervised deep facial expression recognition. All unlabeled samples are partitioned into two subsets by comparing their confidence scores with the adaptively learned confidence margin at each training epoch: (1) subset I including samples whose confidence scores are no lower than the margin;(2) subset II including samples whose confidence scores are lower than the margin. For samples in subset I, we constrain their predictions to match pseudo labels. Meanwhile, samples in subset II participate in the feature-level contrastive objective to learn effective facial expression features. We extensively evaluate Ada-CM on four challenging datasets, showing that our method achieves state-of-the-art performance, especially surpassing fully-supervised baselines in a semi-supervised manner. Ablation study further proves the effectiveness of our method. The source code is available at https : //***/hangyu94/Ada-CM.

关键词： Training Adaptation models computer vision Codes Face recognition Semisupervised learning Data models

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PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds

PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estim...

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

作者： Wei, Yi Wang, Ziyi Rao, Yongming Lu, Jiwen Zhou, Jie Tsinghua Univ Dept Automat Beijing Peoples R China State Key Lab Intelligent Technol & Syst Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Beijing Peoples R China

ISBN: (纸本)9781665445092

In this paper, we propose a Point-Voxel Recurrent All-Pairs Field Transforms (PV-RAFT) method to estimate scene flow from point clouds. Since point clouds are irregular and unordered, it is challenging to efficiently extract features from all-pairs fields in the 3D space, where all-pairs correlations play important roles in scene flow estimation. To tackle this problem, we present point-voxel correlation fields, which capture both local and long-range dependencies of point pairs. To capture point-based correlations, we adopt the K-Nearest Neighbors search that preserves fine-grained information in the local region. By voxelizing point clouds in a multi-scale manner, we construct pyramid correlation voxels to model long-range correspondences. Integrating these two types of correlations, our PV-RAFT makes use of all-pairs relations to handle both small and large displacements. We evaluate the proposed method on the FlyingThings3D and KITTI Scene Flow 2015 datasets. Experimental results show that P V-RAFT outperforms state-of-the-art methods by remarkable margins.

关键词： Visualization computer vision Correlation Three-dimensional displays Estimation Lattices Transforms

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Revisiting Random Channel Pruning for Neural Network Compression

Revisiting Random Channel Pruning for Neural Network Compres...

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

作者： Li, Yawei Adamczewski, Kamil Li, Wen Gu, Shuhang Timofte, Radu Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland MPI IS Stuttgart Germany UESTC Chengdu Peoples R China USYD Camperdown NSW Australia Katholieke Univ Leuven Leuven Belgium

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Channel (or 3D filter) pruning serves as an effective way to accelerate the inference of neural networks. There has been a flurry of algorithms that try to solve this practical problem, each being claimed effective in some ways. Yet, a benchmark to compare those algorithms directly is lacking, mainly due to the complexity of the algorithms and some custom settings such as the particular network configuration or training procedure. A fair benchmark is important for the further development of channel pruning. Meanwhile, recent investigations reveal that the channel configurations discovered by pruning algorithms are at least as important as the pre-trained weights. This gives channel pruning a new role, namely searching the optimal channel configuration. In this paper, we try to determine the channel configuration of the pruned models by random search. The proposed approach provides a new way to compare different methods, namely how well they behave compared with random pruning. We show that this simple strategy works quite well compared with other channel pruning methods. We also show that under this setting, there are surprisingly no clear winners among different channel importance evaluation methods, which then may tilt the research efforts into advanced channel configuration searching methods. Code will be released at https: // github. com/ ofsoundof/ random_ channel_ pruning.

关键词： Training computer vision Three-dimensional displays Neural network compression Benchmark testing Network architecture Filtering algorithms

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Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans

Neural Body: Implicit Neural Representations with Structured...

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

作者： Peng, Sida Zhang, Yuanqing Xu, Yinghao Wang, Qianqian Shuai, Qing Bao, Hujun Zhou, Xiaowei Zhejiang Univ Hangzhou Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9781665445092

This paper addresses the challenge of novel view synthesis for a human performer from a very sparse set of camera views. Some recent works have shown that learning implicit neural representations of 3D scenes achieves remarkable view synthesis quality given dense input views. However, the representation learning will be ill-posed if the views are highly sparse. To solve this ill-posed problem, our key idea is to integrate observations over video frames. To this end, we propose Neural Body, a new human body representation which assumes that the learned neural representations at different frames share the same set of latent codes anchored to a deformable mesh, so that the observations across frames can be naturally integrated. The deformable mesh also provides geometric guidance for the network to learn 3D representations more efficiently. To evaluate our approach, we create a multi-view dataset named ZJU-MoCap that captures performers with complex motions. Experiments on ZJU-MoCap show that our approach outperforms prior works by a large margin in terms of novel view synthesis quality. We also demonstrate the capability of our approach to reconstruct a moving person from a monocular video on the People-Snapshot dataset.

关键词： Geometry Deformable models computer vision Codes Three-dimensional displays Neural networks Dynamics

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