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FvOR: Robust Joint Shape and Pose Optimization for Few-view Object Reconstruction

FvOR: Robust Joint Shape and Pose Optimization for Few-view ...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yang, Zhenpei Ren, Zhile Bautista, Miguel Angel Zhang, Zaiwei Shan, Qi Huang, Qixing Univ Texas Austin Austin TX 78712 USA Apple Cupertino CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Reconstructing an accurate 3D object model from a few image observations remains a challenging problem in computer vision. State-of-the-art approaches typically assume accurate camera poses as input, which could be difficult to obtain in realistic settings. In this paper, we present FvOR, a learning-based object reconstruction method that predicts accurate 3D models given a few images with noisy input poses. The core of our approach is a fast and robust multi-view reconstruction algorithm to jointly refine 3D geometry and camera pose estimation using learnable neural network modules. We provide a thorough benchmark of state-of-the-art approaches for this problem on ShapeNet. Our approach achieves best-in-class results. It is also two orders of magnitude faster than the recent optimization-based approach IDR[67].

关键词： Solid modeling computer vision Three-dimensional displays Shape Computational modeling Pose estimation Reconstruction algorithms

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Evaluation-oriented Knowledge Distillation for Deep Face recognition

Evaluation-oriented Knowledge Distillation for Deep Face Rec...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Huang, Yuge Wu, Jiaxiang Xu, Xingkun Ding, Shouhong Tencent Youtu Lab Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Knowledge distillation (KD) is a widely-used technique that utilizes large networks to improve the performance of compact models. Previous KD approaches usually aim to guide the student to mimic the teacher's behavior completely in the representation space. However, such one-toone corresponding constraints may lead to inflexible knowledge transfer from the teacher to the student, especially those with low model capacities. Inspired by the ultimate goal of KD methods, we propose a novel Evaluationoriented KD method (EKD) for deep face recognition to directly reduce the performance gap between the teacher and student models during training. Specifically, we adopt the commonly used evaluation metrics in face recognition, i.e., False Positive Rate (FPR) and True Positive Rate (TPR) as the performance indicator. According to the evaluation protocol, the critical pair relations that cause the TPR and FPR difference between the teacher and student models are selected. Then, the critical relations in the student are constrained to approximate the corresponding ones in the teacher by a novel rank-based loss function, giving more flexibility to the student with low capacity. Extensive experimental results on popular benchmarks demonstrate the superiority of our EKD over state-of-the-art competitors.

关键词： Training Performance evaluation Knowledge engineering computer vision Protocols Face recognition Benchmark testing

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ScaleNet: A Shallow Architecture for Scale Estimation

ScaleNet: A Shallow Architecture for Scale Estimation

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Barroso-Laguna, Axel Tian, Yurun Mikolajczyk, Krystian Imperial Coll London London England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we address the problem of estimating scale factors between images. We formulate the scale estimation problem as a prediction of a probability distribution over scale factors. We design a new architecture, ScaleNet, that exploits dilated convolutions as well as self-and cross-correlation layers to predict the scale between images. We demonstrate that rectifying images with estimated scales leads to significant performance improvements for various tasks and methods. Specifically, we show how ScaleNet can be combined with sparse local features and dense correspondence networks to improve camera pose estimation, 3D reconstruction, or dense geometric matching in different benchmarks and datasets. We provide an extensive evaluation on several tasks, and analyze the computational overhead of ScaleNet. The code, evaluation protocols, and trained models are publicly available at https://***/axelBarroso/ScaleNet.

关键词： Three-dimensional displays Protocols Pose estimation Pipelines Training data computer architecture Cameras

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Ranking-Based Siamese Visual Tracking

Ranking-Based Siamese Visual Tracking

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Tang, Feng Ling, Qiang Univ Sci & Technol China Dept Automat Hefei Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Current Siamese-based trackers mainly formulate the visual tracking into two independent subtasks, including classification and localization. They learn the classification subnetwork by processing each sample separately and neglect the relationship among positive and negative samples. Moreover, such tracking paradigm takes only the classification confidence of proposals for the final prediction, which may yield the misalignment between classification and localization. To resolve these issues, this paper proposes a ranking-based optimization algorithm to explore the relationship among different proposals. To this end, we introduce two ranking losses, including the classification one and the IoU-guided one, as optimization constraints. The classification ranking loss can ensure that positive samples rank higher than hard negative ones, i.e., distractors, so that the trackers can select the foreground samples successfully without being fooled by the distractors. The IoU-guided ranking loss aims to align classification confidence scores with the Intersection over Union(IoU) of the corresponding localization prediction for positive samples, enabling the well-localized prediction to be represented by high classification confidence. Specifically, the proposed two ranking losses are compatible with most Siamese trackers and incur no additional computation for inference. Extensive experiments on seven tracking benchmarks, including OTB100, UAV123, TC128, VOT2016, NFS30, GOT-10k and LaSOT, demonstrate the effectiveness of the proposed ranking-based optimization algorithm. The code and raw results are available at https: //***/sansanfree/RBO.

关键词： Location awareness Visualization computer vision Tracking Prediction algorithms Inference algorithms Classification algorithms

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Augmented Geometric Distillation for Data-Free Incremental Person ReID

Augmented Geometric Distillation for Data-Free Incremental P...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Lu, Yichen Wang, Mei Deng, Weihong Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Incremental learning (IL) remains an open issue for Person Re-identification (ReID), where a ReID system is expected to preserve preceding knowledge while learning incrementally. However, due to the strict privacy licenses and the open-set retrieval setting, it is intractable to adapt existing class IL methods to ReID. In this work, we propose an Augmented Geometric Distillation (AGD) framework to tackle these issues. First, a general data-free incremental framework with dreaming memory is constructed to avoid privacy disclosure. On this basis, we reveal a "noisy distillation" problem stemming from the noise in dreaming memory, and further propose to augment distillation in a pairwise and cross-wise pattern over different views of memory to mitigate it. Second, for the open-set retrieval property, we propose to maintain feature space structure during evolving via a novel geometric way and preserve relationships between exemplars when representations drift. Extensive experiments demonstrate the superiority of our AGD to baseline with a margin of 6.0% mAP / 7.9% R@1 and it could be generalized to class IL. Code is available here(dagger).

关键词： Privacy Data privacy computer vision Codes Semantics Licenses pattern recognition

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A Self-Supervised Descriptor for Image Copy Detection

A Self-Supervised Descriptor for Image Copy Detection

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Pizzi, Ed Roy, Sreya Dutta Ravindra, Sugosh Nagavara Goyal, Priya Douze, Matthijs Meta AI Menlo Pk CA 94025 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Image copy detection is an important task for content moderation. We introduce SSCD, a model that builds on a recent self-supervised contrastive training objective. We adapt this method to the copy detection task by changing the architecture and training objective, including a pooling operator from the instance matching literature, and adapting contrastive learning to augmentations that combine images. Our approach relies on an entropy regularization term, promoting consistent separation between descriptor vectors, and we demonstrate that this significantly improves copy detection accuracy. Our method produces a compact descriptor vector, suitable for real-world web scale applications. Statistical information from a background image distribution can be incorporated into the descriptor. On the recent DISC2021 benchmark, SSCD is shown to outperform both baseline copy detection models and self-supervised architectures designed for image classification by huge margins, in all settings. For example, SSCD outperforms SimCLR descriptors by 48% absolute. Code is available at https://***/facebookresearch/sscd-copy-detection

关键词： Training Measurement Adaptation models Image retrieval computer architecture Entropy pattern recognition

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Learning to generate line drawings that convey geometry and semantics

Learning to generate line drawings that convey geometry and ...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Chan, Caroline Durand, Fredo Isola, Phillip MIT Cambridge MA 02139 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper presents an unpaired method for creating line drawings from photographs. Current methods often rely on high quality paired datasets to generate line drawings. However, these datasets often have limitations due to the subjects of the drawings belonging to a specific domain, or in the amount of data collected. Although recent work in unsupervised image-to-image translation has shown much progress, the latest methods still struggle to generate compelling line drawings. We observe that line drawings are encodings of scene information and seek to convey 3D shape and semantic meaning. We build these observations into a set of objectives and train an image translation to map photographs into line drawings. We introduce a geometry loss which predicts depth information from the image features of a line drawing, and a semantic loss which matches the CLIP features of a line drawing with its corresponding photograph. Our approach outperforms state-of-the-art unpaired image translation and line drawing generation methods on creating line drawings from arbitrary photographs.

关键词： Geometry computer vision Three-dimensional displays Shape Semantics Encoding pattern recognition

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Local Texture Estimator for Implicit Representation Function

Local Texture Estimator for Implicit Representation Function

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Lee, Jaewon Jin, Kyong Hwan Daegu Gyeongbuk Inst Sci & Technol DGIST Daegu South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent works with an implicit neural function shed light on representing images in arbitrary resolution. However, a standalone multi-layer perceptron shows limited performance in learning high frequency components. In this paper, we propose a Local Texture Estimator (LTE), a dominant frequency estimator for natural images, enabling an implicit function to capture fine details while reconstructing images in a continuous manner. When jointly trained with a deep super-resolution (SR) architecture, LTE is capable of characterizing image textures in 2D Fourier space. We show that an LTE-based neural function achieves favorable performance against existing deep SR methods within an arbitrary-scale factor. Furthermore, we demonstrate that our implementation takes the shortest running time compared to previous works.

关键词： Visualization Image texture computer vision Superresolution computer architecture Feature extraction Frequency estimation

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MeMViT: Memory-Augmented Multiscale vision Transformer for Efficient Long-Term Video recognition

MeMViT: Memory-Augmented Multiscale Vision Transformer for E...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wu, Chao-Yuan Li, Yanghao Mangalam, Karttikeya Fan, Haoqi Xiong, Bo Malik, Jitendra Feichtenhofer, Christoph Facebook AI Res Menlo Pk CA 94025 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

While today's video recognition systems parse snapshots or short clips accurately, they cannot connect the dots and reason across a longer range of time yet. Most existing video architectures can only process <5 seconds of a video without hitting the computation or memory bottlenecks. In this paper, we propose a new strategy to overcome this challenge. Instead of trying to process more frames at once like most existing methods, we propose to process videos in an online fashion and cache "memory" at each iteration. Through the memory, the model can reference prior context for long-term modeling, with only a marginal cost. Based on this idea, we build MeMViT, a Memory-augmented Multiscale vision Transformer, that has a temporal support 30xlonger than existing models with only 4.5% more compute;traditional methods need >3,000% more compute to do the same. On a wide range of settings, the increased temporal support enabled by MeMViT brings large gains in recognition accuracy consistently. MeMViT obtains state-of-the-art results on the AVA, EPIC-Kitchens-100 action classification, and action anticipation datasets. Code and models will be made publicly available.

关键词： computer vision Visualization Costs Codes Computational modeling Cache memory computer architecture

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NeRFusion: Fusing Radiance Fields for Large-Scale Scene Reconstruction

NeRFusion: Fusing Radiance Fields for Large-Scale Scene Reco...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Xiaoshuai Bi, Sai Sunkavalli, Kalyan Su, Hao Xu, Zexiang Univ Calif San Diego San Diego CA 92103 USA Adobe Res Washington DC USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

While NeRF [28] has shown great success for neural reconstruction and rendering, its limited MLP capacity and long per-scene optimization times make it challenging to model large-scale indoor scenes. In contrast, classical 3D reconstruction methods can handle large-scale scenes but do not produce realistic renderings. We propose NeRFusion, a method that combines the advantages of NeRF and TSDF-based fusion techniques to achieve efficient largescale reconstruction and photo-realistic rendering. We process the input image sequence to predict per-frame local radiance fields via direct network inference. These are then fused using a novel recurrent neural network that incrementally reconstructs a global, sparse scene representation in real-time at 22 fps. This global volume can be further fine-tuned to boost rendering quality. We demonstrate that NeR-Fusionachieves state-of-the-art quality on both large-scale indoor and small-scale object scenes, with substantially faster reconstruction than NeRF and other recent methods.

关键词： computer vision Recurrent neural networks Three-dimensional displays Rendering (computer graphics) Real-time systems Image sequences pattern recognition

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