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FFTLasso: Large-Scale LASSO in the Fourier Domain 30

FFTLasso: Large-Scale LASSO in the Fourier Domain

引用

30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Bibi, Adel Itani, Hani Ghanem, Bernard King Abdullah Univ Sci & Technol KAUST Thuwal Saudi Arabia

ISBN: (纸本)9781538604571

In this paper, we revisit the LASSO sparse representation problem, which has been studied and used in a variety of different areas, ranging from signal processing and information theory to computer vision and machine learning. In the vision community, it found its way into many important applications, including face recognition, tracking, super resolution, image denoising, to name a few. Despite advances in efficient sparse algorithms, solving large-scale LASSO problems remains a challenge. To circumvent this difficulty, people tend to downsample and subsample the problem (e.g. via dimensionality reduction) to maintain a manageable sized LASSO, which usually comes at the cost of losing solution accuracy. this paper proposes a novel circulant reformulation of the LASSO that lifts the problem to a higher dimension, where ADMM can be efficiently applied to its dual form. Because of this lifting, all optimization variables are updated using only basic element-wise operations, the most computationally expensive of which is a 1D FFT. In this way, there is no need for a linear system solver nor matrix-vector multiplication. Since all operations in our FFTLasso method are element-wise, the subproblems are completely independent and can be trivially parallelized (e.g. on a GPU). the attractive computational properties of FFTLasso are verified by extensive experiments on synthetic and real data and on the face recognition task. they demonstrate that FFTLasso scales much more effectively than a state-of-the-art solver.

关键词： Dictionaries Face recognition Sparse matrices Convolutional codes Linear systems Graphics processing units Encoding

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Level Playing Field for Million Scale Face recognition 30

Level Playing Field for Million Scale Face Recognition

引用

30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Nech, Aaron Kemelmacher-Shlizerman, Ira Univ Washington Paul G Allen Sch Comp Sci & Engn Seattle WA 98195 USA

ISBN: (纸本)9781538604571

Face recognition has the perception of a solved problem, however when tested at the million-scale exhibits dramatic variation in accuracies across the different algorithms [11]. Are the algorithms very different? Is access to good/big training data their secret weapon? Where should face recognition improve? To address those questions, we created a benchmark, MF2, that requires all algorithms to be trained on same data, and tested at the million scale. MF2 is a public large-scale set with 672K identities and 4.7M photos created with the goal to level playing field for large scale face recognition. We contrast our results with findings from the other two large-scale benchmarks MegaFace Challenge and MS-Celebs-1M where groups were allowed to train on any private/public/big/small set. Some key discoveries: 1) algorithms, trained on MF2, were able to achieve state of the art and comparable results to algorithms trained on massive private sets, 2) some outperformed themselves once trained on MF2, 3) invariance to aging suffers from low accuracies as in MegaFace, identifying the need for larger age variations possibly within identities or adjustment of algorithms in future testings(1)

关键词： Face Flickr Training Face recognition Benchmark testing Clustering algorithms Labeling

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Quo Vadis, Action recognition? A New Model and the Kinetics Dataset 30

Quo Vadis, Action Recognition? A New Model and the Kinetics ...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Carreira, Joao Zisserman, Andrew DeepMind London England Univ Oxford Dept Engn Sci Oxford England

ISBN: (纸本)9781538604571

the paucity of videos in current action classification datasets (UCF-101 and HMDB-51) has made it difficult to identify good video architectures, as most methods obtain similar performance on existing small-scale benchmarks. this paper re-evaluates state-of-the-art architectures in light of the new Kinetics Human Action Video dataset. Kinetics has two orders of magnitude more data, with 400 human action classes and over 400 clips per class, and is collected from realistic, challenging YouTube videos. We provide an analysis on how current architectures fare on the task of action classification on this dataset and how much performance improves on the smaller benchmark datasets after pre-training on Kinetics. We also introduce a new Two-Stream Inflated 3D Conv-Net (I3D) that is based on 2D ConvNet inflation: filters and pooling kernels of very deep image classification ConvNets are expanded into 3D, making it possible to learn seamless spatio-temporal feature extractors from video while leveraging successful ImageNet architecture designs and even their parameters. We show that, after pre-training on Kinetics, I3D models considerably improve upon the state-of-the-art in action classification, reaching 80.2% on HMDB-51 and 97.9% on UCF-101.

关键词： Videotapes Dataset UCF 101 Kinetics ARCHITECTURE Action model cars

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What Is the Space of Attenuation Coefficients in Underwater computer vision? 30

What Is the Space of Attenuation Coefficients in Underwater ...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Akkaynak, Derya Treibitz, Tali Shlesinger, Tom Tamir, Raz Loya, Yossi Iluz, David Univ Haifa Haifa Israel Interuniv Inst Marine Sci Elat Israel Tel Aviv Univ Tel Aviv Israel Bar Ilan Univ Ramat Gan Israel

ISBN: (纸本)9781538604571

Underwater image reconstruction methods require the knowledge of wideband attenuation coefficients per color channel. Current estimation methods for these coefficients require specialized hardware or multiple images, and none of them leverage the multitude of existing ocean optical measurements as priors. Here, we aim to constrain the set of physically-feasible wideband attenuation coefficients in the ocean by utilizing water attenuation measured worldwide by oceanographers. We calculate the space of valid wideband effective attenuation coefficients in the 3D RGB domain and find that a bound manifold in 3-space sufficiently represents the variation from the clearest to murkiest waters. We validate our model using in situ experiments in two different optical water bodies, the Red Sea and the Mediterranean. Moreover, we show that contradictory to the common image formation model, the coefficients depend on the imaging range and object reflectance, and quantify the errors resulting from ignoring these dependencies.

关键词： Attenuation Image color analysis Oceans Sea measurements Wideband Cameras Optical attenuators

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Multi-task Correlation Particle Filter for Robust Object Tracking 30

Multi-task Correlation Particle Filter for Robust Object Tra...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Tianzhu Xu, Changsheng Yang, Ming-Hsuan Chinese Acad Sci Natl Lab Pattern Recognit Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Calif Merced Merced CA USA

ISBN: (纸本)9781538604571

In this paper, we propose a multi-task correlation particle filter (MCPF) for robust visual tracking. We first present the multi-task correlation filter (MCF) that takes the interdependencies among different features into account to learn correlation filters jointly. the proposed MCPF is designed to exploit and complement the strength of a MCF and a particle filter. Compared with existing tracking methods based on correlation filters and particle filters, the proposed tracker has several advantages. First, it can shepherd the sampled particles toward the modes of the target state distribution via the MCF, thereby resulting in robust tracking performance. Second, it can effectively handle large-scale variation via a particle sampling strategy. third, it can effectively maintain multiple modes in the posterior density using fewer particles than conventional particle filters, thereby lowering the computational cost. Extensive experimental results on three benchmark datasets demonstrate that the proposed MCPF performs favorably against the state-of-the-art methods.

关键词： Correlation Target tracking Visualization Robustness Computational efficiency Benchmark testing

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Multimodal Transfer: A Hierarchical Deep Convolutional Neural Network for Fast Artistic Style Transfer 30

Multimodal Transfer: A Hierarchical Deep Convolutional Neura...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wang, Xin Oxholm, Geoffrey Zhang, Da Wang, Yuan-Fang Univ Calif Santa Barbara Santa Barbara CA 93106 USA Adobe Res San Francisco CA 94103 USA

ISBN: (纸本)9781538604571

Transferring artistic styles onto everyday photographs has become an extremely popular task in both academia and industry. Recently, offline training has replaced online iterative optimization, enabling nearly real-time stylization. When those stylization networks are applied directly to high-resolution images, however, the style of localized regions often appears less similar to the desired artistic style. this is because the transfer process fails to capture small, intricate textures and maintain correct texture scales of the artworks. Here we propose a multimodal convolutional neural network that takes into consideration faithful representations of both color and luminance channels, and performs stylization hierarchically with multiple losses of increasing scales. Compared to state-of-the-art networks, our network can also perform style transfer in nearly realtime by conducting much more sophisticated training offline. By properly handling style and texture cues at multiple scales using several modalities, we can transfer not just large-scale, obvious style cues but also subtle, exquisite ones. that is, our scheme can generate results that are visually pleasing and more similar to multiple desired artistic styles with color and texture cues at multiple scales.

关键词： Image color analysis Biological neural networks Training computer architecture Optimization

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Mining Object Parts from CNNs via Active Question-Answering 30

Mining Object Parts from CNNs via Active Question-Answering

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Quanshi Cao, Ruiming Wu, Ying Nian Zhu, Song-Chun Univ Calif Los Angeles Los Angeles CA 90024 USA

ISBN: (纸本)9781538604571

Given a convolutional neural network (CNN) that is pre-trained for object classification, this paper proposes to use active question-answering to semanticize neural patterns in conv-layers of the CNN and mine part concepts. For each part concept, we mine neural patterns in the pre-trained CNN, which are related to the target part, and use these patterns to construct an And-Or graph (AOG) to represent a four-layer semantic hierarchy of the part. As an interpretable model, the AOG associates different CNN units with different explicit object parts. We use an active human-computer communication to incrementally grow such an AOG on the pre-trained CNN as follows. We allow the computer to actively identify objects, whose neural patterns cannot be explained by the current AOG. then, the computer asks human about the unexplained objects, and uses the answers to automatically discover certain CNN patterns corresponding to the missing knowledge. We incrementally grow the AOG to encode new knowledge discovered during the active-learning process. In experiments, our method exhibits high learning efficiency. Our method uses about 1/6-1/3 of the part annotations for training, but achieves similar or better part-localization performance than fast-RCNN methods.

关键词： Semantics Visualization Strain Object recognition Training Object detection

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Stereo-Based 3D Reconstruction of Dynamic Fluid Surfaces by Global Optimization 30

Stereo-Based 3D Reconstruction of Dynamic Fluid Surfaces by ...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Qian, Yiming Gong, Minglun Yang, Yee-Hong Univ Alberta Edmonton AB Canada Mem Univ Newfoundland St John NF Canada

ISBN: (纸本)9781538604571

3D reconstruction of dynamic fluid surfaces is an open and challenging problem in computer vision. Unlike previous approaches that reconstruct each surface point independently and often return noisy depth maps, we propose a novel global optimization-based approach that recovers both depths and normals of all 3D points simultaneously. Using the traditional refraction stereo setup, we capture the wavy appearance of a pre-generated random pattern, and then estimate the correspondences between the captured images and the known background by tracking the pattern. Assuming that the light is refracted only once through the fluid interface, we minimize an objective function that incorporates both the cross-view normal consistency constraint and the single-view normal consistency constraints. the key idea is that the normals required for light refraction based on Snell's law from one view should agree with not only the ones from the second view, but also the ones estimated from local 3D geometry. Moreover, an effective reconstruction error metric is designed for estimating the refractive index of the fluid. We report experimental results on both synthetic and real data demonstrating that the proposed approach is accurate and shows superiority over the conventional stereo-based method.

关键词： Stereo-Based 3D Reconstruction Dynamic Fluid Surfaces by Global Optimization three-dimensional displays Global optimization Reconstructive Surgical Procedures Fluid images computer vision normal consistency Light refraction consistency constraint Reconstructions Normal

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Dynamic Facial Analysis: From Bayesian Filtering to Recurrent Neural Network 30

Dynamic Facial Analysis: From Bayesian Filtering to Recurren...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Gu, Jinwei Yang, Xiaodong De Mello, Shalini Kautz, Jan NVIDIA Santa Clara CA 95051 USA

ISBN: (纸本)9781538604571

Facial analysis in videos, including head pose estimation and facial landmark localization, is key for many applications such as facial animation capture, human activity recognition, and human-computer interaction. In this paper, we propose to use a recurrent neural network (RNN) for joint estimation and tracking of facial features in videos. We are inspired by the fact that the computation performed in an RNN bears resemblance to Bayesian filters, which have been used for tracking in many previous methods for facial analysis from videos. Bayesian filters used in these methods, however, require complicated, problem-specific design and tuning. In contrast, our proposed RNN-based method avoids such tracker-engineering by learning from training data, similar to how a convolutional neural network (CNN) avoids feature-engineering for image classification. As an end-to-end network, the proposed RNN-based method provides a generic and holistic solution for joint estimation and tracking of various types of facial features from consecutive video frames. Extensive experimental results on head pose estimation and facial landmark localization from videos demonstrate that the proposed RNN-based method outperforms frame-wise models and Bayesian filtering. In addition, we create a large-scale synthetic dataset for head pose estimation, with which we achieve state-of-the-art performance on a benchmark dataset.

关键词： Bayesian filtering recurrent neural nets Videotapes joint estimation Facial Features Face Face Head tracking pose estimation computer human interaction

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Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations 30

Efficient Diffusion on Region Manifolds: Recovering Small Ob...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Iscen, Ahmet Tolias, Giorgos Avrithis, Yannis Furon, Teddy Chum, Ondrej Inria Rennes Rennes France CTU FEE VRG Prague Czech Republic

ISBN: (纸本)9781538604571

Query expansion is a popular method to improve the quality of image retrieval with both conventional and CNN representations. It has been so far limited to global image similarity. this work focuses on diffusion, a mechanism that captures the image manifold in the feature space. the diffusion is carried out on descriptors of overlapping image regions rather than on a global image descriptor like in previous approaches. An efficient off-line stage allows optional reduction in the number of stored regions. In the on-line stage, the proposed handling of unseen queries in the indexing stage removes additional computation to adjust the precomputed data. We perform diffusion through a sparse linear system solver, yielding practical query times well below one second. Experimentally, we observe a significant boost in performance of image retrieval with compact CNN descriptors on standard benchmarks, especially when the query object covers only a small part of the image. Small objects have been a common failure case of CNN-based retrieval.

关键词： IMAGING MANIFOLD diffusion CASING FAILURE image retrieval Quantum efficiency Descriptors region of an image Eigenvector Small parts Compact Compact pattern representations Manifolds RECOVERING Imagery (Psychotherapy)

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