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MR-VNet: Media Restoration using Volterra Networks

MR-VNet: Media Restoration using Volterra Networks

引用

conference on computer vision and pattern recognition (cvpr)

作者： Siddharth Roheda Amit Unde Loay Rashid Samsung Research Institute Bengaluru India

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

This research paper presents a novel class of restoration network architecture based on the Volterra series formulation. By incorporating non-linearity into the system response function through higher order convolutions instead of traditional activation functions, we introduce a general framework for image/video restoration. Through extensive experimentation, we demonstrate that our proposed architecture achieves state-of-the-art (SOTA) performance in the field of Image/video Restoration. Moreover, we establish that the recently introduced Non-Linear Activation Free Network (NAF-NET) can be considered a special case within the broader class of Volterra Neural Networks. These findings highlight the potential of Volterra Neural Networks as a versatile and powerful tool for addressing complex restoration tasks in computer vision.

关键词： computer vision PSNR Filters Neural networks Noise computer architecture Media

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Self-Point-Flow: Self-Supervised Scene Flow Estimation from Point Clouds with Optimal Transport and Random Walk

Self-Point-Flow: Self-Supervised Scene Flow Estimation from ...

引用

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

作者： Li, Ruibo Lin, Guosheng Xie, Lihua Nanyang Technol Univ S Lab Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9781665445092

Due to the scarcity of annotated scene flow data, self-supervised scene flow learning in point clouds has attracted increasing attention. In the self-supervised manner, establishing correspondences between two point clouds to approximate scene flow is an effective approach. Previous methods often obtain correspondences by applying point-wise matching that only takes the distance on 3D point coordinates into account, introducing two critical issues: (1) it overlooks other discriminative measures, such as color and surface normal, which often bring fruitful clues for accurate matching;and (2) it often generates sub-par performance, as the matching is operated in an unconstrained situation, where multiple points can be ended up with the same corresponding point. To address the issues, we formulate this matching task as an optimal transport problem. The output optimal assignment matrix can be utilized to guide the generation of pseudo ground truth. In this optimal transport, we design the transport cost by considering multiple descriptors and encourage one-to-one matching by mass equality constraints. Also, constructing a graph on the points, a random walk module is introduced to encourage the local consistency of the pseudo labels. Comprehensive experiments on FlyingThings3D and KITTI show that our method achieves state-of-the-art performance among self-supervised learning methods. Our self-supervised method even performs on par with some supervised learning approaches, although we do not need any ground truth flow for training.

关键词： Training computer vision Three-dimensional displays Costs Image color analysis Supervised learning Estimation

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Searching for Fast Model Families on Datacenter Accelerators

Searching for Fast Model Families on Datacenter Accelerators

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

作者： Li, Sheng Tan, Mingxing Pang, Ruoming Li, Andrew Cheng, Liqun Le, Quoc, V Jouppi, Norman P. Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Neural Architecture Search (NAS), together with model scaling, has shown remarkable progress in designing high accuracy and fast convolutional architecture families. However, as neither NAS nor model scaling considers sufficient hardware architecture details, they do not take full advantage of the emerging datacenter (DC) accelerators. In this paper, we search for fast and accurate CNN model families for efficient inference on DC accelerators. We first analyze DC accelerators and find that existing CNNs suffer from insufficient operational intensity, parallelism, and execution efficiency and exhibit FLOPs-latency nonproportionality. These insights let us create a DC-accelerator-optimized search space, with space-to-depth, space-to-batch, hybrid fused convolution structures with vanilla and depthwise convolutions, and block-wise activation functions. We further propose a latency-aware compound scaling (LACS), the first multi-objective compound scaling method optimizing both accuracy and latency. Our LACS discovers that network depth should grow much faster than image size and network width, which is quite different from the observations from previous compound scaling. With the new search space and LACS, our search and scaling on datacenter accelerators results in a new model series named EfficientNet-X. EfficientNet-X is up to more than 2X faster than Efficient-Net (a model series with state-of-the-art trade-off on FLOPs and accuracy) on TPUv3 and GPUv100, with comparable accuracy. EfficientNet-X is also up to 7X faster than recent RegNet and ResNeSt on TPUv3 and GPUv100.

关键词： Convolutional codes computer vision Convolution Computational modeling Search methods Parallel processing Hardware

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Causal Hidden Markov Model for Time Series Disease Forecasting

Causal Hidden Markov Model for Time Series Disease Forecasti...

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

作者： Li, Jing Wu, Botong Sun, Xinwei Wang, Yizhou Peking Univ Dept Comp Sci Beijing Peoples R China Peking Univ Adv Inst Info Tech Beijing Peoples R China Peking Univ Ctr Frontiers Comp Studies Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China Deepwise AI Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

We propose a causal hidden Markov model to achieve robust prediction of irreversible disease at an early stage, which is safety-critical and vital for medical treatment in early stages. Specifically, we introduce the hidden variables which propagate to generate medical data at each time step. To avoid learning spurious correlation (e.g., confounding bias), we explicitly separate these hidden variables into three parts: a) the disease (clinical)-related part;b) the disease (non-clinical)-related part;c) others, with only a),b) causally related to the disease however c) may contain spurious correlations (with the disease) inherited from the data provided. With personal attributes and disease label respectively provided as side information and supervision, we prove that these disease-related hidden variables can be disentangled from others, implying the avoidance of spurious correlation for generalization to medical data from other (out-of-) distributions. Guaranteed by this result, we propose a sequential variational auto-encoder with a reformulated objective function. We apply our model to the early prediction of peripapillary atrophy and achieve promising results on out-of-distribution test data. Further, the ablation study empirically shows the effectiveness of each component in our method. And the visualization shows the accurate identification of lesion regions from others. (1)

关键词： Correlation Time series analysis Hidden Markov models Medical treatment Predictive models Linear programming pattern recognition

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ABMDRNet: Adaptive-weighted Bi-directional Modality Difference Reduction Network for RGB-T Semantic Segmentation

ABMDRNet: Adaptive-weighted Bi-directional Modality Differen...

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

作者： Zhang, Qiang Zhao, Shenlu Luo, Yongjiang Zhang, Dingwen Huang, Nianchang Han, Jungong Xidian Univ Sch Mechano Elect Engn Xian Peoples R China Xidian Univ Sch Elect Engn Xian Peoples R China Aberystwyth Univ Comp Sci Dept Aberystwyth Dyfed Wales

ISBN: (纸本)9781665445092

Semantic segmentation models gain robustness against poor lighting conditions by virtue of complementary information from visible (RGB) and thermal images. Despite its importance, most existing RGB-T semantic segmentation models perform primitive fusion strategies, such as concatenation, element-wise summation and weighted summation, to fuse features from different modalities. These strategies, unfortunately, overlook the modality differences due to different imaging mechanisms, so that they suffer from the reduced discriminability of the fused features. To address such an issue, we propose, for the first time, the strategy of bridging-then-fusing, where the innovation lies in a novel Adaptive-weighted Bi-directional Modality Difference Reduction Network (ABMDRNet). Concretely, a Modality Difference Reduction and Fusion (MDRF) subnetwork is designed, which first employs a bi-directional image-to-image translation based method to reduce the modality differences between RGB features and thermal features, and then adaptively selects those discriminative multi-modality features for RGB-T semantic segmentation in a channel-wise weighted fusion way. Furthermore, considering the importance of contextual information in semantic segmentation, a Multi-Scale Spatial Context (MSC) module and a Multi-Scale Channel Context (MCC) module are proposed to exploit the interactions among multi-scale contextual information of cross-modality features together with their long-range dependencies along spatial and channel dimensions, respectively. Comprehensive experiments on MFNet dataset demonstrate that our method achieves new state-of-the-art results.

关键词： Image segmentation Technological innovation computer vision Fuses Semantics Lighting Imaging

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Learning the Superpixel in a Non-iterative and Lifelong Manner

Learning the Superpixel in a Non-iterative and Lifelong Mann...

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

作者： Zhu, Lei She, Qi Zhang, Bin Lu, Yanye Lu, Zhilin Li, Duo Hu, Jie Peking Univ Hlth Sci Ctr Inst Med Technol Beijing Peoples R China Bytedance AI Lab Beijing Peoples R China Peking Univ Dept Biomed Engn Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Peking Univ Inst Biomed Engn Shenzhen Grad Sch Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Superpixel is generated by automatically clustering pixels in an image into hundreds of compact partitions, which is widely used to perceive the object contours for its excellent contour adherence. Although some works use the Convolution Neural Network (CNN) to generate high-quality superpixel, we challenge the design principles of these networks, specifically for their dependence on manual labels and excess computation resources, which limits their flexibility compared with the traditional unsupervised segmentation methods. We target at redefining the CNN-based superpixel segmentation as a lifelong clustering task and propose an unsupervised CNN-based method called LNS-Net. The LNS-Net can learn superpixel in a non-iterative and lifelong manner without any manual labels. Specifically, a lightweight feature embedder is proposed for LNS-Net to efficiently generate the cluster-friendly features. With those features, seed nodes can be automatically assigned to cluster pixels in a non-iterative way. Additionally, our LNS-Net can adapt the sequentially lifelong learning by rescaling the gradient of weight based on both channel and spatial context to avoid overfitting. Experiments show that the proposed LNS-Net achieves significantly better performance on three benchmarks with nearly ten times lower complexity compared with other state-of-the-art methods.

关键词： Image segmentation Convolution Neural networks Manuals Benchmark testing pattern recognition Finite element analysis

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Open-Set Representation Learning through Combinatorial Embedding

Open-Set Representation Learning through Combinatorial Embed...

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conference on computer vision and pattern recognition (cvpr)

作者： Geeho Kim Junoh Kang Bohyung Han Computer Vision Laboratory ECE Seoul National University IPAI Seoul National University

Visual recognition tasks are often limited to dealing with a small subset of classes simply because the labels for the remaining classes are unavailable. We are interested in identifying novel concepts in a dataset through representation learning based on both labeled and unlabeled examples, and extending the horizon of recognition to both known and novel classes. To address this challenging task, we propose a combinatorial learning approach, which naturally clusters the examples in unseen classes using the compositional knowledge given by multiple supervised meta-classifiers on heterogeneous label spaces. The representations given by the combinatorial embedding are made more robust by unsupervised pairwise relation learning. The proposed algorithm discovers novel concepts via a joint optimization for enhancing the discrimitiveness of unseen classes as well as learning the representations of known classes generalizable to novel ones. Our extensive experiments demonstrate remarkable performance gains by the proposed approach on public datasets for image retrieval and image categorization with novel class discovery.

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STMTrack: Template-free Visual Tracking with Space-time Memory Networks

STMTrack: Template-free Visual Tracking with Space-time Memo...

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

作者： Fu, Zhihong Liu, Qingjie Fu, Zehua Wang, Yunhong Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Beihang Univ Hangzhou Innovat Inst Beijing Peoples R China

ISBN: (纸本)9781665445092

Boosting performance of the offline trained siamese trackers is getting harder nowadays since the fixed information of the template cropped from the first frame has been almost thoroughly mined, but they are poorly capable of resisting target appearance changes. Existing trackers with template updating mechanisms rely on time-consuming numerical optimization and complex hand-designed strategies to achieve competitive performance, hindering them from real-time tracking and practical applications. In this paper, we propose a novel tracking framework built on top of a space-time memory network that is competent to make full use of historical information related to the target for better adapting to appearance variations during tracking. Specifically, a novel memory mechanism is introduced, which stores the historical information of the target to guide the tracker to focus on the most informative regions in the current frame. Furthermore, the pixel-level similarity computation of the memory network enables our tracker to generate much more accurate bounding boxes of the target. Extensive experiments and comparisons with many competitive trackers on challenging large-scale benchmarks, OTB-2015, TrackingNet, GOT-10k, LaSOT, UAV123, and VOT2018, show that, without bells and whistles, our tracker outperforms all previous state-of-the-art real-time methods while running at 37 FPS.

关键词： Visualization computer vision Target tracking Codes Adaptive systems Benchmark testing Boosting

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Explainable Noisy Label Flipping for Multi-Label Fashion Image Classification

Explainable Noisy Label Flipping for Multi-Label Fashion Ima...

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

作者： Ferreira, Beatriz Quintino Costeira, Joao P. Gomes, Joao P. Univ Lisbon ISR IST Lisbon Portugal

ISBN: (纸本)9781665448994

In online shopping applications, the daily insertion of new products requires an overwhelming annotation effort. Usually done by humans, it comes at a huge cost and yet generates high rates of noisy/missing labels that seriously hinder the effectiveness of CNNs in multi-label classification. We propose SELF-ML, a classification framework that exploits the relation between visual attributes and appearance together with the "low-rank" nature of the feature space. It learns a sparse reconstruction of image features as a convex combination of very few images - a basis - that are correctly annotated. Building on this representation, SELF-ML has a module that relabels noisy annotations from the derived combination of the clean data. Due to such structured reconstruction, SELF-ML gives an explanation of its label-flipping decisions. Experiments on a real-world shopping dataset show that SELF-ML significantly increases the number of correct labels even with few clean annotations.

关键词： Visualization computer vision Annotations conferences Buildings pattern recognition Noise measurement

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Tuning IR-cut Filter for Illumination-aware Spectral Reconstruction from RGB

Tuning IR-cut Filter for Illumination-aware Spectral Reconst...

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

作者： Sun, Bo Yan, Junchi Zhou, Xiao Zheng, Yinqiang Univ Southern Calif Los Angeles CA 90007 USA Shanghai Jiao Tong Univ Shanghai Peoples R China Anhui Normal Univ Wuhu Peoples R China Univ Tokyo Tokyo Japan

ISBN: (纸本)9781665445092

To reconstruct spectral signals from multi-channel observations, in particular trichromatic RGBs, has recently emerged as a promising alternative to traditional scanning-based spectral imager. It has been proven that the reconstruction accuracy relies heavily on the spectral response of the RGB camera in use. To improve accuracy, data-driven algorithms have been proposed to retrieve the best response curves of existing RGB cameras, or even to design brand new three-channel response curves. Instead, this paper explores the filter-array based color imaging mechanism of existing RGB cameras, and proposes to design the IR-cut filter properly for improved spectral recovery, which stands out as an in-between solution with better trade-off between reconstruction accuracy and implementation complexity. We further propose a deep learning based spectral reconstruction method, which allows to recover the illumination spectrum as well. Experiment results with both synthetic and real images under daylight illumination have shown the benefits of our IR-cut filter tuning method and our illumination-aware spectral reconstruction method.

关键词： Reflectivity Deep learning computer vision Lighting Reconstruction algorithms Filtering algorithms Cameras

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