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NL-FFC: Non-Local Fast Fourier Convolution for Image Super Resolution

NL-FFC: Non-Local Fast Fourier Convolution for Image Super R...

引用

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

作者： Sinha, Abhishek Kumar Moorthi, S. Manthira Dhar, Debajyoti Space Applicat Ctr Signal & Image Proc Grp Ahmadabad India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Deep neural networks have shown promising results in image super-resolution by learning a complex mapping from low resolution to high resolution image. However, most of the approaches learns to upsample by using convolution in spatial domain and are confined to local features. This results into restricting the receptive field of the network and therefore deteriorates the overall quality of the high-resolution image. To alleviate this issue, we propose an architecture that learns both local and global features, and fuses them together to generate high quality images. The network uses a non-local attention aided Fast Fourier Convolutions (NL-FFC) to widen the receptive field and learn long-range dependencies. The analyses further show that these Fourier features implicitly provide faster convergence on low frequency components only to learn prior for unobserved high frequency components. The model generalizes well to different datasets. We further investigate the role of non-local attention, and the ratio of local and global features to maximize the performance gain in the ablation study.

关键词： Deep learning Convolution Fuses Superresolution Neural networks Performance gain pattern recognition

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Class-wise Thresholding for Robust Out-of-Distribution Detection

Class-wise Thresholding for Robust Out-of-Distribution Detec...

引用

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

作者： Guarrera, Matteo Jin, Baihong Lin, Tung-Wei Zuluaga, Maria A. Chen, Yuxin Sangiovanni-Vincentelli, Alberto Univ Calif Berkeley Berkeley CA 94720 USA EURECOM Biot France Univ Chicago Chicago IL 60637 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We consider the problem of detecting Out-of-Distribution (OoD) input data when using deep neural networks, and we propose a simple yet effective way to improve the robustness of several popular OoD detection methods against label shift. Our work is motivated by the observation that most existing OoD detection algorithms consider all training/test data as a whole, regardless of which class entry each input activates (inter-class differences). Through extensive experimentation, we have found that such practice leads to a detector whose performance is sensitive and vulnerable to label shift. To address this issue, we propose a class-wise thresholding scheme that can apply to most existing OoD detection algorithms and can maintain similar OoD detection performance even in the presence of label shift in the test distribution.

关键词： Training Deep learning computer vision conferences Neural networks Training data Detectors

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Integrating Pose and Mask Predictions for Multi-person in Videos

Integrating Pose and Mask Predictions for Multi-person in Vi...

引用

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

作者： Heo, Miran Hwang, Sukjun Oh, Seoung Wug Lee, Joon-Young Kim, Seon Joo Yonsei Univ Seoul South Korea Adobe Res San Jose CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In real-world applications for video editing, humans are arguably the most important objects. When editing videos of humans, the efficient tracking of fine-grained masks and body joints is the fundamental requirement. In this paper, we propose a simple and efficient system for jointly tracking pose and segmenting high-quality masks for all humans in the video. We design a pipeline that globally tracks pose and locally segments fine-grained masks. Specifically, CenterTrack is first employed to track human poses by viewing the whole scene, and then the proposed local segmentation network leverages the pose information as a powerful query to carry out high-quality segmentation. Furthermore, we adopt a highly light-weight MLP-Mixer layer within the segmentation network that can efficiently propagate the query pose throughout the region of interest with minimal overhead. For the evaluation, we collect a new benchmark called KineMask which includes various appearances and actions. The experimental results demonstrate that our method has superior fine-grained segmentation performance. Moreover, it runs at 33 fps, achieving a great balance of speed and accuracy compared to the prevailing online Video Instance Segmentation methods.

关键词： computer vision conferences Computational modeling Pipelines Benchmark testing pattern recognition

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Cross Transferring Activity recognition to Word Level Sign Language Detection

Cross Transferring Activity Recognition to Word Level Sign L...

引用

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

作者： Radhakrishnan, Srijith Mohan, Nikhil C. Varma, Manisimha Varma, Jaithra Pai, Smitha N. Manipal Acad Higher Educ Manipal Inst Technol Dept Informat & Commun Technol Manipal 576104 Karnataka India Manipal Acad Higher Educ Manipal Inst Technol Dept Comp Sci & Engn Manipal 576104 Karnataka India Manipal Acad Higher Educ Manipal Inst Technol Dept Data Sci & Comp Applicat Manipal 576104 Karnataka India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The lack of large scale labelled datasets in word-level sign language recognition (WSLR) poses a challenge to detecting sign language from videos. Most WSLR approaches operate on datasets that do not model real-world settings very well, as they do not have a high degree of variability in terms of signers, background, lighting and inter signer variation. We chose the MS-ASL dataset to overcome these limitations as they model open-world settings very well. This paper benchmarks successful action recognition architectures on the MS-ASL dataset using transfer learning. We have achieved new state-of-the-art accuracy (92.35%) with an improvement of 7.03% over the previous state-of-the-art introduced by the MS-ASL paper. We have analyzed how action-recognition architectures fair in the task of WSLR, and we propose SlowFast 8x8 ResNet 101 as a robust and suitable architecture for the task of WSLR.

关键词： computer vision conferences Transfer learning Lighting Gesture recognition computer architecture Assistive technologies

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On-Sensor Binarized Fully Convolutional Neural Network for Localisation and Coarse Segmentation

On-Sensor Binarized Fully Convolutional Neural Network for L...

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

作者： Liu, Yanan Lu, Yao Shanghai Univ Sch Microelect Shanghai Peoples R China Univ Bristol Visual Informat Lab Bristol Avon England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Current neural networks are compatible with high-performance GPU/CPUs. However, implementing neural networks on emerging embedded sensor for inference is challenging due to sensor's unique hardware architecture and stringent computing resources. With this in mind, this work presents new methods to implement fully convolutional neural networks (FCNs) on Pixel Processor Array (PPA) sensors with many techniques to fully use the limited resources on sensor. Specifically, we, for the first time, design and train binarized FCN for both binary weights and activations using batchnorm, group convolution, and learnable threshold for binarization, producing networks small enough to be embedded on the focal plane of the PPA, with limited local memory resources, and using parallel elementary add/subtract, shifting, and bit operations only. We demonstrate the first implementation of an FCN on a PPA device, performing three convolution layers entirely in the pixel-level processors. We use this architecture to demonstrate inference generating heat maps for object segmentation and localisation at over 280 FPS using the SCAMP-5 PPA vision chip.

关键词： Performance evaluation Heating systems Convolution Neural networks computer architecture Object segmentation Parallel processing

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Spacing Loss for Discovering Novel Categories

Spacing Loss for Discovering Novel Categories

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

作者： Joseph, K. J. Paul, Sujoy Aggarwal, Gaurav Biswas, Soma Rai, Piyush Han, Kai Balasubramanian, Vineeth N. Indian Inst Technol Hyderabad Hyderabad India Google Res Bengaluru India Indian Inst Sci Bengaluru India Lndian Inst Technol Kanpur Kanpur Uttar Pradesh India Univ Hong Kong Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Novel Class Discovery (NCD) is a learning paradigm, where a machine learning model is tasked to semantically group instances from unlabeled data, by utilizing labeled instances from a disjoint set of classes. In this work, we first characterize existing NCD approaches into single-stage and two-stage methods based on whether they require access to labeled and unlabeled data together while discovering new classes. Next, we devise a simple yet powerful loss function that enforces separability in the latent space using cues from multi-dimensional scaling, which we refer to as Spacing Loss. Our proposed formulation can either operate as a standalone method or can be plugged into existing methods to enhance them. We validate the efficacy of Spacing Loss with thorough experimental evaluation across multiple settings on CIFAR-10 and CIFAR-100 datasets.

关键词： Training computer vision conferences Computational modeling Machine learning Benchmark testing Data models

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Continual Learning Based on OOD Detection and Task Masking

Continual Learning Based on OOD Detection and Task Masking

引用

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

作者： Kim, Gyuhak Esmaeilpour, Sepideh Xiao, Changnan Liu, Bing Univ Illinois Chicago IL 60607 USA ByteDance Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Existing continual learning techniques focus on either task incremental learning (TIL) or class incremental learning (CIL) problem, but not both. CIL and TIL differ mainly in that the task-id is provided for each test sample during testing for TIL, but not provided for CIL. Continual learning methods intended for one problem have limitations on the other problem. This paper proposes a novel unified approach based on out-of-distribution (OOD) detection and task masking, called CLOM, to solve both problems. The key novelty is that each task is trained as an OOD detection model rather than a traditional supervised learning model, and a task mask is trained to protect each task to prevent forgetting. Our evaluation shows that CLOM outperforms existing state-of-the-art baselines by large margins. The average TIL/CIL accuracy of CLOM over six experiments is 87.6/67.9% while that of the best baselines is only 84.4/55.0%.

关键词： Training computer vision Machine learning algorithms Codes conferences Supervised learning Data models

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Posture-based Infant Action recognition in the Wild with Very Limited Data

Posture-based Infant Action Recognition in the Wild with Ver...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Huang, Xiaofei Luan, Lingfei Hatamimajoumerd, Elaheh Wan, Michael Kakhaki, Pooria Daneshvar Obeid, Rita Ostadabbas, Sarah Northeastern University MA United States Case Western Reserve University OH United States

ISBN: (纸本)9798350302493

Automatic detection of infant actions from home videos could aid medical and behavioral specialists in the early detection of motor impairments in infancy. However, most computer vision approaches for action recognition are centered around adult subjects, following datasets and benchmarks in the field. In this work, we present a data-efficient pipeline for infant action recognition based on the idea of modeling an action as a time sequence consisting of two different stable postures with a transition period between them. The postures are detected frame-wise from the estimated 2D and 3D infant body poses and the action sequence is segmented based on the posture-driven low-dimensional features of each frame. To spur further research in the field, we also created and release the first-of-its-kind infant action dataset - InfAct - consisting of 200 fully annotated home videos representing a wide range of common infant actions, intended as a public benchmark. Among the ten more common classes of infant actions, our action recognition model achieved 78.0% accuracy when tested on InfAct, highlighting the promise of video-based infant action recognition as a viable monitoring tool for infant motor development1. © 2023 ieee.

关键词： computer vision

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Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization

Unsupervised Domain Adaptation for Cardiac Segmentation: Tow...

引用

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

作者： Lu, Changjie Zheng, Shen Gupta, Gaurav Wenzhou Kean Univ Coll Sci & Technol Wenzhou Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Unsupervised domain adaptation approaches have recently succeeded in various medical image segmentation tasks. The reported works often tackle the domain shift problem by aligning the domain-invariant features and minimizing the domain-specific discrepancies. That strategy works well when the difference between a specific domain and between different domains is slight. However, the generalization ability of these models on diverse imaging modalities remains a significant challenge. This paper introduces UDA-VAE++, an unsupervised domain adaptation framework for cardiac segmentation with a compact loss function lower bound. To estimate this new lower bound, we develop a novel Structure Mutual Information Estimation (SMIE) block with a global estimator, a local estimator, and a prior information matching estimator to maximize the mutual information between the reconstruction and segmentation tasks. Specifically, we design a novel sequential reparameterization scheme that enables information flow and variance correction from the low-resolution latent space to the high-resolution latent space. Comprehensive experiments on benchmark cardiac segmentation datasets demonstrate that our model outperforms previous state-of-the-art qualitatively and quantitatively.

关键词： Image segmentation Adaptation models conferences Estimation Benchmark testing pattern recognition Task analysis

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GraphWalks: Efficient Shape Agnostic Geodesic Shortest Path Estimation

GraphWalks: Efficient Shape Agnostic Geodesic Shortest Path ...

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

作者： Potamias, Rolandos Alexandros Neofytou, Alexandros Bintsi, Kyriaki Margarita Zafeiriou, Stefanos Imperial Coll London London England OCM Digital Media Athens Greece

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Geodesic paths and distances are among the most popular intrinsic properties of 3D surfaces. Traditionally, geodesic paths on discrete polygon surfaces were computed using shortest path algorithms, such as Dijkstra. However, such algorithms have two major limitations. They are non-differentiable which limits their direct usage in learnable pipelines and they are considerably time demanding. To address such limitations and alleviate the computational burden, we propose a learnable network to approximate geodesic paths. The proposed method is comprised by three major components: a graph neural network that encodes node positions in a high dimensional space, a path embedding that describes previously visited nodes and a point classifier that selects the next point in the path. The proposed method provides efficient approximations of the shortest paths and geodesic distances estimations. Given that all of the components of our method are fully differentiable, it can be directly plugged into any learnable pipeline as well as customized under any differentiable constraint. We extensively evaluate the proposed method with several qualitative and quantitative experiments.

关键词： computer vision Three-dimensional displays Shape conferences Pipelines Estimation Approximation algorithms

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