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GAF-NAU: Gramian Angular Field encoded Neighborhood Attention U-Net for Pixel-Wise Hyperspectral Image Classification

GAF-NAU: Gramian Angular Field encoded Neighborhood Attentio...

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

作者： Paheding, Sidike Reyes, Abel A. Kasaragod, Anush Oommen, Thomas Michigan Technol Univ Houghton MI 49931 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Hyperspectral image (HSI) classification is the most vibrant area of research in the hyperspectral community due to the rich spectral information contained in HSI can greatly aid in identifying objects of interest. However, inherent non-linearity between materials and the corresponding spectral profiles brings two major challenges in HSI classification: interclass similarity and intraclass variability. Many advanced deep learning methods have attempted to address these issues from the perspective of a region/patch-based approach, instead of a pixel-based alternate. However, the patch-based approaches hypothesize that neighborhood pixels of a target pixel in a fixed spatial window belong to the same class. And this assumption is not always true. To address this problem, we herein propose a new deep learning architecture, namely Gramian Angular Field encoded Neighborhood Attention U-Net (GAF-NAU), for pixel-based HSI classification. The proposed method does not require regions or patches centered around a raw target pixel to perform 2D-CNN based classification, instead, our approach transforms 1D pixel vector in HSI into 2D angular feature space using Gramian Angular Field (GAF) and then embed it to a new neighborhood attention network to suppress irrelevant angular feature while emphasizing on pertinent features useful for HSI classification task. Evaluation results on three publicly available HSI datasets demonstrate the superior performance of the proposed model. The source code available at https://***/MAIN-Lab/GAF-NAU/

关键词： Training Deep learning Transforms Logic gates pattern recognition Object recognition Task analysis

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ImageSig: A signature transform for ultra-lightweight image recognition

ImageSig: A signature transform for ultra-lightweight image ...

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

作者： Ibrahim, Mohamed R. Lyons, Terry Alan Turing Inst London England Univ Oxford Math Inst Oxford England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper introduces a new lightweight method for image recognition. ImageSig is based on computing signatures and does not require a convolutional structure or an attention-based encoder. It is striking to the authors that it achieves: a) an accuracy for 64 X 64 RGB images that exceeds many of the state-of-the-art methods and simultaneously b) requires orders of magnitude less FLOPS, power and memory footprint. The pretrained model can be as small as 44.2 KB in size. ImageSig shows unprecedented performance on hardware such as Raspberry Pi and Jetson-nano. ImageSig treats images as streams with multiple channels. These streams are parameterized by spatial directions. We contribute to the functionality of signature and rough path theory to stream-like data and vision tasks on static images beyond temporal streams. With very few parameters and small size models, the key advantage is that one could have many of these "detectors" assembled on the same chip;moreover, the feature acquisition can be performed once and shared between different models of different tasks - further accelerating the process. This contributes to energy efficiency and the advancements of embedded AI at the edge.

关键词： Image edge detection Memory management Transforms Detectors Streaming media Feature extraction Hardware

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A Joint Cross-Attention Model for Audio-Visual Fusion in Dimensional Emotion recognition

A Joint Cross-Attention Model for Audio-Visual Fusion in Dim...

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

作者： Praveen, R. Gnana de Melo, Wheidima Carneiro Ullah, Nasib Aslam, Haseeb Zeeshan, Osama Denorme, Theo Pedersoli, Marco Koerich, Alessandro L. Bacon, Simon Cardinal, Patrick Granger, Eric Ecole Technol Super LIVIA Montreal PQ Canada Concordia Univ Dept Hlth Kinesiol & Appl Physiol Montreal PQ Canada

ISBN: (纸本)9781665487399

Multimodal emotion recognition has recently gained much attention since it can leverage diverse and complementary modalities, such as audio, visual, and biosignals. However, most state-of-the-art audio-visual (A-V) fusion methods rely on recurrent networks or conventional attention mechanisms that do not effectively leverage the complementary nature of A-V modalities. This paper focuses on dimensional emotion recognition based on the fusion of facial and vocal modalities extracted from videos. We propose a joint cross-attention fusion model that can effectively exploit the complementary inter-modal relationships, allowing for an accurate prediction of valence and arousal. In particular, this model computes cross-attention weights based on the correlation between joint feature representations and individual modalities. By deploying a joint A-V feature representation into the cross-attention module, the performance of our fusion model improves significantly over the vanilla cross-attention module. Experimental results(1) on the AffWild2 dataset highlight the robustness of our proposed A-V fusion model. It has achieved a concordance correlation coefficient (CCC) of 0.374 (0.663) and 0.363 (0.584) for valence and arousal, respectively, on the test set (validation set). This represents a significant improvement over the baseline for the third challenge of Affective Behavior Analysis in-the-Wild 2022 (ABAW3) competition, with a CCC of 0.180 (0.310) and 0.170 (0.170).

关键词： Correlation coefficient Emotion recognition Visualization Correlation Computational modeling Predictive models Feature extraction

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Simple and Efficient Architectures for Semantic Segmentation

Simple and Efficient Architectures for Semantic Segmentation

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

作者： Mehta, Dushyant Skliar, Andrii Ben Yahia, Haitam Borse, Shubhankar Porikli, Fatih Habibian, Amirhossein Blankevoort, Tijmen Qualcomm AI Res San Diego CA 92121 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Though the state-of-the architectures for semantic segmentation, such as HRNet, demonstrate impressive accuracy, the complexity arising from their salient design choices hinders a range of model acceleration tools, and further they make use of operations that are inefficient on current hardware. This paper demonstrates that a simple encoder-decoder architecture with a ResNet-like backbone and a small multi-scale head, performs on-par or better than complex semantic segmentation architectures such as HRNet, FANet and DDRNets. Naively applying deep backbones designed for Image Classification to the task of Semantic Segmentation leads to sub-par results, owing to a much smaller effective receptive field of these backbones. Implicit among the various design choices put forth in works like HRNet, DDRNet, and FANet are networks with a large effective receptive field. It is natural to ask if a simple encoder-decoder architecture would compare favorably if comprised of backbones that have a larger effective receptive field, though without the use of inefficient operations like dilated convolutions. We show that with minor and inexpensive modifications to ResNets, enlarging the receptive field, very simple and competitive baselines can be created for Semantic Segmentation. We present a family of such simple architectures for desktop as well as mobile targets, which match or exceed the performance of complex models on the Cityscapes dataset. We hope that our work provides simple yet effective baselines for practitioners to develop efficient semantic segmentation models. The model definitions and pre-trained weights are available at https://***/Qualcomm-AI-research/FFNet.

关键词： Image segmentation Head Semantics Graphics processing units computer architecture Hardware Distance measurement

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ARIA: Adversarially Robust Image Attribution for Content Provenance

ARIA: Adversarially Robust Image Attribution for Content Pro...

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

作者： Andriushchenko, Maksym Li, Xiaoyang Rebecca Oxholm, Geoffrey Gittings, Thomas Bui, Tu Flammarion, Nicolas Collomosse, John EPFL Vaud Switzerland Adobe Res San Jose CA USA Univ Surrey Guildford GU2 5XH Surrey England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Image attribution - matching an image back to a trusted source - is an emerging tool in the fight against online misinformation. Deep visual fingerprinting models have recently been explored for this purpose. However, they are not robust to tiny input perturbations known as adversarial examples. First we illustrate how to generate valid adversarial images that can easily cause incorrect image attribution. Then we describe an approach to prevent imperceptible adversarial attacks on deep visual fingerprinting models, via robust contrastive learning. The proposed training procedure leverages training on l(infinity)-bounded adversarial examples, it is conceptually simple and incurs only a small computational overhead. The resulting models are substantially more robust, are accurate even on unperturbed images, and perform well even over a database with millions of images. In particular, we achieve 91.6% standard and 85.1% adversarial recall under l(infinity)-bounded perturbations on manipulated images compared to 80.1% and 0.0% from prior work. We also show that robustness generalizes to other types of imperceptible perturbations unseen during training. Finally, we show how to train an adversarially robust image comparator model for detecting editorial changes in matched images. Project page: https://***/robust_image_attribution.

关键词： Training Location awareness Heating systems Visualization Perturbation methods Computational modeling Fingerprint recognition

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Self-Attention with Convolution and Deconvolution for Efficient Eye Gaze Estimation from a Full Face Image

Self-Attention with Convolution and Deconvolution for Effici...

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

作者： Oh, Jun O. Chang, Hyung Jin Choi, Sang-Il Dankook Univ Comp Engn Yongin South Korea Univ Birmingham Sch Comp Sci Birmingham W Midlands England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper proposes a whole new face image-based eye gaze estimation network to solve low generalization performance. Due to the high variance of facial appearance and environmental conditions, conventional methods in gaze estimation have low generalization performance and are easily overfitted to training subjects. To solve this problem, we adopt a self-attention mechanism that has better generalization performance. Nevertheless, applying self-attention directly to an image incurs a high computational cost. Thus, we introduce a new projection that uses convolution in the entire face image to accurate model the local context and reduce the computational cost of self-attention. The proposed model also includes deconvolution that transforms the down-sampled global context to the same size as the input so that spatial information is not lost. We confirmed through observations that the new method achieved state of the art on the EYEDIAP, MPIIFaceGaze, Gaze360 and RT-GENE datasets and achieved a performance increase of 0.02 degrees to 0.30 degrees compared to the other state of the art model. In addition, we show the generalization performance of the proposed model through a cross-dataset evaluation.

关键词： Training Deconvolution Convolution Computational modeling Face recognition Estimation Transforms

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UIGR: Unified Interactive Garment Retrieval

UIGR: Unified Interactive Garment Retrieval

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

作者： Han, Xiao He, Sen Zhang, Li Song, Yi-Zhe Xiang, Tao Univ Surrey CVSSP Guildford Surrey England iFlyTek Surrey Joint Res Ctr Artificial Intellige Guildford Surrey England Fudan Univ Sch Data Sci Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Interactive garment retrieval (IGR) aims to retrieve a target garment image based on a reference garment image along with user feedback on what to change on the reference garment. Two IGR tasks have been studied extensively: text-guided garment retrieval (TGR) and visually compatible garment retrieval (VCR). The user feedback for the former indicates what semantic attributes to change with the garment category preserved, while the category is the only thing to be changed explicitly for the latter, with an implicit requirement on style preservation. Despite the similarity between these two tasks and the practical need for an efficient system tackling both, they have never been unified and modeled jointly. In this paper, we propose a Unified Interactive Garment Retrieval (UIGR) framework to unify TGR and VCR. To this end, we first contribute a large-scale benchmark suited for both problems. We further propose a strong baseline architecture to integrate TGR and VCR in one model. Extensive experiments suggest that unifying two tasks in one framework is not only more efficient by requiring a single model only, it also leads to better performance. Code and datasets are available at GitHub.

关键词： Computational modeling Clothing Semantics computer architecture Benchmark testing Multitasking pattern recognition

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Self-Calibrated Efficient Transformer for Lightweight Super-Resolution

Self-Calibrated Efficient Transformer for Lightweight Super-...

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

作者： Zou, Wenbin Ye, Tian Zheng, Weixin Zhang, Yunchen Chen, Liang Wu, Yi Fujian Normal Univ Fujian Prov Key Lab Photon Technol Fuzhou Peoples R China Jimei Univ Sch Ocean Informat Engn Xiamen Peoples R China Fuzhou Univ Coll Phys & Informat Engn Fuzhou Peoples R China China Design Grp Co Ltd Nanjing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recently, deep learning has been successfully applied to the single-image super-resolution (SISR) with remarkable performance. However, most existing methods focus on building a more complex network with a large number of layers, which can entail heavy computational costs and memory storage. To address this problem, we present a lightweight Self-Calibrated Efficient Transformer (SCET) network to solve this problem. The architecture of SCET mainly consists of the self-calibrated module and efficient transformer block, where the self-calibrated module adopts the pixel attention mechanism to extract image features effectively. To further exploit the contextual information from features, we employ an efficient transformer to help the network obtain similar features over long distances and thus recover sufficient texture details. We provide comprehensive results on different settings of the overall network. Our proposed method achieves more remarkable performance than baseline methods.

关键词： Deep learning Visualization computer vision conferences Superresolution computer architecture Transformers

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Regression or Classification? Reflection on BP prediction from PPG data using Deep Neural Networks in the scope of practical applications

Regression or Classification? Reflection on BP prediction fr...

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

作者： Schrumpf, Fabian Serdack, Paul Rudi Fuchs, Mirco Leipzig Univ Appl Sci Leipzig Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Photoplethysmographic (PPG) signals offer diagnostic potential beyond heart rate analysis or blood oxygen level monitoring. In the recent past, research focused extensively on non-invasive PPG-based approaches to blood pressure (BP) estimation. These approaches can be subdivided into regression and classification methods. The latter assign PPG signals to predefined BP intervals that represent clinically relevant ranges. The former predict systolic (SBP) and diastolic (DBP) BP as continuous variables and are of particular interest to the research community. However, the reported accuracies of BP regression methods vary widely among publications with some authors even questioning the feasibility of PPG-based BP regression altogether. In our work, we compare BP regression and classification approaches. We argue that BP classification might provide diagnostic value that is equivalent to regression in many clinically relevant scenarios while being similar or even superior in terms of performance. We compare several established neural architectures using publicly available PPG data for SBP regression and classification with and without personalization using subject-specific data. We found that classification and regression models perform similar before personalization. However, after personalization, the accuracy of classification based methods outperformed regression approaches. We conclude that BP classification might be preferable over BP regression in certain scenarios where a coarser segmentation of the BP range is sufficient.

关键词： Heart rate Deep learning computer vision conferences Neural networks Estimation computer architecture

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Blueprint Separable Residual Network for Efficient Image Super-Resolution

Blueprint Separable Residual Network for Efficient Image Sup...

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

作者： Li, Zheyuan Liu, Yingqi Chen, Xiangyu Cai, Haoming Gu, Jinjin Qiao, Yu Dong, Chao Chinese Acad Sci ShenZhen Key Lab Comp Vis & Pattern Recognit Shenzhen Inst Adv Technol SIAT SenseTime Joint Lab Beijing Peoples R China Univ Macau Taipa Macao Peoples R China Shanghai AI Lab Shanghai Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent advances in single image super-resolution (SISR) have achieved extraordinary performance, but the computational cost is too heavy to apply in edge devices. To alleviate this problem, many novel and effective solutions have been proposed. Convolutional neural network (CNN) with the attention mechanism has attracted increasing attention due to its efficiency and effectiveness. However, there is still redundancy in the convolution operation. In this paper, we propose Blueprint Separable Residual Network (BSRN) containing two efficient designs. One is the usage of blueprint separable convolution (BSConv), which takes place of the redundant convolution operation. The other is to enhance the model ability by introducing more effective attention modules. The experimental results show that BSRN achieves state-of-the-art performance among existing efficient SR methods. Moreover, a smaller variant of our model BSRN-S won the first place in model complexity track of NTIRE 2022 Efficient SR Challenge. The code is available at https://***/xiaom233/BSRN.

关键词： Performance evaluation Convolution Computational modeling Superresolution Redundancy Complexity theory pattern recognition

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