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Doubling down: sparse grounding with an additional, almost-matching caption for detection-oriented multimodal pretraining

Doubling down: sparse grounding with an additional, almost-m...

引用

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

作者： Nebbia, Giacomo Kovashka, Adriana Univ Pittsburgh Pittsburgh PA 15260 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

A common paradigm in deep learning applications for computer vision is self-supervised pretraining followed by supervised fine-tuning on a target task. In the self-supervision step, a model is trained in a supervised fashion, but the source of supervision needs to be implicitly defined by the data. Image-caption alignment is often used as such a source of implicit supervision in multimodal pretraining, and grounding (i.e., matching word tokens with visual tokens) is one way to exploit it. We introduce a strategy to take advantage of an underexplored structure in image-caption datasets: the relationship between captions matched with different images but mentioning the same objects. Given an image-caption pair, we find an additional caption that mentions one of the objects the first caption mentions, and we impose a sparse grounding between the image and the second caption so that only a few word tokens are grounded in the image. Our goal is to learn a better feature representation for the objects mentioned by both captions, encouraging grounding between the additional caption and the image to focus on the common objects only. We report superior grounding performance when comparing our approach with a previously-published pretraining strategy, and we show the benefit of our proposed double-caption grounding on two downstream detection tasks: supervised detection and open-vocabulary detection.

关键词： Deep learning computer vision Visualization Grounding conferences Computational modeling Data models

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Doppelganger Saliency: Towards More Ethical Person Re-Identification

Doppelganger Saliency: Towards More Ethical Person Re-Identi...

引用

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

作者： RichardWebster, Brandon Hu, Brian Fieldhouse, Keith Hoogs, Anthony Kitware Inc 1712 Route 9Suite 300 Clifton Pk NY 12065 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Modern surveillance systems have become increasingly dependent on artificial intelligence to provide actionable information for real-time decision making. A critical question relates to how these systems handle difficult ethical dilemmas, such as the re-identification of similar looking individuals. Potential misidentification of individuals can have severe negative consequences, as evidenced by recent headlines of individuals who were wrongly targeted for crimes they did not commit based on false matches. A computer vision-based saliency algorithm is proposed to help identify pixel-level differences in pairs of images containing visually similar individuals, which we term "doppelgangers." The computed saliency maps can alert human users of the presence of doppelg angers and provide important visual evidence to reduce the potential of false matches in these high-stakes situations. We show both qualitative and quantitative saliency results on doppelg angers found in a video-based person re-identification dataset (MARS) using three different state-of-the-art models. Our results suggest that this novel use of visual saliency can improve overall outcomes by helping human users in the person re-identification setting, while assuring the ethical and trusted operation of surveillance systems.

关键词： Ethics Visualization computer vision Surveillance conferences Computational modeling Decision making

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Exploring Robustness Connection between Artificial and Natural Adversarial Examples

Exploring Robustness Connection between Artificial and Natur...

引用

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

作者： Agarwal, Akshay Ratha, Nalini Vatsa, Mayank Singh, Richa Buffalo Buffalo NY 14260 USA IIT Jodhpur Jodhpur Rajasthan India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Although recent deep neural network algorithm has shown tremendous success in several computer vision tasks, their vulnerability against minute adversarial perturbations has raised a serious concern. In the early days of crafting these adversarial examples, artificial noises are optimized through the network and added in the images to decrease the confidence of the classifiers against the true class. However, recent efforts are showcasing the presence of natural adversarial examples which can also be effectively used to fool the deep neural networks with high confidence. In this paper, for the first time, we have raised the question that whether there is any robustness connection between artificial and natural adversarial examples. The possible robustness connection between natural and artificial adversarial examples is studied in the form that whether an adversarial example detector trained on artificial examples can detect the natural adversarial examples. We have analyzed several deep neural networks for the possible detection of artificial and natural adversarial examples in seen and unseen settings to set up a robust connection. The extensive experimental results reveal several interesting insights to defend the deep classifiers whether vulnerable against natural or artificially perturbed examples. We believe these findings can pave a way for the development of unified resiliency because defense against one attack is not sufficient for real-world use cases.

关键词： Deep learning computer vision Neural networks Semantics Transformers Robustness Convolutional neural networks

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MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients

MPAF: Model Poisoning Attacks to Federated Learning based on...

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

作者： Cao, Xiaoyu Gong, Neil Zhenqiang Duke Univ Durham NC 27706 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Existing model poisoning attacks to federated learning assume that an attacker has access to a large fraction of compromised genuine clients. However, such assumption is not realistic in production federated learning systems that involve millions of clients. In this work, we propose the first Model Poisoning Attack based on Fake clients called MPAF. Specifically, we assume the attacker injects fake clients to a federated learning system and sends carefully crafted fake local model updates to the cloud server during training, such that the learnt global model has low accuracy for many indiscriminate test inputs. Towards this goal, our attack drags the global model towards an attacker-chosen base model that has low accuracy. Specifically, in each round of federated learning, the fake clients craft fake local model updates that point to the base model and scale them up to amplify their impact before sending them to the cloud server. Our experiments show that MPAF can significantly decrease the test accuracy of the global model, even if classical defenses and norm clipping are adopted, highlighting the need for more advanced defenses.

关键词： Training computer vision Computational modeling conferences Production Collaborative work pattern recognition

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Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning

Adaptive Differential Filters for Fast and Communication-Eff...

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

作者： Becking, Daniel Kirchhoffer, Heiner Tech, Gerhard Haase, Paul Mueller, Karsten Schwarz, Heiko Samek, Wojciech Fraunhofer Heinrich Hertz Inst HHI Berlin Germany

ISBN: (纸本)9781665487399

Federated learning (FL) scenarios inherently generate a large communication overhead by frequently transmitting neural network updates between clients and server. To minimize the communication cost, introducing sparsity in conjunction with differential updates is a commonly used technique. However, sparse model updates can slow down convergence speed or unintentionally skip certain update aspects, e.g., learned features, if error accumulation is not properly addressed. In this work, we propose a new scaling method operating at the granularity of convolutional filters which 1) compensates for highly sparse updates in FL processes, 2) adapts the local models to new data domains by enhancing some features in the filter space while diminishing others and 3) motivates extra sparsity in updates and thus achieves higher compression ratios, i.e., savings in the overall data transfer. Compared to unscaled updates and previous work, experimental results on different computer vision tasks (Pascal VOC, CIFAR10, Chest X-Ray) and neural networks (ResNets, MobileNets, VGGs) in uni-, bidirectional and partial update FL settings show that the proposed method improves the performance of the central server model while converging faster and reducing the total amount of transmitted data by up to 377x.

关键词： computer vision Adaptation models Computational modeling Pipelines Neural networks Collaborative work Data models

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Remote Estimation of Continuous Blood Pressure by a Convolutional Neural Network Trained on Spatial patterns of Facial Pulse Waves

Remote Estimation of Continuous Blood Pressure by a Convolut...

引用

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

作者： Iuchi, Kaito Miyazaki, Ryogo Cardoso, George C. Ogawa-Ochiai, Keiko Tsumura, Norimichi Chiba Univ Grad Sch Sci & Engn Dept Imaging Sci Chiba Japan Univ Sao Paulo Phys Dept FFCLRP Sao Paulo Brazil Hiroshima Univ Hosp Dept Gen Med Hiroshima Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose a remote method to estimate continuous blood pressure based on spatial information of a pulse wave at a single point in time. By setting regions of interest to cover a face in a mutually exclusive and collectively exhaustive manner, RGB facial video is converted into a spatial pulse wave signal. The spatial pulse wave signal is converted into spatial signals of contours of each segmented pulse beat and relationships of each segmented pulse beat. The spatial signal is represented as a time-continuous value based on a representation of a pulse contour in a time axis and a phase axis and an interpolation along with the time axis. A relationship between the spatial signals and blood pressure is modeled by a convolutional neural network. A dataset was built to demonstrate the effectiveness of the proposed method. The dataset consists of continuous blood pressure and facial RGB videos of ten healthy volunteers. A comparison of conventional methods with the proposed method shows superior error for the latter. The results show an adequate estimation of the performance of the proposed method, when compared to the ground truth in mean blood pressure, in both the correlation coefficient (0.85) and mean absolute error (5.4 mmHg).

关键词： Correlation coefficient Interpolation computer vision Face recognition conferences Estimation Blood pressure

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Thermal Image Super-Resolution Challenge Results - PBVS 2022

Thermal Image Super-Resolution Challenge Results - PBVS 2022

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

作者： Rivadeneira, Rafael E. Sappa, Angel D. Vintimilla, Boris X. Kim, Jin Kim, Dogun Li, Zhihao Jian, Yingchun Yan, Bo Cao, Leilei Qi, Fengliang Wang, Hongbin Wu, Rongyuan Sun, Lingchen Zhao, Yongqiang Li, Lin Wang, Kai Wang, Yicheng Zhang, Xuanming Wei, Huiyuan Lv, Chonghua Sun, Qigong Tian, Xiaolin Jia, Zhuang Hu, Jiakui Wang, Chenyang Zhong, Zhiwei Liu, Xianming Jiang, Junjun Escuela Super Politecn Litoral ESPOL Guayaquil Ecuador Comp Vision Ctr Campus UAB Bellaterra 08193 Spain

ISBN: (纸本)9781665487399

This paper presents results from the third Thermal Image Super-Resolution (TISR) challenge organized in the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop. The challenge uses the same thermal image dataset as the first two challenges, with 951 training images and 50 validation images at each resolution. A set of 20 images was kept aside for testing. The evaluation tasks were to measure the PSNR and SSIM between the SR image and the ground truth (HR thermal noisy image downsampled by four), and also to measure the PSNR and SSIM between the SR image and the semi-registered HR image (acquired with another camera). The results outperformed those from last year's challenge, improving both evaluation metrics. This year, almost 100 teams participants registered for the challenge, showing the community's interest in this hot topic.

关键词： Training Deep learning conferences Superresolution Transformers Particle measurements pattern recognition

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Multi-modal Aerial View Object Classification Challenge Results - PBVS 2022

Multi-modal Aerial View Object Classification Challenge Resu...

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

作者： Low, Spencer Nina, Oliver Sappa, Angel D. Blasch, Erik Brigham Young Univ Provo UT 84602 USA Air Force Res Lab Dayton OH USA ESPOL Polytech Univ Ecuador Comp Vision Ctr Guayaquil Ecuador Air Force Off Sci Res Arlington VA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper details the results and main findings of the second iteration of the Multi-modal Aerial View Object Classification (MAVOC) challenge. The primary goal of both MAVOC challenges is to inspire research into methods for building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) imagery. Teams are encouraged to develop multi-modal approaches that incorporate complementary information from both domains. While the 2021 challenge showed a proof of concept that both modalities could be used together, the 2022 challenge focuses on the detailed multi-modal methods. The 2022 challenge uses the same UNIfied COincident Optical and Radar for recognition (UNICORN) dataset and competition format that was used in 2021. Specifically, the challenge focuses on two tasks, (1) SAR classification and (2) SAR + EO classification. The bulk of this document is dedicated to discussing the top performing methods and describing their performance on our blind test set. Notably, all of the top ten teams outperform a Resnet-18 baseline. For SAR classification, the top team showed a 129% improvement over baseline and an 8% average improvement from the 2021 winner. The top team for SAR + EO classification shows a 165% improvement with a 32% average improvement over 2021.

关键词： Training Laser radar Image recognition Radar imaging Optical imaging pattern recognition Task analysis

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ConvMLP: Hierarchical Convolutional MLPs for vision

ConvMLP: Hierarchical Convolutional MLPs for Vision

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

作者： Li, Jiachen Hassani, Ali Walton, Steven Shi, Humphrey SHI Lab University of Oregon UIUC United States

ISBN: (纸本)9798350302493

MLP-based architectures, which consist of a sequence of consecutive multi-layer perceptron blocks, have recently been found to reach comparable results to convolutional and transformer-based methods on image classification. However, most methods adopt spatial MLPs which take fixed-dimension inputs, therefore making it difficult to apply them as backbones to downstream tasks such as object detection and semantic segmentation, which require inputs with arbitrary dimension. Moreover, single-stage designs further limit the performance in other computer vision tasks and fully-connected layers bear heavy computation. To tackle these problems, we propose ConvMLP: a Hierarchical Convolutional MLP for visual recognition, which is a lightweight, stage-wise, co-design of convolution layers, and MLPs. In particular, ConvMLP-S achieves 76.8% top-1 accuracy on ImageNet-1k with 9M parameters and 2.4 GMACs (15% and 19% of MLP-Mixer-B/16, respectively). Experiments on object detection and semantic segmentation further show that visual representation learned by ConvMLP can be seamlessly transferred to downstream tasks and achieve competitive results with fewer parameters. Our code and pre-trained models are open-sourced at https://***/SHI-Labs/Convolutional-MLPs. © 2023 ieee.

关键词： Object detection

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VISTA: vision Transformer enhanced by U-Net and Image Colorfulness Frame Filtration for Automatic Retail Checkout

VISTA: Vision Transformer enhanced by U-Net and Image Colorf...

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

作者： Shihab, Md Istiak Hossain Tasnim, Nazia Zunair, Hasib Rupty, Labiba Kanij Mohammed, Nabeel Shahjalal Univ Sci & Technol Sylhet Bangladesh Giga Tech Ltd Dhaka Bangladesh Concordia Univ Montreal PQ Canada North South Univ Dhaka Bangladesh

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Multi-class product counting and recognition identifies product items from images or videos for automated retail checkout. The task is challenging due to the real-world scenario of occlusions where product items overlap, fast movement in conveyor belt, large similarity in overall appearance of the items being scanned, novel products, the negative impact of misidentifying items. Further there is a domain bias between training and test sets, specifically the provided training dataset consists of synthetic images and the test set videos consist of foreign objects such as hands and tray. To address these aforementioned issues, we propose to segment and classify individual frames from a video sequence. The segmentation method consists of a unified single product item- and hand-segmentation followed by entropy masking to address the domain bias problem. The multi-class classification method is based on vision Transformers (ViT). To identify the frames with target objects, we utilize several image processing methods and propose a custom metric to discard frames not having any product items. Combining all these mechanisms, our best system achieves 3rd place in the AI City Challenge 2022 Track 4 with F1 score of 0.4545.

关键词： Measurement Training Image segmentation Urban areas Video sequences Transformers Entropy

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