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Signature Detection, Restoration, and Verification: A Novel Chinese Document Signature Forgery Detection Benchmark

Signature Detection, Restoration, and Verification: A Novel ...

引用

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

作者： Yan, Kaihong Zhang, Ying Tang, Haoran Ren, Chengkai Zhang, Jian Wang, Gaoang Wang, Hongwei Zhejiang Univ Zhejiang Univ Univ Illinois Urbana Champaign Inst Haining 314400 Zhejiang Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Offline signature forgery detection has attracted many researchers in recent years. In real situations, signatures should be detected from the signed documents and verified by the forgery detection system. There are many challenges in the pipeline. First, some signatures have low resolutions and are difficult to be detected. Second, the cropped signatures may contain irrelevant background context of the document, making the signature hard to be verified. Third, some forgery signatures are very similar to genuine ones, increasing the challenge of verification. In addition, most existing datasets do not cover all the pipeline tasks. Moreover, publicly available Chinese-based signature datasets are rare for research purposes. In this paper, we construct a novel Chinese document offline signature forgery detection benchmark, namely ChiSig, which includes all pipeline tasks, i.e., signature detection, restoration, and verification. Besides, we extensively compare different deep learning-based approaches in these three tasks. The results show that our proposed dataset can effectively provide solutions for constructing pipeline systems for Chinese document signature forgery detection.

关键词： computer vision conferences Pipelines Benchmark testing Forgery Task analysis

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Mitigating Paucity of Data in Sinusoid Characterization Using Generative Synthetic Noise

Mitigating Paucity of Data in Sinusoid Characterization Usin...

引用

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

作者： Sattarzadeh, Sam Shalmani, Shervin Manzuri Azad, Shervin Goldspot Discoveries Corp Montreal PQ Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Although the remarkable breakthrough offered by Deep Learning (DL) models in numerous computer vision tasks, the need to acquire large amounts of high-quality natural data and fine-grained annotations is a shortcoming that fundamentally increases the cost and time devoted to training these models in real-world applications. Hence, synthetic datasets are considered reliable alternatives that can reduce the data acquisition by replacing or merging with natural data or effective pre-training of the models. To this end, in this work, we propose a novel approach to integrate structural data structures with the synthetic noise structures learned by unsupervised models that mimic the noise structures in natural data. Based on the proposed approach, we introduce the Sinusoid Feature recognition (SFR) dataset, which contains hard-to-detect fixed-period sinusoid waves. While the previous works in this regard use generative models to sample synthetic data to inflate the training set, we instead apply unsupervised learning models to generate deep synthetic noise which makes training models in the proposed dataset more challenging. We evaluate the segmentation, image reconstruction, and sinusoid characterization models pre-trained or fully trained on the synthetic SFR dataset on a private dataset of grayscale Acoustic Tele-Viewer (ATV) images. Experimental results show that supervision on our proposed synthetic dataset can improve the accuracy of the models by 3-4% via pre-training, and by 17-27% via ad-hoc training while dealing with challenging, realistic real-world images.

关键词： Training Deep learning Image segmentation computer vision Costs Computational modeling Data acquisition

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Pseudo-label Generation and Various Data Augmentation for Semi-Supervised Hyperspectral Object Detection

Pseudo-label Generation and Various Data Augmentation for Se...

引用

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

作者： Yu, Jun Zhang, Liwen Du, Shenshen Chang, Hao Lu, Keda Zhang, Zhong Yu, Ye Wang, Lei Ling, Qiang Univ Sci & Technol China Hefei Anhui Peoples R China Hefei ZhanDa Intelligence Technol Co Ltd Hefei Anhui Peoples R China Hefei Univ Technol Hefei Anhui Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Semi-supervised learning is a highly researched problem, but existing semi-supervised object detection frameworks are based on RGB images, and existing pre-trained models cannot be used for hyperspectral images. To overcome these difficulties, this paper first select fewer but suitable data augmentation methods to improve the accuracy of the supervised model based on the labeled training set, which is suitable for the characteristics of hyperspectral images. Next, in order to make full use of the unlabeled training set, we generate pseudo-labels with the model trained in the first stage and mix the obtained pseudo-labels with the labeled training set. Then, a large number of strong data augmentation methods are added to make the final model better. We achieve the SOTA, with an AP of 26.35, on the Semi-Supervised Hyperspectral Object Detection Challenge (SSHODC) in the CVPR 2022 Perception Beyond the Visible Spectrum Workshop, and win the first place in this Challenge.

关键词： Training computer vision conferences Object detection Semisupervised learning Data models pattern recognition

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Cross-modal Target Retrieval for Tracking by Natural Language

Cross-modal Target Retrieval for Tracking by Natural Languag...

引用

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

作者： Li, Yihao Yu, Jun Cai, Zhongpeng Pan, Yuwen Univ Sci & Technol China Hefei Anhui Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Tracking by natural language specification in a video is a challenging task in computer vision. Distinct from initializing the target state only by the bounding box in the first frame, language specification has a strong potential to assist visual object trackers to capture appearance variation and eliminate semantic ambiguity of the tracked object. In this paper, we carefully design a unified local-global-search framework from the perspective of cross-modal retrieval, including a local tracker, an adaptive retrieval switch module, and a target-specific retrieval module. The adaptive retrieval switch module aligns semantics from the visual signal and the lingual description of the target using three sub-modules, i.e., object-aware attention memory, part-aware cross-attention, and vision-language contrast, which achieve an automatic switch between local search and global search. When booting the global search mechanism, the target-specific retrieval module relocalizes the missing target in the image-wide range via an efficient vision-language guided proposal selector and target-text match. Numerous experimental results on three prevailing benchmarks show the effectiveness and generalization of our framework.

关键词： Visualization computer vision Target tracking Natural languages Semantics Switches Benchmark testing

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Towards Open-Set Object Detection and Discovery

Towards Open-Set Object Detection and Discovery

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

作者： Zheng, Jiyang Li, Weihao Hong, Jie Petersson, Lars Barnes, Nick Australian Natl Univ Canberra ACT Australia Data61 CSIRO Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

With the human pursuit of knowledge, open-set object detection (OSOD) has been designed to identify unknown objects in a dynamic world. However, an issue with the current setting is that all the predicted unknown objects share the same category as "unknown", which require incremental learning via a human-in-the-loop approach to label novel classes. In order to address this problem, we present a new task, namely Open-Set Object Detection and Discovery (OSODD). This new task aims to extend the ability of open-set object detectors to further discover the categories of unknown objects based on their visual appearance without human effort. We propose a two-stage method that first uses an open-set object detector to predict both known and unknown objects. Then, we study the representation of predicted objects in an unsupervised manner and discover new categories from the set of unknown objects. With this method, a detector is able to detect objects belonging to known classes and define novel categories for objects of unknown classes with minimal supervision. We show the performance of our model on the MS-COCO dataset under a thorough evaluation protocol. We hope that our work will promote further research towards a more robust real-world detection system.

关键词： Visualization computer vision Protocols conferences Object detection Detectors Human in the loop

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Difficulty Estimation with Action Scores for computer vision Tasks

Difficulty Estimation with Action Scores for Computer Vision...

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

作者： Arriaga, Octavio Palacio, Sebastian Valdenegro-Toro, Matias Univ Bremen Bremen Germany German Res Ctr Artificial Intelligence Kaiserslautern Germany Univ Groningen Groningen Netherlands

ISBN: (纸本)9798350302493

As more machine learning models are now being applied in real world scenarios it has become crucial to evaluate their difficulties and biases. In this paper we present an unsupervised method for calculating a difficulty score based on the accumulated loss per epoch. Our proposed method does not require any modification to the model, neither any external supervision, and it can be easily applied to a wide range of machine learning tasks. We provide results for the tasks of image classification, image segmentation, and object detection. We compare our score against similar metrics and provide theoretical and empirical evidence of their difference. Furthermore, we show applications of our proposed score for detecting incorrect labels, and test for possible biases.

关键词： Object detection

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On Visible Adversarial Perturbations & Digital Watermarking 31

On Visible Adversarial Perturbations & Digital Watermarking

引用

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

作者： Hayes, Jamie UCL London England

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Given a machine learning model, adversarial perturbations transform images such that the model's output is classified as an attacker chosen class. Most research in this area has focused on adversarial perturbations that are imperceptible to the human eye. However, recent work has considered attacks that are perceptible but localized to a small region of the image. Under this threat model, we discuss both defenses that remove such adversarial perturbations, and attacks that can bypass these defenses.

关键词： Perturbation methods Watermarking Computational modeling Visualization Task analysis Image restoration computer vision

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Using Pure Pollen Species When Training a CNN to Segment Pollen Mixtures

Using Pure Pollen Species When Training a CNN to Segment Pol...

引用

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

作者： Yang, Nana Joos, Victor Jacquemart, A. L. Buyens, Christel De Vleeschouwer, C. UCLouvain ICTEAM Inst Ottignies Belgium UCLouvain ELI Inst Ottignies Belgium

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recognizing the types of pollen grains and estimating their proportion in pollen mixture samples collected in a specific geographical area is important for agricultural, medical, and ecosystem research. Our paper adopts a convolutional neural network for the automatic segmentation of pollen species in microscopy images, and proposes an original strategy to train such network at reasonable manual annotation cost. Our approach is founded on a large dataset composed of pure pollen images. It first (semi-)manually segments foreground, i.e. pollen grains, and background in a fraction of those images, and use the resulting annotated dataset to train a universal pollen segmentation CNN. In the second step, this model is used to automatically segment a large number of additional pure pollen images, so as to supervise the training of a pollen species segmentation model. Despite the fact that it has been trained from pure images only, the model is shown to provide accurate segmentation of species in pollen mixtures. Our experiments also demonstrate that dedicating a model to the segmentation of a subset of the available pure pollen species makes it possible to train a bin pollen class, corresponding to pollen species that are not in the subset of species recognized by the model. This strategy is useful to cope with unexpected species in a mixture.

关键词： Training Image segmentation computer vision Costs Computational modeling Microscopy pattern recognition

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Nerfels: Renderable Neural Codes for Improved Camera Pose Estimation

Nerfels: Renderable Neural Codes for Improved Camera Pose Es...

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

作者： Avraham, Gil Straub, Julian Shen, Tianwei Yang, Tsun-Yi Germain, Hugo Sweeney, Chris Balntas, Vasileios Novotny, David DeTone, Daniel Newcombe, Richard Monash Univ Clayton Vic Australia Ecole Ponts Champs Sur Marne France Facebook Real Labs Menlo Pk CA USA Facebook AI Res Menlo Pk CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper presents a framework that combines traditional keypoint-based camera pose optimization with an invertible neural rendering mechanism. Our proposed 3D scene representation, Nerfels, is locally dense yet globally sparse. As opposed to existing invertible neural rendering systems which overfit a model to the entire scene, we adopt a feature-driven approach for representing scene-agnostic, local 3D patches with renderable codes. By modelling a scene only where local features are detected, our framework effectively generalizes to unseen local regions in the scene via an optimizable code conditioning mechanism in the neural renderer, all while maintaining the low memory footprint of a sparse 3D map representation. Our model can be incorporated to existing state-of-the-art hand-crafted and learned local feature pose estimators, yielding improved performance when evaluating on ScanNet for wide camera baseline scenarios.

关键词： Solid modeling Three-dimensional displays Codes conferences Pose estimation Rendering (computer graphics) Cameras

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MixAugment & Mixup: Augmentation Methods for Facial Expression recognition

MixAugment & Mixup: Augmentation Methods for Facial Expressi...

引用

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

作者： Psaroudakis, Andreas Kollias, Dimitrios Natl Tech Univ Athens Athens Greece Queen Mary Univ London London England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Automatic Facial Expression recognition (FER) has attracted increasing attention in the last 20 years since facial expressions play a central role in human communication. Most FER methodologies utilize Deep Neural Networks (DNNs) that are powerful tools when it comes to data analysis. However, despite their power, these networks are prone to overfitting, as they often tend to memorize the training data. What is more, there are not currently a lot of in-the-wild (i.e. in unconstrained environment) large databases for FER. To alleviate this issue, a number of data augmentation techniques have been proposed. Data augmentation is a way to increase the diversity of available data by applying constrained transformations on the original data. One such technique, which has positively contributed to various classification tasks, is Mixup. According to this, a DNN is trained on convex combinations of pairs of examples and their corresponding labels. In this paper, we examine the effectiveness of Mixup for in-the-wild FER in which data have large variations in head poses, illumination conditions, backgrounds and contexts. We then propose a new data augmentation strategy which is based on Mixup, called MixAugment. According to this, the network is trained concurrently on a combination of virtual examples and real examples;all these examples contribute to the overall loss function. We conduct an extensive experimental study that proves the effectiveness of MixAugment over Mixup and various state-of-the-art methods. We further investigate the combination of dropout with Mixup and MixAugment, as well as the combination of other data augmentation techniques with MixAugment.

关键词： Deep learning computer vision Databases Face recognition conferences Neural networks Training data

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