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Dynamic Feature Queue for Surveillance Face Anti-spoofing via Progressive Training

Dynamic Feature Queue for Surveillance Face Anti-spoofing vi...

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

作者： Wang, Keyao Huang, Mouxiao Zhang, Guosheng Yue, Haixiao Zhang, Gang Qiao, Yu China Chinese Academy of Sciences ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology China University of Chinese Academy of Sciences China

ISBN: (纸本)9798350302493

In recent years, face recognition systems have faced increasingly security threats, making it essential to employ Face Anti-spoofing (FAS) to protect against various types of attacks in traditional scenarios like phone unlocking, face payment and self-service security inspection. However, further exploration is required to fully secure FAS in long-distance settings. In this paper, we propose two contributions to enhance the security of face recognition systems: Dynamic Feature Queue (DFQ) and Progressive Training Strategy (PTS). DFQ converts the conventional binary classification task into a multi-classification task. It treats live samples as a closed set and attack samples as an open set by using a dynamic queue that stores the features of spoofing samples and updates them. On the other hand, PTS targets difficult samples and iteratively adds them in batches for training. The proposed PTS divides the entire training set into blocks, trains only a small portion of the data, and gradually increases the training data with each stage while also incorporating low-scoring positive samples and high-scoring spoof samples from the test set. These two contributions complement each other by enhancing the model's ability to generalize and defend against various types of attacks, making the face recognition system more secure and reliable. Our proposed methods have achieved top performance on ACER metric with 4.73% on the SuHiFiMask dataset [11] and won the first prize in Surveillance Face Anti-spoofing track of the Challenge@CVPR 2023. © 2023 ieee.

关键词： Iterative methods

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NTIRE 2023 Challenge on HR Depth from Images of Specular and Transparent Surfaces

NTIRE 2023 Challenge on HR Depth from Images of Specular and...

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

作者： Ramirez, Pierluigi Zama Costanzino, Alex Tosi, Fabio Poggi, Matteo Salti, Samuele Mattoccia, Stefano Di Stefano, Luigi Timofte, Radu Cao, Chengzhi Zhang, Fanrui Zhan, Qiang Wang, Kunyu Shi, Jun Zhang, Dafeng Yong, A. Jin, Yixiang Li, Dingzhe Li, Chao Liu, Zhiwen Zhang, Qi Wang, Yixing Li, Liangyan Liang, Runchen Liu, Yangyi Liu, Huan Song, Siyu Chen, Jun Yin, Shi University of Bologna Italy Computer Vision Lab University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland University of Science and Technology of China China China Vivo China McMaster University Canada Nanjing Tech University China

ISBN: (纸本)9798350302493

This paper reports about the NTIRE 2023 challenge on HR Depth From images of Specular and Transparent surfaces, held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2023. This challenge is held to boost the research on depth estimation, mainly to deal with two of the open issues in the field: high-resolution images and non-Lambertian surfaces characterizing specular and transparent materials. The challenge is divided into two tracks: a stereo track focusing on disparity estimation from rectified pairs and a mono track dealing with single-image depth estimation. The challenge attracted about 100 registered participants for the two tracks. In the final testing stage, 5 participating teams submitted their models and fact sheets, 2 and 3 for the Stereo and Mono tracks, respectively. © 2023 ieee.

关键词： Image enhancement

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RB-Dust - A Reference-based Dataset for vision-based Dust Removal

RB-Dust - A Reference-based Dataset for Vision-based Dust Re...

引用

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

作者： Buckel, Peter Oksanen, Timo Dietmueller, Thomas Ravensburg Germany Technical University of Munich Germany Germany

ISBN: (纸本)9798350302493

Dust in the agricultural landscape is a significant challenge and influences, for example, the environmental perception of autonomous agricultural machines. Image enhancement algorithms can be used to reduce dust. However, these require dusty and dust-free images of the same environment for validation. In fact, to date, there is no dataset that we are aware of that addresses this issue. Therefore, we present the agriscapes RB-Dust dataset, which is named after its purpose of reference-based dust removal. It is not possible to take pictures from the cabin during tillage, as this would cause shifts in the images. Because of this, we built a setup from which it is possible to take images from a stationary position close to the passing tractor. The test setup was based on a half-sided gate through which the tractor could drive. The field tests were carried out on a farm in Bavaria, Germany, during tillage. During the field tests, other parameters such as soil moisture and wind speed were controlled, as these significantly affect dust development. We validated our dataset with contrast enhancement and image dehazing algorithms and analyzed the generalizability from recordings from the moving tractor. Finally, we demonstrate the application of dust removal based on a high-level vision task, such as person classification. Our empirical study confirms the validity of RB-Dust for vision-based dust removal in agriculture. © 2023 ieee.

关键词： Image enhancement

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Universal Watermark Vaccine: Universal Adversarial Perturbations for Watermark Protection

Universal Watermark Vaccine: Universal Adversarial Perturbat...

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

作者： Chen, Jianbo Liu, Xinwei Liang, Siyuan Jia, Xiaojun Xun, Yuan Hunan University College of Computer Science and Electronic Engineering Hunan China Chinese Academy of Sciences Institute of Information Engineering Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9798350302493

As computing ability continues to rapidly develop, neural networks have found widespread use in various fields. However, in the realm of visible watermarking for image copyright protection, neural networks have made image protection through watermarking less effective. Some research has even shown that watermarks can be removed without damaging to the original image, posing a significant threat to digital copyright protection. In response, the community has introduced adversarial perturbations for watermark protection, but these are sample-specific and time-consuming in real-world scenarios. To address this issue, we propose a new universal adversarial perturbation for watermark removal networks that offers two options. The first option involves adding perturbations to the entire host image, bringing the output of the watermark removal network closer to the original image and providing protection. The second option involves adding perturbations only to the watermark position, reducing the impact of the perturbation on the image and enhancing stealthiness. Our experiments demonstrate that our method effectively resists watermark removal networks and has good generalizability across different images. © 2023 ieee.

关键词： Copyrights

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Exploring Joint Embedding Architectures and Data Augmentations for Self-Supervised Representation Learning in Event-Based vision

Exploring Joint Embedding Architectures and Data Augmentatio...

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

作者： Barchid, Sami Mennesson, José Djéraba, Chaabane Univ. Lille Cnrs Centrale Lille Umr 9189 CRIStAL France Univ. Lille Centre for Digital Systems Imt Nord Europe Institut Mines-Télécom LilleF-59000 France

ISBN: (纸本)9798350302493

This paper proposes a self-supervised representation learning (SSRL) framework for event-based vision, which leverages various lightweight convolutional neural networks (CNNs) including 2D-, 3D-, and Spiking CNNs. The method uses a joint embedding architecture to maximize the agreement between features extracted from different views of the same event sequence. Popular event data augmentation techniques are employed to design an efficient augmentation policy for event-based SSRL, and we provide novel data augmentation methods to enhance the pretraining pipeline. Given the novelty of SSRL for event-based vision, we elaborate standard evaluation protocols and use them to evaluate our approach. Our study demonstrates that pretrained CNNs acquire effective and transferable features, enabling them to achieve competitive performance in object or action recognition across various commonly used event-based datasets, even in a low-data regime. This paper also conducts an experimental analysis of the extracted features regarding the Uniformity-Tolerance tradeoff to assess their quality, and measure the similarity of representations using linear Center Kernel Alignement. These quantitative measurements reinforce our observations from the performance benchmarks and show substantial differences between the learned representations of all types of CNNs despite being optimized with the same approach. © 2023 ieee.

关键词： Embeddings

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SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data

SynthASpoof: Developing Face Presentation Attack Detection B...

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

作者： Fang, Meiling Huber, Marco Damer, Naser Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany TU Darmstadt Department of Computer Science Darmstadt Germany

ISBN: (纸本)9798350302493

Recently, significant progress has been made in face presentation attack detection (PAD), which aims to secure face recognition systems against presentation attacks, owing to the availability of several face PAD datasets. However, all available datasets are based on privacy and legally-sensitive authentic biometric data with a limited number of subjects. To target these legal and technical challenges, this work presents the first synthetic-based face PAD dataset, named SynthASpoof, as a large-scale PAD development dataset. The bona fide samples in SynthASpoof are synthetically generated and the attack samples are collected by presenting such synthetic data to capture systems in a real attack scenario. The experimental results demonstrate the feasibility of using SynthASpoof for the development of face PAD. Moreover, we boost the performance of such a solution by incorporating the domain generalization tool MixStyle into the PAD solutions. Additionally, we showed the viability of using synthetic data as a supplement to enrich the diversity of limited authentic training data and consistently enhance PAD performances. The SynthASpoof dataset, containing 25,000 bona fide and 78,800 attack samples, the implementation, and the pre-trained weights are made publicly available https://***/meilfang/***. © 2023 ieee.

关键词： Face recognition

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Low-Rank Adaptation vs. Fine-Tuning for Handwritten Text recognition

Low-Rank Adaptation vs. Fine-Tuning for Handwritten Text Rec...

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2025 ieee/CVF Winter conference on Applications of computer vision workshops, WACVW 2025

作者： Huttner, Lukas Mayr, Martin Gorges, Thomas Wu, Fei Seuret, Mathias Maier, Andreas Christlein, Vincent Friedrich-Alexander Universität Erlangen-Nürnberg Pattern Recognition Lab Erlangen91058 Germany

ISBN: (纸本)9798331536626

The continuous expansion of neural network sizes is a notable trend in machine learning, with transformer models exceeding 20 billion parameters in computer vision. This growth comes with rising demands for computational resources and large-scale datasets. Efficient techniques for transfer learning thus become an attractive option in setups with limited data, as in handwriting recognition. Recently, parameter-efficient fine-tuning (PEFT) methods, such as low-rank adaptation (LoRA) and weight-decomposed low-rank adaptation (DoRA), have gained wide-spread interest. In this paper, we explore tradeoffs in parameter-efficient transfer learning using the synthetically pretrained Transformer-Based Optical Character recognition (TrOCR) model for handwritten text recognition with LoRA and DoRA. Additionally, we analyze the performance of full fine-tuning with a limited number of samples, scaling from a few-shot learning scenario up to using the whole dataset. We conduct experiments on the popular IAM Handwriting database as well as the historical READ 2016 dataset. We find that (a) LoRA/DoRA does not outperform full fine-tuning as opposed to a recent paper and (b) LoRA/DoRA is not substantially faster than full fine-tuning of TrOCR. © 2025 ieee.

关键词： Optical character recognition

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Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in computer vision Tasks for Robotic Grasping on the Edge

Fast GraspNeXt: A Fast Self-Attention Neural Network Archite...

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

作者： Wong, Alexander Wu, Yifan Abbasi, Saad Nair, Saeejith Chen, Yuhao Shafiee, Mohammad Javad Vision and Image Processing Research Group University of Waterloo Canada Waterloo Artificial Intelligence Institute WaterlooON Canada DarwinAI Corp. WaterlooON Canada

ISBN: (纸本)9798350302493

Multi-task learning has shown considerable promise for improving the performance of deep learning-driven vision systems for the purpose of robotic grasping. However, high architectural and computational complexity can result in poor suitability for deployment on embedded devices that are typically leveraged in robotic arms for real-world manufacturing and warehouse environments. As such, the design of highly efficient multi-task deep neural network architectures tailored for computer vision tasks for robotic grasping on the edge is highly desired for widespread adoption in manufacturing environments. Motivated by this, we propose Fast GraspNeXt, a fast self-attention neural network architecture tailored for embedded multi-task learning in computer vision tasks for robotic grasping. To build Fast GraspNeXt, we leverage a generative network architecture search strategy with a set of architectural constraints customized to achieve a strong balance between multitask learning performance and embedded inference efficiency. Experimental results on the MetaGraspNet benchmark dataset show that the Fast GraspNeXt network design achieves the highest performance (average precision (AP), accuracy, and mean squared error (MSE)) across multiple computer vision tasks when compared to other efficient multi-task network architecture designs, while having only 17.8M parameters (about >5× smaller), 259 GFLOPs (as much as >5× lower) and as much as >3.15× faster on a NVIDIA Jetson TX2 embedded processor. © 2023 ieee.

关键词： computer vision

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Efficient Multi-Lens Bokeh Effect Rendering and Transformation

Efficient Multi-Lens Bokeh Effect Rendering and Transformati...

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

作者： Seizinger, Tim Conde, Marcos V. Kolmet, Manuel Bishop, Tom E. Timofte, Radu University of Würzburg Computer Vision Lab CAIDAS & IFI Germany Glass Imaging Inc. CA United States

ISBN: (纸本)9798350302493

Many advancements of mobile cameras aim to reach the visual quality of professional DSLR cameras. Great progress was shown over the last years in optimizing the sharp regions of an image and in creating virtual portrait effects with artificially blurred backgrounds. Bokeh is the aesthetic quality of the blur in out-of-focus areas of an image. This is a popular technique among professional photographers, and for this reason, a new goal in computational photography is to optimize the Bokeh effect *** paper introduces EBokehNet, a efficient state-of-the-art solution for Bokeh effect transformation and rendering. Our method can render Bokeh from an all-in-focus image, or transform the Bokeh of one lens to the effect of another lens without harming the sharp foreground regions in the image. Moreover we can control the shape and strength of the effect by feeding the lens properties i.e. type (Sony or Canon) and aperture, into the neural network as an additional input. Our method is a winning solution at the NTIRE 2023 Lens-to-Lens Bokeh Effect Transformation Challenge, and state-of-the-art at the EBB benchmark. © 2023 ieee.

关键词： Color photography

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A New Dataset and Approach for Timestamp Supervised Action Segmentation Using Human Object Interaction

A New Dataset and Approach for Timestamp Supervised Action S...

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

作者： Sayed, Saif Ghoddoosian, Reza Trivedi, Bhaskar Athitsos, Vassilis University of Texas Arlington United States

ISBN: (纸本)9798350302493

This paper focuses on leveraging Human Object Interaction (HOI) information to improve temporal action segmentation under timestamp supervision, where only one frame is annotated for each action segment. This information is obtained from an off-the-shelf pre-trained HOI detector, that requires no additional HOI-related annotations in our experimental datasets. Our approach generates pseudo labels by expanding the annotated timestamps into intervals and allows the system to exploit the spatio-temporal continuity of human interaction with an object to segment the video. We also propose the (3+1)Real-time Cooking (ReC)1 dataset as a realistic collection of videos from 30 participants cooking 15 breakfast items. Our dataset has three main properties: 1) to our knowledge, the first to offer synchronized third and first person videos, 2) it incorporates diverse actions and tasks, and 3) it consists of high resolution frames to detect fine-grained information. In our experiments we benchmark state-of-the-art segmentation methods under different levels of supervision on our dataset. We also quantitatively show the advantages of using HOI information, as our framework improves its baseline segmentation method on several challenging datasets with varying viewpoints, providing improvements of up to 10.9% and 5.3% in F1 score and frame-wise accuracy respectively. © 2023 ieee.

关键词： computer vision

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