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Joint Physical-Digital Facial Attack Detection Via Simulating Spoofing Clues

Joint Physical-Digital Facial Attack Detection Via Simulatin...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Xianhua He Dashuang Liang Song Yang Zhanlong Hao Hui Ma Binjie Mao Xi Li Yao Wang Pengfei Yan Ajian Liu Vision AI Department Meituan M.U.S.T MAIS CASIA China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Face recognition systems are frequently subjected to a variety of physical and digital attacks of different types. Previous methods have achieved satisfactory performance in scenarios that address physical attacks and digital attacks, respectively. However, few methods are considered to integrate a model that simultaneously addresses both physical and digital attacks, implying the necessity to develop and maintain multiple models. To jointly detect physical and digital attacks within a single model, we propose an innovative approach that can adapt to any network architecture. Our approach mainly contains two types of data augmentation, which we call Simulated Physical Spoofing Clues augmentation (SPSC) and Simulated Digital Spoofing Clues augmentation (SDSC). SPSC and SDSC augment live samples into simulated attack samples by simulating spoofing clues of physical and digital attacks, respectively, which significantly improve the capability of the model to detect "unseen" attack types. Extensive experiments show that SPSC and SDSC can achieve state-of-the-art generalization in Protocols 2.1 and 2.2 of the UniAttackData dataset, respectively. Our method won first place in "Unified Physical-Digital Face Attack Detection" of the 5th Face Anti-spoofing Challenge@CVPR2024. Our final submission obtains 3.75% APCER, 0.93% BPCER, and 2.34% ACER, respectively. Our code is available at https://***/Xianhua-He/cvpr2024-face-anti-spoofing-challenge.

关键词： Adaptation models computer vision Protocols Codes Face recognition conferences Computational modeling

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IMIL: Interactive Medical Image Learning Framework

IMIL: Interactive Medical Image Learning Framework

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Adrit Rao Andrea Fisher Ken Chang John Christopher Panagides Katherine McNamara Joon-Young Lee Oliver Aalami Stanford University Palo Alto High School Adobe Research

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Data augmentations are widely used in training medical image deep learning models to increase the diversity and size of sparse datasets. However, commonly used augmentation techniques can result in loss of clinically relevant information from medical images, leading to incorrect predictions at inference time. We propose the Interactive Medical Image Learning (IMIL) framework, a novel approach for improving the training of medical image analysis algorithms that enables clinician-guided intermediate training data augmentations on misprediction outliers, focusing the algorithm on relevant visual information. To prevent the model from using irrelevant features during training, IMIL will ’blackout’ clinician-designated irrelevant regions and replace the original images with the augmented samples. This ensures that for originally mispredicted samples, the algorithm subsequently attends only to relevant regions and correctly correlates them with the respective diagnosis. We validate the efficacy of IMIL using radiology residents and compare its performance to state-of-the-art data augmentations. A 4.2% improvement in accuracy over ResNet-50 was observed when using IMIL on only 4% of the training set. Our study demonstrates the utility of clinician-guided interactive training to achieve meaningful data augmentations for medical image analysis algorithms.

关键词： Training computer vision Image analysis Accuracy Computational modeling Radiology Data augmentation

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Semantic Pre-supplement for Exposure Correction

Semantic Pre-supplement for Exposure Correction

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Zhen Zou Wei Yu Jie Huang Feng Zhao University of Science and Technology of China He Fei China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Exposure correction tasks are dedicated to recovering the brightness and structural information of overexposed or underexposed images. The recovery difficulty of areas with different exposure levels is different, as severely exposed areas are more difficult to recover due to severe structural information loss than commonly exposed areas. However, existing methods focus on the simultaneous recovery of global brightness and structure, ignoring that the recovery difficulty varies between areas. To address this issue, we propose a novel exposure correction strategy named "Inpainting Assisted Exposure Correction"(IAEC), which pre-performs image structure repair on severely exposed areas to guide the exposure correction process. This method is based on the observation that the contextual semantic information contained in the image structure can effectively help the overall image recovery, and the lack of contextual semantic information in severely incorrectly exposed areas is very severe. The pre-performed structural repair by the inpainting model can well supplement the insufficient contextual semantic information caused by severe exposure. Therefore, we use an inpainting model to perform pre-structure repair on severely exposed areas to obtain supplementary contextual semantic information and then align the structure-repaired image with the improperly exposed input at the feature level. Extensive experiments demonstrate that our method gets superior results than the state-of-the-art methods and has the potential to be applied to other tasks with similar context loss problems.

关键词： Training computer vision conferences Semantics Brightness Maintenance engineering Cognition

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in2IN: Leveraging individual Information to Generate Human INteractions

in2IN: Leveraging individual Information to Generate Human I...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Pablo Ruiz-Ponce German Barquero Cristina Palmero Sergio Escalera José García-Rodríguez Universidad de Alicante San Vicente del Raspeig Spain Universitat de Barcelona Barcelona Spain Computer Vision Center Cerdanyola del Vallès Spain

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Generating human-human motion interactions conditioned on textual descriptions is a very useful application in many areas such as robotics, gaming, animation, and the metaverse. Alongside this utility also comes a great diffi-culty in modeling the highly dimensional inter-personal dynamics. In addition, properly capturing the intra-personal diversity of interactions has a lot of challenges. Current methods generate interactions with limited diversity of intra-person dynamics due to the limitations of the available datasets and conditioning strategies. For this, we introduce in2IN, a novel diffusion model for human-human motion generation which is conditioned not only on the textual description of the overall interaction but also on the individual descriptions of the actions performed by each person involved in the interaction. To train this model, we use a large language model to extend the InterHuman dataset with individual descriptions. As a result, in2IN achieves state-of-the- art performance in the InterHuman dataset. Furthermore, in order to increase the intra-personal diversity on the existing interaction datasets, we propose DualMDM, a model composition technique that combines the motions generated with in2IN and the motions generated by a single-person motion prior pre-trained on HumanML3D. As a result, DualMDM generates motions with higher individual diversity and improves control over the intra-person dynamics while maintaining inter-personal coherence.

关键词： Legged locomotion computer vision Metaverse Large language models conferences Dynamics Diversity reception

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How to Benchmark vision Foundation Models for Semantic Segmentation?

How to Benchmark Vision Foundation Models for Semantic Segme...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Tommie Kerssies Daan De Geus Gijs Dubbelman Eindhoven University of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent vision foundation models (VFMs) have demonstrated proficiency in various tasks but require supervised fine-tuning to perform the task of semantic segmentation effectively. Benchmarking their performance is essential for selecting current models and guiding future model developments for this task. The lack of a standardized benchmark complicates comparisons. Therefore, the primary objective of this paper is to study how VFMs should be benchmarked for semantic segmentation. To do so, various VFMs are fine-tuned under various settings, and the impact of individual settings on the performance ranking and training time is assessed. Based on the results, the recommendation is to fine-tune the ViT-B variants of VFMs with a 16 × 16 patch size and a linear decoder, as these settings are representative of using a larger model, more advanced decoder and smaller patch size, while reducing training time by more than 13 times. Using multiple datasets for training and evaluation is also recommended, as the performance ranking across datasets and domain shifts varies. Linear probing, a common practice for some VFMs, is not recommended, as it is not representative of end-to-end fine-tuning. The benchmarking setup recommended in this paper enables a performance analysis of VFMs for semantic segmentation. The findings of such an analysis reveal that pretraining with promptable segmentation is not beneficial, whereas masked image modeling (MIM) with abstract representations is crucial, even more important than the type of supervision used. The code for efficiently fine-tuning VFMs for semantic segmentation can be accessed through the project page 1 .

关键词： Training computer vision Analytical models Codes Semantic segmentation conferences Benchmark testing

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AutoSoccerPose: Automated 3D posture Analysis of Soccer Shot Movements

AutoSoccerPose: Automated 3D posture Analysis of Soccer Shot...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Calvin Yeung Kenjiro Ide Keisuke Fujii Nagoya University Japan RIKEN/JST PRESTO Japan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Image understanding is a foundational task in computer vision, with recent applications emerging in soccer posture analysis. However, existing publicly available datasets lack comprehensive information, notably in the form of posture sequences and 2D pose annotations. Moreover, current analysis models often rely on interpretable linear models (e.g., PCA and regression), limiting their capacity to capture non-linear spatiotemporal relationships in complex and diverse scenarios. To address these gaps, we introduce the 3D Shot Posture (3DSP) dataset in soccer broadcast videos, which represents the most extensive sports image dataset with 2D pose annotations to our knowledge. Additionally, we present the 3DSP-GRAE (Graph Recurrent AutoEncoder) model, a non-linear approach for embedding pose sequences. Furthermore, we propose AutoSoccerPose, a pipeline aimed at semi-automating 2D and 3D pose estimation and posture analysis. While achieving full automation proved challenging, we provide a foundational baseline, extending its utility beyond the scope of annotated data. We validate AutoSoccerPose on SoccerNet and 3DSP datasets, and present posture analysis results based on 3DSP. The dataset, code, and models are available at: https://***/calvinyeungck/3D-Shot-Posture-Dataset.

关键词： computer vision Analytical models Three-dimensional displays Annotations Computational modeling Pose estimation Spatiotemporal phenomena

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SciFlow: Empowering Lightweight Optical Flow Models with Self-Cleaning Iterations

SciFlow: Empowering Lightweight Optical Flow Models with Sel...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jamie Menjay Lin Jisoo Jeong Hong Cai Risheek Garrepalli Kai Wang Fatih Porikli Qualcomm Technologies Inc. Qualcomm AI Research

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Optical flow estimation is crucial to a variety of vision tasks. Despite substantial recent advancements, achieving real-time on-device optical flow estimation remains a complex challenge. First, an optical flow model must be sufficiently lightweight to meet computation and memory constraints to ensure real-time performance on devices. Second, the necessity for real-time on-device operation imposes constraints that weaken the model’s capacity to adequately handle ambiguities in flow estimation, thereby intensifying the difficulty of preserving flow *** paper introduces two synergistic techniques, Self-Cleaning Iteration (SCI) and Regression Focal Loss (RFL), designed to enhance the capabilities of optical flow models, with a focus on addressing optical flow regression ambiguities. These techniques prove particularly effective in mitigating error propagation, a prevalent issue in optical flow models that employ iterative refinement. Notably, these techniques add negligible to zero overhead in model parameters and inference latency, thereby preserving real-time on-device *** effectiveness of our proposed SCI and RFL techniques, collectively referred to as SciFlow for brevity, is demonstrated across two distinct lightweight optical flow model architectures in our experiments. Remarkably, SciFlow enables substantial reduction in error metrics (EPE and Fl-all) over the baseline models by up to 6.3% and 10.5% for in-domain scenarios and by up to 6.2% and 13.5% for cross-domain scenarios on the Sintel and KITTI 2015 datasets, respectively.

关键词： Optical losses Training Performance evaluation computer vision Image motion analysis Computational modeling Estimation

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Video Interaction recognition using an Attention Augmented Relational Network and Skeleton Data

Video Interaction Recognition using an Attention Augmented R...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Farzaneh Askari Cyril Yared Rohit Ramaprasad Devin Garg Anjun Hu James J. Clark University of McGill Montreal QC Canada University of California San Diego San Diego CA United States University of Oxford Oxford United Kingdom

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recognizing interactions in multi-person videos, known as Video Interaction recognition (VIR), is crucial for understanding video content. Often the human skeleton pose (skeleton, for short) is a popular feature for VIR as the main feature, given its success for the task in hand. While many studies have made progress using complex architectures like Graph Neural Networks (GNN) and Transformers to capture interactions in videos, studies such as [33] that apply simple, easy to train, and adaptive architectures such as Relation reasoning Network (RN) [37], yield competitive results. Inspired by this trend, we propose the Attention Augmented Relational Network (AARN), a straightforward yet effective model that uses skeleton data to recognize interactions in videos. AARN outperforms other RN-based models and remains competitive against larger, more intricate models. We evaluate our approach on a challenging real-world Hockey Penalty Dataset (HPD), where the videos depict complex interactions between players in a non-laboratory recording setup, in addition to popular benchmark datasets demonstrating strong performance. Lastly, we show the impact of skeleton quality on the classification accuracy and the struggle of off-the-shelf pose estimators to extract precise skeleton from the challenging HPD dataset.

关键词： Adaptation models computer architecture Transformers Skeleton Graph neural networks Data models Recording

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Improving the Robustness of 3D Human Pose Estimation: A Benchmark Dataset and Learning from Noisy Input

Improving the Robustness of 3D Human Pose Estimation: A Benc...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Trung-Hieu Hoang Mona Zehni Huy Phan Duc Minh Vo Minh N. Do University of Illinois at Urbana-Champaign USA VinUniversity Ha Noi Vietnam The University of Tokyo Japan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Despite the promising performance of current 3D human pose estimation techniques, understanding and enhancing their robustness on challenging in-the-wild videos remain an open problem. In this work, we focus on building robust 2D-to-3D pose lifters. To this end, we develop two benchmark datasets, namely Human3.6M-C and HumanEva-I-C, to examine the resilience of video-based 3D pose lifters to a wide range of common video corruptions including temporary occlusion, motion blur, and pixel-level noise. We demonstrate the poor generalization of state-of-the-art 3D pose lifters in the presence of corruption and establish two techniques to tackle this issue. First, we introduce Temporal Additive Gaussian Noise (TAGN) as a simple yet effective 2D input pose data augmentation. Additionally, to incorporate the confidence scores output by the 2D pose detectors, we design a confidence-aware convolution (CA-Conv) block. Extensively tested on corrupted videos, the proposed strategies consistently boost the robustness of 3D pose lifters and serve as new baselines for future research.

关键词： Solid modeling Three-dimensional displays Convolution Pose estimation Benchmark testing Robustness pattern recognition

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Parameter Efficient Fine-tuning of Self-supervised ViTs without Catastrophic Forgetting

Parameter Efficient Fine-tuning of Self-supervised ViTs with...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Reza Akbarian Bafghi Nidhin Harilal Claire Monteleoni Maziar Raissi University of Colorado Boulder INRIA Paris University of California Riverside

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Artificial neural networks often suffer from catastrophic forgetting, where learning new concepts leads to a complete loss of previously acquired knowledge. We observe that this issue is particularly magnified in vision transformers (ViTs), where post-pre-training and fine-tuning on new tasks can significantly degrade the model’s original general abilities. For instance, a DINO ViT-Base/16 pre-trained on ImageNet-1k loses over 70% accuracy on ImageNet-1k after just 10 iterations of fine-tuning on CIFAR-100. Overcoming this stability-plasticity dilemma is crucial for enabling ViTs to continuously learn and adapt to new domains while preserving their initial knowledge. In this work, we study two new parameter-efficient fine-tuning strategies: (1) Block Expansion, and (2) Low-rank adaptation (LoRA). Our experiments reveal that using either Block Expansion or LoRA on self-supervised pre-trained ViTs surpass fully fine-tuned ViTs in new domains while offering significantly greater parameter efficiency. Notably, we find that Block Expansion experiences only a minimal performance drop in the pre-training domain, thereby effectively mitigating catastrophic forgetting in pre-trained ViTs 1 .

关键词： Knowledge engineering computer vision Adaptation models conferences Learning (artificial intelligence) Artificial neural networks Transformers

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