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EVREAL: Towards a Comprehensive Benchmark and Analysis Suite for Event-based Video Reconstruction

EVREAL: Towards a Comprehensive Benchmark and Analysis Suite...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Burak Ercan Onur Eker Aykut Erdem Erkut Erdem Computer Engineering Department Hacettepe University HAVELSAN Inc Computer Engineering Department Koç University KUIS AI Center Koç University

Event cameras are a new type of vision sensor that incorporates asynchronous and independent pixels, offering advantages over traditional frame-based cameras such as high dynamic range and minimal motion blur. However, their output is not easily understandable by humans, making the reconstruction of intensity images from event streams a fundamental task in event-based vision. While recent deep learning-based methods have shown promise in video reconstruction from events, this problem is not completely solved yet. To facilitate comparison between different approaches, standardized evaluation protocols and diverse test datasets are essential. This paper proposes a unified evaluation methodology and introduces an open-source framework called EVREAL to comprehensively benchmark and analyze various event-based video reconstruction methods from the literature. Using EVREAL, we give a detailed analysis of the state-of-the-art methods for event-based video reconstruction, and provide valuable insights into the performance of these methods under varying settings, challenging scenarios, and downstream tasks.

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Neural Response Interpretation through the Lens of Critical Pathways

Neural Response Interpretation through the Lens of Critical ...

引用

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

作者： Khakzar, Ashkan Baselizadeh, Soroosh Khanduja, Saurabh Rupprecht, Christian Kim, Seong Tae Navab, Nassir CAMP Tech Univ Munich Munich Germany VGG Univ Oxford Oxford England

ISBN: (纸本)9781665445092

Is critical input information encoded in specific sparse pathways within the neural network? In this work, we discuss the problem of identifying these critical pathways and subsequently leverage them for interpreting the network's response to an input. The pruning objective - selecting the smallest group of neurons for which the response remains equivalent to the original network - has been previously proposed for identifying critical pathways. We demonstrate that sparse pathways derived from pruning do not necessarily encode critical input information. To ensure sparse pathways include critical fragments of the encoded input information, we propose pathway selection via neurons' contribution to the response. We proceed to explain how critical pathways can reveal critical input features. We prove that pathways selected via neuron contribution are locally linear (in an l(2)-ball), a property that we use for proposing a feature attribution method: "pathway gradient". We validate our interpretation method using mainstream evaluation experiments. The validation of pathway gradient interpretation method further confirms that selected pathways using neuron contributions correspond to critical input features. The code(1 2) is publicly available.

关键词： Gradient methods computer vision computer network reliability Neurons pattern recognition Reliability Object recognition

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Building Reliable Explanations of Unreliable Neural Networks: Locally Smoothing Perspective of Model Interpretation

Building Reliable Explanations of Unreliable Neural Networks...

引用

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

作者： Lim, Dohun Lee, Hyeonseok Kim, Sungchan Jeonbuk Natl Univ Div Comp Sci & Engn Jeonju South Korea

ISBN: (纸本)9781665445092

We present a novel method for reliably explaining the predictions of neural networks. We consider an explanation reliable if it identifies input features relevant to the model output by considering the input and the neighboring data points. Our method is built on top of the assumption of smooth landscape in a loss function of the model prediction: locally consistent loss and gradient profile. A theoretical analysis established in this study suggests that those locally smooth model explanations are learned using a batch of noisy copies of the input with the LI regularization for a saliency map. Extensive experiments support the analysis results, revealing that the proposed saliency maps retrieve the original classes of adversarial examples crafted against both naturally and adversarially trained models, significantly outperforming previous methods. We further demonstrated that such good performance results from the learning capability of this method to identify input features that are truly relevant to the model output of the input and the neighboring data points, fulfilling the requirements of a reliable explanation.

关键词： Analytical models computer vision Smoothing methods computer network reliability Neural networks Predictive models Reliability theory

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The devil is in discretization discrepancy. Robustifying Differentiable NAS with Single-Stage Searching Protocol

The devil is in discretization discrepancy. Robustifying Dif...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Konstanty Subbotko Wojciech Jablonski Piotr Bilinski University of Warsaw

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural Architecture Search (NAS) has been widely adopted to design neural networks for various computer vision tasks. One of its most promising subdomains is differentiable NAS (DNAS), where the optimal architecture is found in a differentiable manner. However, gradient-based methods suffer from the discretization error, which can severely damage the process of obtaining the final architecture. In our work, we first study the risk of discretization error and show how it affects an unregularized supernet. Then, we present that penalizing high entropy, a common technique of architecture regularization, can hinder the supernet’s performance. Therefore, to robustify the DNAS framework, we introduce a novel single-stage searching protocol, which is not reliant on decoding a continuous architecture. Our results demonstrate that this approach outperforms other DNAS methods by achieving 75.3% in the searching stage on the Cityscapes validation dataset and attains performance 1.1% higher than the optimal network of DCNAS on the non-dense search space comprising short connections. The entire training process takes only 5.5 GPU days due to the weight reuse, and yields a computationally efficient architecture. Additionally, we propose a new dataset split procedure, which substantially improves results and prevents architecture degeneration in DARTS.

关键词： Training computer vision Protocols Neural networks DNA Graphics processing units computer architecture

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Verifiability and Predictability: Interpreting Utilities of Network Architectures for Point Cloud Processing

Verifiability and Predictability: Interpreting Utilities of ...

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

作者： Shen, Wen Wei, Zhihua Huang, Shikun Zhang, Binbin Chen, Panyue Zhao, Ping Zhang, Quanshi Shanghai Jiao Tong Univ Shanghai Peoples R China Tongji Univ Shanghai Peoples R China Shanghai Jiao Tong Univ Hoperoft Ctr Shanghai Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence AI Inst Shanghai Peoples R China

ISBN: (纸本)9781665445092

In this paper, we diagnose deep neural networks for 3D point cloud processing to explore utilities of different intermediate-layer network architectures. We propose a number of hypotheses on the effects of specific intermediate-layer network architectures on the representation capacity of DNNs. In order to prove the hypotheses, we design five metrics to diagnose various types of DNNs from the following perspectives, information discarding, information concentration, rotation robustness, adversarial robustness, and neighborhood inconsistency. We conduct comparative studies based on such metrics to verify the hypotheses. We further use the verified hypotheses to revise intermediate-layer architectures of existing DNNs and improve their utilities. Experiments demonstrate the effectiveness of our method. The code will be released when this paper is accepted.

关键词： Measurement Deep learning computer vision Three-dimensional displays Uncertainty computer architecture Network architecture

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Outsmarting Biometric Imposters: Enhancing Iris-recognition System Security through Physical Adversarial Example Generation and PAD Fine-Tuning

Outsmarting Biometric Imposters: Enhancing Iris-Recognition ...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Yuka Ogino Kazuya Kakizaki Takahiro Toizumi Atsushi Ito NEC Corporation

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we address the vulnerabilities of iris recognition systems to both image-based impersonation attacks and Presentation Attacks (PAs) in physical environments. While existing Presentation Attack Detection (PAD) methods have been effective against PAs, they remain susceptible to adversarial examples. We propose a combination of physical adversarial attacks tailored to iris recognition and PAD, and also propose a defense method against them. Our attack methods involve a physical impersonation attack using adversarial perturbation on the iris region and a physical PAD evading attack using an adversarial patch on the pupil region. We demonstrate the high transferability and effectiveness of our attacks on multiple PA instruments in digital and distinct physical environments using multiple recognition engines. To counteract these attacks, we develop a defense method for PAD involving adversarial fine-tuning against both the physical attacks. This defense method successfully reduces the PAD evasion attack success rate from 71.5% to 21.0% in physical environments and ultimately lowers the overall physical impersonation success rate from 58.0% to 19.5%. Our proposed method lays the groundwork for developing more robust and secure iris recognition systems with increased protection against sophisticated PAs.

关键词： Perturbation methods Instruments Impersonation attacks Robustness pattern recognition Security Pupils

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Bilinear Parameterization for Non-Separable Singular Value Penalties

Bilinear Parameterization for Non-Separable Singular Value P...

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

作者： Ornhag, Marcus Valtonen Iglesias, Jose Pedro Olsson, Carl Lund Univ Ctr Math Sci Lund Sweden Chalmers Univ Technol Dept Elect Engn Gothenburg Sweden

ISBN: (纸本)9781665445092

Low rank inducing penalties have been proven to successfully uncover fundamental structures considered in computer vision and machine learning;however, such methods generally lead to non-convex optimization problems. Since the resulting objective is non-convex one often resorts to using standard splitting schemes such as Alternating Direction Methods of Multipliers (ADMM), or other sub gradient methods, which exhibit slow convergence in the neighbourhood of a local minimum. We propose a method using second order methods, in particular the variable projection method (VarPro), by replacing the nonconvex penalties with a surrogate capable of converting the original objectives to differentiable equivalents. In this way we benefit from faster convergence. The bilinear framework is compatible with a large family of regularizers, and we demonstrate the benefits of our approach on real datasets for rigid and non-rigid structure from motion. The qualitative difference in reconstructions show that many popular non-convex objectives enjoy an advantage in transitioning to the proposed framework.(1)

关键词： computer vision Structure from motion Machine learning Convex functions pattern recognition Optimization Standards

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GAN-based vision Transformer for High-Quality Thermal Image Enhancement

GAN-based Vision Transformer for High-Quality Thermal Image ...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Mohamed Amine Marnissi Abir Fathallah Ecole Nationale d’Ingénieurs de Sfax Université de Sfax Sfax Tunisie Samovar CNRS Télécom SudParis Institut Polytechnique de Paris Evry Cedex France

Generative Adversarial Networks (GANs) have shown an outstanding ability to generate high-quality images with visual realism and similarity to real images. This paper presents a new architecture for thermal image enhancement. Precisely, the strengths of architecture-based vision transformers and generative adversarial networks are exploited. The thermal loss feature introduced in our approach is specifically used to produce high-quality images. Thermal image enhancement also relies on fine-tuning based on visible images, resulting in an overall improvement in image quality. A visual quality metric was used to evaluate the performance of the proposed architecture. Significant improvements were found over the original thermal images and other enhancement methods established on a subset of the KAIST dataset. The performance of the proposed enhancement architecture is also verified on the detection results by obtaining better performance with a considerable margin regarding different versions of the YOLO detector.

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Enhancing Traffic Signal State Retrieval Reliability in Autonomous Buses Using a Dual-Camera System

Enhancing Traffic Signal State Retrieval Reliability in Auto...

引用

On-Line conference (ONCON), Industrial Electronics society Annual

作者： Ludburgh Miyajima Zhaocheng Gu Daishi Watabe Saitama Institute of Technology Fukaya Japan

ISBN: (数字)9798331540319

ISBN: (纸本)9798331540326

In the context of urban intelligent transportation systems (ITS), ensuring that autonomous buses can follow traffic flow to safeguard passenger safety is fundamental. To achieve this, these buses must accurately obtain the current status of traffic control systems, such as traffic signals. The advanced methods of traffic signal recognition typically rely on computer vision and pattern recognition algorithms, which allow the autonomous vehicle to gather information similarly to a human driver without requiring modifications to existing infrastructure. However, these algorithms face challenges like high computational demands, failure to recognize objects outside the camera's field of view, glare, low light, noise, or poor image quality. This study aims to explore methods for improving traffic signal information retrieval by enhancing existing computer vision-based recognition or through the integration of radio-based data acquisition. A proposed solution combines dual-camera systems with radio-based data collection, offering a more reliable and efficient traffic signal recognition system. This approach effectively enhances the safety of urban ITSs by leveraging a fusion of computer vision and radio technology.

关键词： YOLO Wireless communication computer vision Wireless sensor networks Face recognition Urban areas Traffic control Safety Autonomous vehicles Vehicles

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Space-time Neural Irradiance Fields for Free-Viewpoint Video

Space-time Neural Irradiance Fields for Free-Viewpoint Video

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

作者： Xian, Wenqi Huang, Jia-Bin Kopf, Johannes Kim, Changil Cornell Tech New York NY 10044 USA Virginia Tech Blacksburg VA USA Facebook Menlo Pk CA USA

ISBN: (纸本)9781665445092

We present a method that learns a spatiotemporal neural irradiance field for dynamic scenes from a single video. Our learned representation enables free-viewpoint rendering of the input video. Our method builds upon recent advances in implicit representations. Learning a spatiotemporal irradiance field from a single video poses significant challenges because the video contains only one observation of the scene at any point in time. The 3D geometry of a scene can be legitimately represented in numerous ways since varying geometry (motion) can be explained with varying appearance and vice versa. We address this ambiguity by constraining the time-varying geometry of our dynamic scene representation using the scene depth estimated from video depth estimation methods, aggregating contents from individual frames into a single global representation. We provide an extensive quantitative evaluation and demonstrate compelling free-viewpoint rendering results.

关键词： Geometry computer vision Three-dimensional displays Heuristic algorithms Dynamics Estimation Rendering (computer graphics)

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