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Rethinking Adversarial Examples in Wargames

Rethinking Adversarial Examples in Wargames

引用

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

作者： Chen, Yuwei Chinese Aeronaut Estab Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Artificial intelligence technology is increasingly widely used in games, especially for wargames. The addition of artificial intelligence algorithms enables these games to solve decision-making problems in complex environments more quickly, surpassing the vast majority of experienced human players in competitive games. However, due to the vulnerability of the neural network before adversarial examples, all modules using artificial intelligence algorithms are at risk of being attacked. For wargames, adversarial examples will make the units in the game no longer able to follow the established routes or actions to perform tasks. Based on such risks, this paper proposes a deceptive concept scheme of attacking intelligent modules in wargames through adversarial examples, and proposes challenges and prospects for current technologies. To our knowledge, we are the first team to analyze the impact of adversarial examples in the running process of wargames, namely the OODA loop, and simulate them in the corresponding wargaming software. In the end, we found that when artificial intelligence technology is widely used in war games, adversarial examples will have a subversive impact on several activities in several steps, which will directly lead to the failure to complete the established game goals.

关键词： Neural networks Decision making Games Prediction algorithms Software pattern recognition Security

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Layer Importance Estimation with Imprinting for Neural Network Quantization

Layer Importance Estimation with Imprinting for Neural Netwo...

引用

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

作者： Liu, Hongyang Elkerdawy, Sara Ray, Nilanjan Elhoushi, Mostafa Univ Alberta Edmonton AB Canada Huawei Shenzhen Peoples R China

ISBN: (纸本)9781665448994

Neural network quantization has achieved a high compression rate using fixed low bit-width representation of weights and activations while maintaining the accuracy of the high-precision original network. However, mixed precision (per-layer bit-width precision) quantization requires careful tuning to maintain accuracy while achieving further compression and higher granularity than fixed-precision quantization. We propose an accuracy-aware criterion to quantify the layer's importance rank. Our method applies imprinting per layer which acts as a proxy module for accuracy estimation in an efficient way. We rank the layers based on the accuracy gain from previous modules and iteratively quantize first those with less accuracy gain. Previous mixed-precision methods either rely on expensive search techniques such as reinforcement learning (RL) or end-to-end optimization with a lack of interpretation to the quantization configuration scheme. Our method is a one-shot, efficient, accuracy-aware information estimation and thus draws better interpretability to the selected bit-width configuration.

关键词： Training Quantization (signal) Search methods Neural networks Estimation Reinforcement learning pattern recognition

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Towards Semantic Understanding of Surrounding Vehicular Maneuvers: A Panoramic vision-Based Framework for Real-World Highway Studies 29

Towards Semantic Understanding of Surrounding Vehicular Mane...

引用

29th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Kristoffersen, Miklas S. Dueholm, Jacob V. Satzoda, Ravi K. Trivedi, Mohan M. Mogelmose, Andreas Moeslund, Thomas B. Univ Calif San Diego San Diego CA 92103 USA Aalborg Univ Aalborg Denmark

ISBN: (纸本)9781509014378

This paper proposes the use of multiple low-cost visual sensors to obtain a surround view of the ego-vehicle for semantic understanding. A multi-perspective view will assist the analysis of naturalistic driving studies (NDS), by automating the task of data reduction of the observed sequences into events. A user-centric vision-based framework is presented using a vehicle detector and tracker in each separate perspective. Multi-perspective trajectories are estimated and analyzed to extract 14 different events, including potential dangerous behaviors such as overtakes and cut-ins. The system is tested on ten sequences of real-world data collected on U.S. highways. The results show the potential use of multiple low-cost visual sensors for semantic understanding around the ego-vehicle.

关键词： Vehicles

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Extracurricular Learning: Knowledge Transfer Beyond Empirical Distribution

Extracurricular Learning: Knowledge Transfer Beyond Empirica...

引用

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

作者： Pouransari, Hadi Javaheripi, Mojan Sharma, Vinay Tuzel, Oncel Apple Cupertino CA 95014 USA UCSD La Jolla CA USA

ISBN: (纸本)9781665448994

Knowledge distillation has been used to transfer knowledge learned by a sophisticated model (teacher) to a simpler model (student). This technique is widely used to compress model complexity. However, in most applications the compressed student model suffers from an accuracy gap with its teacher. We propose extracurricular learning, a novel knowledge distillation method, that bridges this gap by (1) modeling student and teacher output distributions;(2) sampling examples from an approximation to the underlying data distribution;and (3) matching student and teacher output distributions over this extended set including uncertain samples. We conduct rigorous evaluations on regression and classification tasks and show that compared to the standard knowledge distillation, extracurricular learning reduces the gap by 46 % to 68 %. This leads to major accuracy improvements compared to the empirical risk minimization-based training for various recent neural network architectures: 16% regression error reduction on the MPIIGaze dataset, +3.4% to +9.1% improvement in top-1 classification accuracy on the CIFAR100 dataset, and +2.9% top-1 improvement on the ImageNet dataset.

关键词： Training Uncertainty Neural networks Estimation Data models pattern recognition Task analysis

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Person-independent 3D Gaze Estimation using Face Frontalization 29

Person-independent 3D Gaze Estimation using Face Frontalizat...

引用

29th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Jeni, Laszlo A. Cohn, Jeffrey F. Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Pittsburgh Pittsburgh PA USA

ISBN: (纸本)9781509014378

Person-independent and pose-invariant estimation of eye-gaze is important for situation analysis and for automated video annotation. We propose a fast cascade regression based method that first estimates the location of a dense set of markers and their visibility, then reconstructs face shape by fitting a part-based 3D model. Next, the reconstructed 3D shape is used to estimate a canonical view of the eyes for 3D gaze estimation. The model operates in a feature space that naturally encodes local ordinal properties of pixel intensities leading to photometric invariant estimation of gaze. To evaluate the algorithm in comparison with alternative approaches, three publicly-available databases were used, Boston University Head Tracking, Multi-View Gaze and CAVE Gaze datasets. Precision for head pose and gaze averaged 4 degrees or less for pitch, yaw, and roll. The algorithm outperformed alternative methods in both datasets.

关键词： Three dimensional computer graphics

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Learning Optimized Low-Light Image Enhancement for Edge vision Tasks

Learning Optimized Low-Light Image Enhancement for Edge Visi...

引用

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

作者： Sharif, S. M. A. Myrzabekov, Azamat Khujaev, Nodirkhuja Tsoy, Roman Kim, Seongwan Lee, Jaeho LG Sciencepk Seoul South Korea

ISBN: (纸本)9798350365474

Low-light image enhancement (LLIE) has a significant role in edge vision applications (EVA). Despite its widespread practicability, the existing LLIE methods are impractical due to their high computational costs. This study proposed a framework to learn optimized low-light image enhancement to tackle the limitations of existing enhancement methods for accelerating EVA. The proposed framework incorporates a lightweight and mobile-friendly deep network. We optimized our proposed model with INT8 precision with a post-training quantization strategy and deployed it on an edge device. The LLIE model has achieved over 199 frames per second (FPS) on a low-power edge board. Additionally, we evaluated the practicability of an optimized model for accelerating the vision application of an edge environment. The experimental results illustrate that our optimized method can significantly accelerate the performance of SOTA vision algorithms in challenging low-light conditions for numerous everyday vision tasks, including object detection and image registration.

关键词： Edge Device Edge LLIE Edge vision Task Low-light Image Enhancement Optimized LLIE Quantization

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Unsupervised Change Detection Based on Image Reconstruction Loss

Unsupervised Change Detection Based on Image Reconstruction ...

引用

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

作者： Noh, Hyeoncheol Ju, Jingi Seo, Minseok Park, Jongchan Choi, Dong-Geol Hanbat Natl Univ Daejeon South Korea SI Analyt Inc Mainz Germany Lunit Inc Seoul South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

To train a change detector, bi-temporal images taken at different times in the same area are used. However, collecting labeled bi-temporal images is expensive and time consuming. To solve this problem, various unsupervised change detection methods have been proposed, but they still require unlabeled bi-temporal images. In this paper, we propose an unsupervised change detection method based on image reconstruction loss, which uses only a single-temporal unlabeled image. The image reconstruction model was trained to reconstruct the original source image by receiving the source image and photometrically transformed source image as a pair. During inference, the model receives bi-temporal images as input and aims to reconstruct one of the inputs. The changed region between bi-temporal images shows high reconstruction loss. Our change detector demonstrated significant performance on various change detection benchmark datasets even though only a single-temporal source image was used. The code and trained models are available in https://***/cjf8899/CDRL

关键词： computer vision Codes conferences Semantics Detectors Benchmark testing pattern recognition

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ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging

ADNet: Attention-guided Deformable Convolutional Network for...

引用

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

作者： Liu, Zhen Lin, Wenjie Li, Xinpeng Rao, Qing Jiang, Ting Han, Mingyan Fan, Haoqiang Sun, Jian Liu, Shuaicheng Megvii Technol Beijing Peoples R China Sichuan Univ Chengdu Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9781665448994

In this paper, we present an attention-guided deformable convolutional network for hand-held multi frame high dynamic range (HDR) imaging, namely ADNet. This problem comprises two intractable challenges of how to handle saturation and noise properly and how to tackle misalignments caused by object motion or camera jittering. To address the former, we adopt a spatial attention module to adaptively select the most appropriate regions of various exposure low dynamic range (LDR) images for fusion. For the latter one, we propose to align the gamma-corrected images in the feature-level with a Pyramid, Cascading and Deformable (PCD) alignment module. The proposed ADNet shows state-of-the-art performance compared with previous methods, achieving a PSNR-l of 39.4471 and a PSNR-mu of 37.6359 in NTIRE 2021 Multi-Frame HDR Challenge.

关键词： Convolutional codes computer vision conferences Pipelines Dynamic range Cameras pattern recognition

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PanoDR: Spherical Panorama Diminished Reality for Indoor Scenes

PanoDR: Spherical Panorama Diminished Reality for Indoor Sce...

引用

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

作者： Gkitsas, Vasileios Sterzentsenko, Vladimiros Zioulis, Nikolaos Albanis, Georgios Zarpalas, Dimitrios Ctr Res & Technol Hellas Thessaloniki Greece

ISBN: (纸本)9781665448994

The rising availability of commercial 360 degrees cameras that democratize indoor scanning, has increased the interest for novel applications, such as interior space re-design. Diminished Reality (DR) fulfills the requirement of such applications, to remove existing objects in the scene, essentially translating this to a counterfactual inpainting task. While recent advances in data-driven inpainting have shown significant progress in generating realistic samples, they are not constrained to produce results with reality mapped structures. To preserve the 'reality' in indoor (re-)planning applications, the scene's structure preservation is crucial. To ensure structure-aware counterfactual inpainting, we propose a model that initially predicts the structure of a indoor scene and then uses it to guide the reconstruction of an empty - background only - representation of the same scene. We train and compare against other state-of-the-art methods on a version of the Structured3D dataset [47] modified for DR, showing superior results in both quantitative metrics and qualitative results, but more interestingly, our approach exhibits a much faster convergence rate. Code and models are available at ***/VCL3D/PanoDR/

关键词： Measurement Image segmentation computer vision conferences Predictive models Planning pattern recognition

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Joint Forecasting of Panoptic Segmentations with Difference Attention

Joint Forecasting of Panoptic Segmentations with Difference ...

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

作者： Graber, Colin Jazra, Cyril Luo, Wenjie Gui, Liangyan Schwing, Alexander Univ Illinois Urbana IL 61801 USA Waymo Mountain View CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665487399

Forecasting of a representation is important for safe and effective autonomy. For this, panoptic segmentations have been studied as a compelling representation in recent work. However, recent state-of-the-art on panoptic segmentation forecasting suffers from two issues: first, individual object instances are treated independently of each other;second, individual object instance forecasts are merged in a heuristic manner. To address both issues, we study a new panoptic segmentation forecasting model that jointly forecasts all object instances in a scene using a transformer model based on 'difference attention.' It further refines the predictions by taking depth estimates into account. We evaluate the proposed model on the Cityscapes and AIODrive datasets. We find difference attention to be particularly suitable for forecasting because the difference of quantities like locations enables a model to explicitly reason about velocities and acceleration. Because of this, we attain state-of-the-art on panoptic segmentation forecasting metrics.

关键词： Measurement computer vision conferences Predictive models Transformers pattern recognition Forecasting

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