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DiffSeg: Towards Detecting Diffusion-Based Inpainting Attacks Using Multi-Feature Segmentation

DiffSeg: Towards Detecting Diffusion-Based Inpainting Attack...

引用

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

作者： Raphael Antonius Frick Martin Steinebach Fraunhofer SIT — ATHENE Center Darmstadt Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

With the advancements made in deep learning over the past years, creating convincing media manipulations has become easy and accessible than ever before. In particular, diffusion models such as Stable-Diffusion allow users to synthesize realistic images based on a given text input. Apart from synthesizing entirely new images, diffusion models can also be used to make edits to images using inpainting. To combat the spread of disinformation and illegal content created with diffusion-based inpainting, this paper presents a new detection method based on multi-feature segmentation. Apart from information derived from the raw pixel values, noise, and frequency information are also exploited to detect and localize regions that have been subject to editing. Evaluation results strongly suggest that the proposed method can achieve high mIoU and AUC scores, outperforming state-of-the-art methods, even for syntheses generated by unseen diffusion models, or highly compressed images.

关键词： Deep learning Image segmentation computer vision Image coding conferences Noise Diffusion models

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Efficient CNN Architecture for Multi-modal Aerial View Object Classification

Efficient CNN Architecture for Multi-modal Aerial View Objec...

引用

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

作者： Miron, Casian Pasarica, Alexandru Timofte, Radu Gheorghe Asachi Tech Univ MCC Resources SRL Iasi Romania

ISBN: (纸本)9781665448994

The NTIRE 2021 workshop features a Multi-modal Aerial View Object Classification Challenge. Its focus is on multi-sensor imagery classification in order to improve the performance of automatic target recognition (ATR) systems. In this paper we describe our entry in this challenge, a method focused on efficiency and low computational time, while maintaining a high level of accuracy. The method is a convolutional neural network with 11 convolutions, 1 max pooling layers and 3 residual blocks which has a total of 373.130 parameters. The method ranks 3rd in the Track 2 (SAR+EO) of the challenge.

关键词： Training computer vision Target recognition conferences Systems architecture Graphics processing units computer architecture

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Multi-Class Multi-Movement Vehicle Counting Based on CenterTrack

Multi-Class Multi-Movement Vehicle Counting Based on CenterT...

引用

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

作者： Kocur, Viktor Ftacnik, Milan Comenius Univ Fac Math Phys & Informat Bratislava Slovakia

ISBN: (纸本)9781665448994

In this paper we present our approach to the Track 1 of the 2021 AI City Challenge. The goal of the challenge track is to to analyse footage captured with traffic cameras by counting the number of vehicles performing various pre-defined motions of interest. Our approach is based on the CenterTrack object detection and tracking neural network used in conjunction with a simple IoU-based tracking algorithm. In the public evaluation server our system achieved the S1 score of 0.8449 placing it at the 8th place on the public leaderboard.

关键词： computer vision Tracking conferences Urban areas Neural networks Object detection computer architecture

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Test-time Specialization of Dynamic Neural Networks

Test-time Specialization of Dynamic Neural Networks

引用

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

作者： Sam Leroux Dewant Katare Aaron Yi Ding Pieter Simoens Department of Information Technology IDLab Ghent University - Imec Belgium Department of Engineering Systems and Services Delft University of Technology The Netherlands

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years, there has been a notable increase in the size of commonly used image classification models. This growth has empowered models to recognize thousands of diverse object types. However, their computational demands pose significant challenges, especially when deploying them on resource-constrained edge devices. In many use cases where a model is deployed on an edge device, only a small subset of the classes will ever be observed by a given model instance. Our proposed test-time specialization of dynamic neural networks allows these models to become faster at recognizing the classes that are observed frequently, while maintaining the ability to recognize all other classes, albeit slightly less efficient. We benchmark our approach on a real-world edge device, obtaining significant speedups compared to the baseline model without test-time adaptation.

关键词： Adaptation models computer vision Computational modeling Image edge detection conferences Neural networks Benchmark testing

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MTN: Forensic Analysis of MP4 Video Files Using Graph Neural Networks

MTN: Forensic Analysis of MP4 Video Files Using Graph Neural...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Xiang, Ziyue Singh Yadav, Amit Kumar Bestagini, Paolo Tubaro, Stefano Delp, Edward J. School of Electrical and Computer Engineering West LafayetteIN United States Politecnico di Milano Dipartimento di Elettronica Informazione e Bioingegneria Milano Italy

ISBN: (纸本)9798350302493

MP4 video files are stored using a tree data structure. These trees contain rich information that can be used for forensic analysis. In this paper, we propose MP4 Tree Network (MTN), an approach based on an end-to-end Graph Neural Networks (GNNs) that is used for forensic analysis of MP4 trees. MTN does not use any video pixel data. MTN is trained using Self-Supervised Learning (SSL), which generates semantic-preserving node embeddings for the nodes in an MP4 tree. We also propose a data augmentation technique for MP4 trees, which helps train MTN in data-scarce scenarios. MTN achieves good performance across 3 video forensics tasks on the EVA-7K dataset. We show that MTN can gain more comprehensive understanding about the MP4 trees and is more robust to potential attacks compared to existing methods. © 2023 ieee.

关键词： Trees (mathematics)

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M3ED: Multi-Robot, Multi-Sensor, Multi-Environment Event Dataset

M3ED: Multi-Robot, Multi-Sensor, Multi-Environment Event Dat...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Chaney, Kenneth Cladera, Fernando Wang, Ziyun Bisulco, Anthony Hsieh, M. Ani Korpela, Christopher Kumar, Vijay Taylor, Camillo J. Daniilidis, Kostas University of Pennsylvania GRASP Laboratory PA19104 United States Robotics Research Center Department of Electrical Engineering and Computer Science United States Military Academy West PointNY10996 United States

ISBN: (纸本)9798350302493

We present M3ED, the first multi-sensor event camera dataset focused on high-speed dynamic motions in robotics applications. M3ED provides high-quality synchronized and labeled data from multiple platforms, including ground vehicles, legged robots, and aerial robots, operating in challenging conditions such as driving along off-road trails, navigating through dense forests, and performing aggressive flight maneuvers. Our dataset also covers demanding operational scenarios for event cameras, such as scenes with high egomotion and multiple independently moving objects. The sensor suite used to collect M3ED includes high-resolution stereo event cameras (1280×720), grayscale imagers, an RGB imager, a high-quality IMU, a 64-beam LiDAR, and RTK localization. This dataset aims to accelerate the development of event-based algorithms and methods for edge cases encountered by autonomous systems in dynamic environments. © 2023 ieee.

关键词： Cameras

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Using Language-Aligned Gesture Embeddings for Understanding Gestures Accompanying Math Terms

Using Language-Aligned Gesture Embeddings for Understanding ...

引用

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

作者： Tristan Maidment Purav J Patel Erin Walker Adriana Kovashka Intelligent Systems Program University of Pittsburgh Pittsburgh PA USA University of Maryland College Park MD USA Computer Science University of Pittsburgh Pittsburgh PA USA Learning Research and Development Center University of Pittsburgh Pittsburgh PA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we introduce an approach for recognizing and classifying gestures that accompany mathematical terms, in a new collection we name the "GAMT" dataset. Our method uses language as a means of providing context to classify gestures. Specifically, we use a CLIP-style framework to construct a shared embedding space for gestures and language, experimenting with various methods for encoding gestures within this space. We evaluate our method on our new dataset containing a wide array of gestures associated with mathematical terms. The shared embedding space leads to a substantial improvement in gesture classification. Furthermore, we identify an efficient model that excelled at classifying gestures from our unique dataset, thus contributing to the further development of gesture recognition in diverse interaction scenarios.

关键词： computer vision conferences Computational modeling Gesture recognition Mathematical models Encoding

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Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertainty in Regression Tasks

Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertain...

引用

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

作者： Ziliang Xiong Arvi Jonnarth Abdelrahman Eldesokey Joakim Johnander Bastian Wandt Per-Erik Forssén Department of Electrical Engineering Computer Vision Laboratory LiU Sweden Husqvarna Group Huskvarna Sweden Visual Computing Center KAUST Saudi Arabia Zenseact Sweden

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

computer vision systems that are deployed in safety-critical applications need to quantify their output uncertainty. We study regression from images to parameter values and here it is common to detect uncertainty by predicting probability distributions. In this context, we investigate the regression-by-classification paradigm which can represent multimodal distributions, without a prior assumption on the number of modes. Through experiments on a specifically designed synthetic dataset, we demonstrate that traditional loss functions lead to poor probability distribution estimates and severe overconfidence, in the absence of full ground truth distributions. In order to alleviate these issues, we propose hinge-Wasserstein – a simple improvement of the Wasserstein loss that reduces the penalty for weak secondary modes during training. This enables prediction of complex distributions with multiple modes, and allows training on datasets where full ground truth distributions are not available. In extensive experiments, we show that the proposed loss leads to substantially better uncertainty estimation on two challenging computer vision tasks: horizon line detection and stereo disparity estimation.

关键词： Training computer vision Uncertainty conferences Estimation Probability distribution pattern recognition

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DDOS: The Drone Depth and Obstacle Segmentation Dataset

DDOS: The Drone Depth and Obstacle Segmentation Dataset

引用

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

作者： Benedikt Kolbeinsson Krystian Mikolajczyk Imperial College London

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The advancement of autonomous drones, essential for sectors such as remote sensing and emergency services, is hindered by the absence of training datasets that fully capture the environmental challenges present in real-world scenarios, particularly operations in non-optimal weather conditions and the detection of thin structures like wires. We present the Drone Depth and Obstacle Segmentation (DDOS) dataset to fill this critical gap with a collection of synthetic aerial images, created to provide comprehensive training samples for semantic segmentation and depth estimation. Specifically designed to enhance the identification of thin structures, DDOS allows drones to navigate a wide range of weather conditions, significantly elevating drone training and operational safety. Additionally, this work introduces innovative drone-specific metrics aimed at refining the evaluation of algorithms in depth estimation, with a focus on thin structure detection. These contributions not only pave the way for substantial improvements in autonomous drone technology but also set a new benchmark for future research, opening avenues for further advancements in drone navigation and safety.

关键词： Measurement Training computer vision Navigation Semantic segmentation Wires Estimation

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Hierarchical Clustering and Refinement for Generalized Multi-Camera Person Tracking

Hierarchical Clustering and Refinement for Generalized Multi...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Li, Zongyi Wang, Runsheng Li, He Wei, Bohao Shi, Yuxuan Ling, Hefei Chen, Jiazhong Liu, Boyuan Li, Zhongyang Zheng, Hanqing Huazhong University of Science and Technology Department of Computer Science and Technology China

ISBN: (纸本)9798350302493

Multi-camera person tracking has gained significant attention in recent times, owing to its widespread application in surveillance scenarios. However, this task is challenging due to the variance viewpoints, heavy occlusion, and illumination changes. In order to tackle these challenges, we propose a novel Hierarchical Clustering and Refinement framework for Generalized Multi-Camera Person Tracking. Specifically, our framework comprises two main components: hierarchical clustering and hierarchical refinement. Compared with directly clustering tracklets among multiple cameras, our hierarchical clustering strategy can progressively assign tracklets to correct targets. Nevertheless, the clustering and tracking process would inevitably produce incorrect matchings. Therefore, a hierarchical refinement strategy is proposed to reduce these incorrect matches which includes: intra-camera tracklet level refinement, appearance refinement, spatial-temporal refinement, and face refinement. Extensive experiments show the effectiveness of our method, which achieves 92% IDF1 in 2023 AI CITY CHALLENGE track1, ranking 5th on the leaderboard. © 2023 ieee.

关键词： Security systems

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