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Multi-modal Aerial View Image Challenge: SAR Classification

Multi-modal Aerial View Image Challenge: SAR Classification

引用

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

作者： Spencer Low Oliver Nina Dylan Bowald Angel D. Sappa Nathan Inkawhich Peter Bruns Brigham Young University Provo Utah Air Force Research Laboratory Dayton OH Ecuador Computer Vision Center ESPOL Polytechnic University Spain Air Force Research Laboratory Rome NY University of Utah Salt Lake UT

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This manuscript delineates the outcomes of the fourth Multi-modal Aerial View Image Challenge - Classification (MAVIC-C). The challenge is aimed at advancing the development of recognition models that leverage Synthetic Aperture Radar (SAR) and Electro-Optical (EO) imagery. Encouraging the integration of data from these two distinct modalities, the challenge seeks to foster the creation of multi-modal approaches that complement characteristics of SAR and EO information. Building upon the precedents set in previous years, the 2021 MAVOC challenge validated the potential of integrating SAR and EO modalities. The subsequent 2022 and 2023 challenges further explored the capabilities of multi-modal frameworks. In its latest iteration, the 2024 challenge presents an enhanced UNIfied COincident Optical and Radar for recognition (UNICORN) dataset alongside a revised competition format, focused on the task of SAR classification. The 2024 challenge evaluates model robustness through out-of-distribution measures, alongside traditional accuracy metrics. The core of this paper is devoted to analyzing the methodologies of the top-performing entries and their performance metrics on a blind test set.

关键词： Measurement Laser radar Image recognition Pipelines Optical imaging Robustness pattern recognition

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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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S3R-Net: A Single-Stage Approach to Self-Supervised Shadow Removal

S3R-Net: A Single-Stage Approach to Self-Supervised Shadow R...

引用

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

作者： Nikolina Kubiak Armin Mustafa Graeme Phillipson Stephen Jolly Simon Hadfield University of Surrey BBC R&D

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper we present S3R-Net, the Self-Supervised Shadow Removal Network. The two-branch WGAN model achieves self-supervision 1 relying on the unify-and-adapt phenomenon - it unifies the style of the output data and infers its characteristics from a database of unaligned shadow-free reference images. This approach stands in contrast to the large body of supervised frameworks. S3R-Net also differentiates itself from the few existing self-supervised models operating in a cycle-consistent manner, as it is a non-cyclic, unidirectional solution. The proposed framework achieves comparable numerical scores to recent self-supervised shadow removal models while exhibiting superior qualitative performance and keeping the computational cost low. The pre-trained models and the code can be found in our github repo.

关键词： computer vision Codes Databases Computational modeling conferences Data models Numerical models

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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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I Find Your Lack of Uncertainty in computer vision Disturbing

I Find Your Lack of Uncertainty in Computer Vision Disturbin...

引用

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

作者： Valdenegro-Toro, Matias German Res Ctr Artificial Intelligence Robert Hooke Str 1 D-28359 Bremen Germany

ISBN: (纸本)9781665448994

Neural networks are used for many real world applications, but often they have problems estimating their own confidence. This is particularly problematic for computer vision applications aimed at making high stakes decisions with humans and their lives. In this paper we make a meta-analysis of the literature, showing that most if not all computer vision applications do not use proper epistemic uncertainty quantification, which means that these models ignore their own limitations. We describe the consequences of using models without proper uncertainty quantification, and motivate the community to adopt versions of the models they use that have proper calibrated epistemic uncertainty, in order to enable out of distribution detection. We close the paper with a summary of challenges on estimating uncertainty for computer vision applications and recommendations.

关键词： computer vision Uncertainty Computational modeling conferences Neural networks pattern recognition

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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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Delta Sampling R-BERT for limited data and low-light action recognition

Delta Sampling R-BERT for limited data and low-light action ...

引用

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

作者： Hira, Sanchit Das, Ritwik Modi, Abhinav Pakhomov, Daniil Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9781665448994

We present an approach to perform supervised action recognition in the dark. In this work, we present our results on the ARID dataset[60]. Most previous works only evaluate performance on large, well illuminated datasets like Kinetics and HMDB51. We demonstrate that our work is able to achieve a very low error rate while being trained on a much smaller dataset of dark videos. We also explore a variety of training and inference strategies including domain transfer methodologies and also propose a simple but useful frame selection strategy. Our empirical results demonstrate that we beat previously published baseline models by 11%.

关键词： Training Deep learning Technological innovation Schedules computer vision conferences pattern recognition

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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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A Lightweight Spatiotemporal Network for Online Eye Tracking with Event Camera

A Lightweight Spatiotemporal Network for Online Eye Tracking...

引用

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

作者： Yan Ru Pei Sasskia Brüers Sébastien Crouzet Douglas McLelland Olivier Coenen Brainchip Inc. Laguna Hills CA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Event-based data are commonly encountered in edge computing environments where efficiency and low latency are critical. To interface with such data and leverage their rich temporal features, we propose a causal spatiotemporal convolutional network. This solution targets efficient implementation on edge-appropriate hardware with limited resources in three ways: 1) deliberately targets a simple architecture and set of operations (convolutions, ReLU activations) 2) can be configured to perform online inference efficiently via buffering of layer outputs 3) can achieve more than 90% activation sparsity through regularization during training, enabling very significant efficiency gains on event-based processors. In addition, we propose a general affine augmentation strategy acting directly on the events, which alleviates the problem of dataset scarcity for event-based systems. We apply our model on the AIS 2024 event-based eye tracking challenge, reaching a score of 0.9916 p10 accuracy on the Kaggle private testset.

关键词： Training Program processors Gaze tracking Streaming media Spatiotemporal phenomena pattern recognition Convolutional neural networks

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Exploring AIGC Video Quality: A Focus on Visual Harmony, Video-Text Consistency and Domain Distribution Gap

Exploring AIGC Video Quality: A Focus on Visual Harmony, Vid...

引用

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

作者： Bowen Qu Xiaoyu Liang Shangkun Sun Wei Gao School of Electronic and Computer Engineering Peking University China Peng Cheng Laboratory China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The recent advancements in Text-to-Video Artificial Intelligence Generated Content (AIGC) have been remarkable. Compared with traditional videos, the assessment of AIGC videos encounters various challenges: visual inconsistency that defy common sense, discrepancies between content and the textual prompt, and distribution gap between various generative models, etc. Target at these challenges, in this work, we categorize the assessment of AIGC video quality into three dimensions: visual harmony, videotext consistency, and domain distribution gap. For each dimension, we design specific modules to provide a comprehensive quality assessment of AIGC videos. Furthermore, our research identifies significant variations in visual quality, fluidity, and style among videos generated by different text-to-video models. Predicting the source generative model can make the AIGC video features more discriminative, which enhances the quality assessment performance. The proposed method was used in the third-place winner of the NTIRE 2024 Quality Assessment for AI-Generated Content - Track 2 Video, demonstrating its effectiveness.

关键词： Visualization computer vision conferences Computational modeling Predictive models Quality assessment pattern recognition

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