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RUIG: Realistic Underwater Image Generation Towards Restoration

RUIG: Realistic Underwater Image Generation Towards Restorat...

引用

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

作者： Desai, Chaitra Tabib, Ramesh Ashok Reddy, Sai Sudheer Patil, Ujwala Mudenagudi, Uma KLE Technol Univ Ctr Excellence Visual Intelligence CEVI Hubballi Karnataka India

ISBN: (纸本)9781665448994

In this paper, we present a novel method for generating synthetic underwater images considering revised image formation model. We propose to use the generated synthetic underwater images to train a conditional generative adversarial network (CGAN) towards restoration of degraded underwater images. Restoration of degraded underwater images using traditional dehazing models is challenging as they are insensitive to wavelength, depth, water type and treat backscattering and direct signal attenuation coefficients to be equal. However, learning based models for restoration perform well but sensitive to availability of ground truth information. Generating ground truth labels in underwater scenario demands in-situ measurements using expensive equipments and is infeasible due to varying underwater currents. Towards this, we propose to generate synthetic underwater images using revised image formation model. Revised image formation model is sensitive to different attenuation coefficients: 1) back scattering, 2) direct scattering and 3) veiling light. We propose to estimate these attenuation coefficients considering proven facts from the literature. We demonstrate restoration of real underwater images through restoration framework trained using rendered synthetic underwater images, and compare results of restoration with state-of-the-art techniques.

关键词： computer vision Image synthesis Wavelength measurement Current measurement conferences Scattering Attenuation

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Deep Video Codec Control for vision Models

Deep Video Codec Control for Vision Models

引用

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

作者： Christoph Reich Biplob Debnath Deep Patel Tim Prangemeier Daniel Cremers Srimat Chakradhar NEC Laboratories America Inc. TU Munich TU Darmstadt Munich Center for Machine Learning (MCML)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Standardized lossy video coding is at the core of almost all real-world video processing pipelines. Rate control is used to enable standard codecs to adapt to different network bandwidth conditions or storage constraints. However, standard video codecs (e.g., H.264) and their rate control modules aim to minimize video distortion w.r.t. human quality assessment. We demonstrate empirically that standard-coded videos vastly deteriorate the performance of deep vision models. To overcome the deterioration of vision performance, this paper presents the first end-to-end learnable deep video codec control that considers both bandwidth constraints and downstream deep vision performance, while adhering to existing standardization. We demonstrate that our approach better preserves downstream deep vision performance than traditional standard video coding.

关键词： Video coding Computational modeling Face recognition Pipelines Bandwidth Self-supervised learning Distortion

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EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task.

EarthNet2021: A large-scale dataset and challenge for Earth ...

引用

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

作者： Requena-Mesa, Christian Benson, Vitus Reichstein, Markus Runge, Jakob Denzler, Joachim Max Planck Inst Biogeochem Biogeochem Integrat Jena Germany German Aerosp Ctr DLR Inst Data Sci Jena Germany Univ Jena Comp Vis Grp Jena Germany Michael Stifel Ctr Jena Data Driven & Simulat Sci Jena Germany Tech Univ Berlin Berlin Germany

ISBN: (纸本)9781665448994

Satellite images are snapshots of the Earth surface. We propose to forecast them. We frame Earth surface forecasting as the task of predicting satellite imagery conditioned on future weather. EarthNet2021 is a large dataset suitable for training deep neural networks on the task. It contains Sentinel 2 satellite imagery at 20 m resolution, matching topography and mesoscale (1.28 km) meteorological variables packaged into 32000 samples. Additionally we frame EarthNet2021 as a challenge allowing for model intercomparison. Resulting forecasts will greatly improve (> x50) over the spatial resolution found in numerical models. This allows localized impacts from extreme weather to be predicted, thus supporting downstream applications such as crop yield prediction, forest health assessments or biodiversity monitoring. Find data, code, and how to participate at ***.

关键词： Earth Training Satellites Weather forecasting Surface topography Numerical models pattern recognition

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Human vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor Scenes

Human Vision Based 3D Point Cloud Semantic Segmentation of L...

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

作者： Sunghwan Yoo Yeonjeong Jeong Maryam Jameela Gunho Sohn Department of Earth and Space Science and Engineering York University Toronto Canada

This paper proposes EyeNet, a novel semantic segmentation network for point clouds that addresses the critical yet often overlooked parameter of coverage area size. Inspired by human peripheral vision, EyeNet overcomes the limitations of conventional networks by introducing a simple but efficient multi-scale input and a parallel processing network with connection blocks between parallel streams. The proposed approach effectively addresses the challenges of dense point clouds, as demonstrated by our ablation studies and state-of-the-art performance on Large-Scale Outdoor datasets.

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Vehicle Re-Identification based on Ensembling Deep Learning Features including a Synthetic Training Dataset, Orientation and Background Features, and Camera Verification.

Vehicle Re-Identification based on Ensembling Deep Learning ...

引用

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

作者： Fernandez, Marta Moral, Paula Garcia-Martin, Alvaro Martinez, Jose M. Univ Autonoma Madrid Video Proc & Understanding Lab Madrid Spain

ISBN: (纸本)9781665448994

Vehicle re-identification has the objective of finding a specific vehicle among different vehicle crops captured by multiple cameras placed at multiple intersections. Among the different difficulties, high intra-class variability and high inter-class similarity can be highlighted. Moreover, the resolution of the images can be different, which also means a challenge in the re-identification task. Intending to face these problems, we use as baseline our previous work based on obtaining different deep learning features and ensembling them to get a single, stable and robust feature vector. It also includes post-processing techniques that explode all the information provided by the CityFlowV2-ReID dataset, including a re-ranking step. Then, in this paper, several newly included improvements are described. Background and orientation similarity matrices are added to the system to reduce bias towards these characteristics. Furthermore, we take into account the camera labels to penalize the gallery images that share camera with the query image. Additionally, to improve the training step, a synthetic dataset is added to the original one.

关键词： Training Deep learning Image resolution Image color analysis Cameras Feature extraction pattern recognition

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A Generative Exploration of Cuisine Transfer

A Generative Exploration of Cuisine Transfer

引用

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

作者： Philip Wootaek Shin Ajay Narayanan Sridhar Jack Sampson Vijaykrishnan Narayanan The Pennsylvania State University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent research has made significant progress in text-to-image editing, yet numerous areas remain under explored. In this work, we propose a novel application in the culinary arts, leveraging diffusion models to adjust a range of dishes into a variety of cuisines. Our approach infuses each dish with unique twists representative of diverse culinary traditions and ingredient profiles. We introduce the Cuisine Transfer task and a comprehensive framework for its execution, along with a curated dataset comprising over 1600 unique food samples at the ingredient level. Additionally, we propose three Cuisine Transfer task specific metrics to accurately evaluate our method and address common failure scenarios in existing image editing techniques. Our evaluations demonstrate that our method significantly outperforms baseline models on the Cuisine Transfer task.

关键词： Measurement Training computer vision Art Computational modeling conferences Text to image

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Supervised Contrastive Learning for Snapshot Spectral Imaging Face Anti-Spoofing

Supervised Contrastive Learning for Snapshot Spectral Imagin...

引用

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

作者： Chuanbiao Song Yan Hong Jun Lan Huijia Zhu Weiqiang Wang Jianfu Zhang Ant Group Qing Yuan Research Institute Shanghai Jiao Tong University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This study reveals a cutting-edge re-balanced contrastive learning strategy aimed at strengthening face anti-spoofing capabilities within facial recognition systems, with a focus on countering the challenges posed by printed photos, and highly realistic silicone or latex masks. Leveraging the HySpeFAS dataset, which benefits from Snapshot Spectral Imaging technology to provide hyperspectral images, our approach harmonizes class-level contrastive learning with data resampling and an innovative real-face oriented reweighting technique. This method effectively mitigates dataset imbalances and reduces identity-related biases. Notably, our strategy achieved an unprecedented 0.0000% Average Classification Error Rate (ACER) on the HySpeFAS dataset, ranking first at the Chalearn Snapshot Spectral Imaging Face Anti-spoofing Challenge on CVPR 2024.

关键词： Training computer vision Error analysis Face recognition conferences Neural networks Imaging

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Spot the GEO Satellites: From Dataset to Kelvins SpotGEO Challenge

Spot the GEO Satellites: From Dataset to Kelvins SpotGEO Cha...

引用

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

作者： Chen, Bo Liu, Daqi Chin, Tat-Jun Rutten, Mark Derksenv, Dawa Martens, Marcus von Looz, Moritz Lecuyer, Gurvan Izzo, Dario Univ Adelaide Adelaide SA Australia InTrack Solut Adelaide SA Australia European Space Agcy Paris France

ISBN: (纸本)9781665448994

The Geosynchronous Equatorial Orbit (GEO) is home to many important space assets such as telecommunication and navigational satellites. Monitoring Resident Space Objects (RSOs) in GEO is a crucial aspect in achieving Space Situational Awareness (SSA) and in protecting critical space assets. However, ground-based GEO object detection is challenging due to the extreme distance of the targets, as well as nuisance factors including cloud coverage, atmospheric/weather effects, light pollution, sensor noise/defects, and star occlusions. The Kelvins SpotGEO Challenge is designed to establish to what extent images coming from a low-cost ground-based telescope can be used to detect GEO and near-GEO RSOs solely from photometric signals that are without any additional meta-data. At the same time, the SpotGEO dataset also addresses the lack of publicly available datasets from a computer vision perspective on the satellite detection problem;by assembling and releasing such a dataset, we hope to spur more efforts on the optical detection of RSOs and enable objective bench-marking for existing and future methods. In this work, we present details of the SpotGEO dataset development, challenge design, evaluation metric, and result analysis.

关键词： Space vehicles Measurement computer vision Satellites Pollution Kelvin Tools

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Emotion recognition Using Transformers with Random Masking

Emotion Recognition Using Transformers with Random Masking

引用

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

作者： Seongjae Min Junseok Yang Sejoon Lim Kookmin University Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years, deep learning has achieved innovative advancements in various fields, including the analysis of human emotions and behaviors. Initiatives such as the Affective Behavior Analysis in-the-wild (ABAW) competition have been particularly instrumental in driving research in this area by providing diverse and challenging datasets that enable precise evaluation of complex emotional states. This study leverages the vision Transformer (ViT) and Transformer models to focus on the estimation of Valence-Arousal (VA), which signifies the positivity and intensity of emotions, recognition of various facial expressions, and detection of Action Units (AU) representing fundamental muscle movements. This approach transcends traditional Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) based methods, proposing a new Transformer-based framework that maximizes the understanding of temporal and spatial features. The core contributions of this research include the introduction of a learning technique through random frame masking and the application of Focal loss adapted for imbalanced data, enhancing the accuracy and applicability of emotion and behavior analysis in real-world settings. This approach is expected to contribute to the advancement of emotional computing and deep learning methodologies.

关键词： Deep learning Emotion recognition Gold computer vision Computational modeling Estimation Transformer cores

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Performance and Power: Systematic Evaluation of AI Workloads on Accelerators with CARAML

Performance and Power: Systematic Evaluation of AI Workloads...

引用

2024 workshops of the International conference for High Performance Computing, Networking, Storage and Analysis, SC workshops 2024

作者： John, Chelsea Maria Nassyr, Stepan Penke, Carolin Herten, Andreas Forschungszentrum Jülich Jülich Supercomputing Centre Jülich Germany

ISBN: (纸本)9798350355543

The rapid advancement of machine learning (ML) technologies has driven the development of specialized hardware accelerators designed to facilitate more efficient model training. This paper introduces the CARAML benchmark suite, which is employed to assess performance and energy consumption during the training of transformer-based large language models and computer vision models on a range of hardware accelerators, including systems from NVIDIA, AMD, and Graphcore. CARAML provides a compact, automated, extensible, and reproducible framework for assessing the performance and energy of ML workloads across various novel hardware architectures. The design and implementation of CARAML, along with a custom power measurement tool called jpwr, are discussed in detail. © 2024 ieee.

关键词： Accelerators AI Benchmark computer vision Energy GPU IPU Machine Learning NLP Performance Measurement

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