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Finding Relevant Flood Images on Twitter Using Content-Based Filters 25th

Finding Relevant Flood Images on Twitter Using Content-Based...

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25th International conference on pattern recognition workshops, ICPR 2020

作者： Barz, Björn Schröter, Kai Kra, Ann-Christin Denzler, Joachim Computer Vision Group Friedrich Schiller University Jena Jena Germany Section of Hydrology Deutsches GeoForschungsZentrum Potsdam Germany

ISBN: (纸本)9783030687793

The analysis of natural disasters such as floods in a timely manner often suffers from limited data due to coarsely distributed sensors or sensor failures. At the same time, a plethora of information is buried in an abundance of images of the event posted on social media platforms such as Twitter. These images could be used to document and rapidly assess the situation and derive proxy-data not available from sensors, e.g., the degree of water pollution. However, not all images posted online are suitable or informative enough for this purpose. Therefore, we propose an automatic filtering approach using machine learning techniques for finding Twitter images that are relevant for one of the following information objectives: assessing the flooded area, the inundation depth, and the degree of water pollution. Instead of relying on textual information present in the tweet, the filter analyzes the image contents directly. We evaluate the performance of two different approaches and various features on a case-study of two major flooding events. Our image-based filter is able to enhance the quality of the results substantially compared with a keyword-based filter, improving the mean average precision from 23% to 53% on average. © 2021, Springer Nature Switzerland AG.

关键词： Floods

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Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook

Scaling up biologically-inspired computer vision: A case stu...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Nicolas Pinto Zak Stone Todd Zickler David Cox McGovern Institute of Brain Research MIT Cambridge MA USA Rowland Institute at Harvard Harvard University Cambridge MA USA School of Engineering and Applied Sciences Harvard University Cambridge MA USA

Biological visual systems are currently unrivaled by artificial systems in their ability to recognize faces and objects in highly variable and cluttered real-world environments. Biologically-inspired computer vision systems seek to capture key aspects of the computational architecture of the brain, and such approaches have proven successful across a range of standard object and face recognition tasks (e.g. [23, 8, 9, 18]). Here, we explore the effectiveness of these algorithms on a large-scale unconstrained real-world face recognition problem based on images taken from the Face-book social networking website. In particular, we use a family of biologically-inspired models derived from a high-throughput feature search paradigm [19, 15] to tackle a face identification task with up to one hundred individuals (a number that approaches the reasonable size of real-world social networks). We show that these models yield high levels of face-identification performance even when large numbers of individuals are considered; this performance increases steadily as more examples are used, and the models outperform a state-of-the-art commercial face recognition system. Finally, we discuss current limitations and future opportunities associated with datasets such as these, and we argue that careful creation of large sets is an important future direction.

关键词： Face Facebook Face recognition Biological system modeling Brain models

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Deformation Modeling for Robust 3D Face Matching

Deformation Modeling for Robust 3D Face Matching

引用

conference on computer vision and pattern recognition (CVPR)

作者： Xiaoguang Lu A.K. Jain Department of Computer Science & Engineering Michigan State University East Lansing MI USA

Human face recognition based on 3D surface matching is promising for overcoming the limitations of current 2D image-based face recognition systems. The 3D shape is invariant to the pose and lighting changes, but not invariant to the non-rigid facial movement, such as expressions. Collecting and storing multiple templates for each subject in a large database (associated with various expressions) is not practical. We present a facial surface modeling and matching scheme to match 2.5D test scans in the presence of both non-rigid deformations and large pose changes (multiview) to a neutral expression 3D face model. A geodesic-based resampling approach is applied to extract landmarks for modeling facial surface deformations. We are able to synthesize the deformation learned from a small group of subjects (control group) onto a 3D neutral model (not in the control group), resulting in a deformed template. A personspecific (3D) deformable model is built for each subject in the gallery w.r.t. the control group by combining the templates with synthesized deformations. By fitting this generative deformable model to a test scan, the proposed approach is able to handle expressions and large pose changes simultaneously. Experimental results demonstrate that the proposed matching scheme based on deformation modeling improves the matching accuracy.

关键词： Deformable models Robustness Face recognition Humans Shape Testing Mouth computer science Image databases Control system synthesis

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Dynamics Based Robust Motion Segmentation

Dynamics Based Robust Motion Segmentation

引用

conference on computer vision and pattern recognition (CVPR)

作者： R. Lublinerman M. Sznaier O. Camps Department of Computer Science and Engineering Pennsylvania State University University Park PA USA Department of Electrical Engineering Pennsylvania State University University Park PA USA

In this paper we consider the problem of segmenting multiple rigid motions using multi-frame point correspondence data. The main idea of the method is to group points according to the complexity of the model required to explain their relative motion. Intuitively, this formalizes the idea that points on the same rigid share more modes of motion (for instance a common translation or rotation) than points on different objects, leading to less complex models. By exploiting results from systems theory, the problem of estimating the complexity of the underlying model is reduced to simply computing the rank of a matrix constructed from the correspondence data. This leads to a simple segmentation algorithm, computationally no more expensive than a sequence of SVDs. Since the proposed method exploits both spatial and temporal constraints, is less sensitive to the effect of noise or outliers than approaches that rely solely on factorizations of the measurements matrix. In addition, the method can also naturally handle "degenerate cases", e.g. cases where the objects partially share motion modes. These results are illustrated using several examples involving both degenerate and non-degenerate cases.

关键词： Robustness Motion segmentation computer vision Propellers Airplanes computer science Data engineering Noise measurement Geometrical optics Optical sensors

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Multi-Target Tracking - Linking Identities using Bayesian Network Inference

Multi-Target Tracking - Linking Identities using Bayesian Ne...

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conference on computer vision and pattern recognition (CVPR)

作者： P. Nillius J. Sullivan S. Carlsson Institute of Computer Science FORTH Heraklion Greece School of Computer Science & Communication Royal Institute of Technology Stockholm Sweden

Multi-target tracking requires locating the targets and labeling their identities. The latter is a challenge when many targets, with indistinct appearances, frequently occlude one another, as in football and surveillance tracking. We present an approach to solving this labeling problem. When isolated, a target can be tracked and its identity maintained. While, if targets interact this is not always the case. This paper assumes a track graph exists, denoting when targets are isolated and describing how they interact. Measures of similarity between isolated tracks are defined. The goal is to associate the identities of the isolated tracks, by exploiting the graph constraints and similarity measures. We formulate this as a Bayesian network inference problem, allowing us to use standard message propagation to find the most probable set of paths in an efficient way. The high complexity inevitable in large problems is gracefully reduced by removing dependency links between tracks. We apply the method to a 10 min sequence of an international football game and compare results to ground truth.

关键词： Joining processes Bayesian methods Target tracking computer science Labeling Robustness Filtering Surveillance Information resources Information analysis

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Vim4Path: Self-Supervised vision Mamba for Histopathology Images

Vim4Path: Self-Supervised Vision Mamba for Histopathology Im...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ali Nasiri-Sarvi Vincent Quoc-Huy Trinh Hassan Rivaz Mahdi S. Hosseini Department of Computer Science and Software Engineering (CSSE) Concordia University Canada Institute for Research in Immunology and Cancer of the University of Montreal Canada Department of Electrical and Computer Engineering (ECE) Concordia University Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Representation learning from Gigapixel Whole Slide Images (WSI) poses a significant challenge in computational pathology due to the complicated nature of tissue structures and the scarcity of labeled data. Multi-instance learning methods have addressed this challenge, leveraging image patches to classify slides utilizing pretrained models using Self-Supervised Learning (SSL) approaches. The performance of both SSL and MIL methods relies on the architecture of the feature encoder. This paper proposes leveraging the vision Mamba (Vim) architecture, inspired by state space models, within the DINO framework for representation learning in computational pathology. We evaluate the performance of Vim against vision Transformers (ViT) on the Camelyon16 dataset for both patch-level and slide-level classification. Our findings highlight Vim’s enhanced performance compared to ViT, particularly at smaller scales, where Vim achieves an 8.21 increase in ROC AUC for models of similar size. An explainability analysis further highlights Vim’s capabilities, which reveals that Vim uniquely emulates the pathologist workflow—unlike ViT. This alignment with human expert analysis highlights Vim’s potential in practical diagnostic settings and contributes significantly to developing effective representation-learning algorithms in computational pathology. We release the codes and pretrained weights at https://***/AtlasAnalyticsLab/Vim4Path.

关键词： Representation learning Training computer vision Adaptation models Computational modeling Self-supervised learning computer architecture

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Domain Targeted Synthetic Plant Style Transfer using Stable Diffusion, LoRA and ControlNet

Domain Targeted Synthetic Plant Style Transfer using Stable ...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Zane K. J. Hartley Rob J. Lind Michael P. Pound Andrew P. French University of Nottingham Syngenta Jealott’s Hill International Research Centre

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Synthetic images can help alleviate much of the cost in the creation of training data for plant phenotyping-focused AI development. Synthetic-to-real style transfer is of particular interest to users of artificial data because of the domain shift problem created by training neural networks on images generated in a digital environment. In this paper we present a pipeline for synthetic plant creation and image-to-image style transfer, with a particular interest in synthetic to real domain adaptation targeting specific real datasets. Utilizing new advances in generative AI, we employ a combination of Stable diffusion, Low Ranked Adapters (LoRA) and ControlNets to produce an advanced system of style transfer. We focus our work on the core task of leaf instance segmentation, exploring both synthetic to real style transfer as well as inter-species style transfer and find that our pipeline makes numerous improvements over CycleGAN for style transfer, and the images we produce are comparable to real images when used as training data.

关键词： Training Instance segmentation computer vision Costs Generative AI conferences Pipelines

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GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing

GPT as Psychologist? Preliminary Evaluations for GPT-4V on V...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Hao Lu Xuesong Niu Jiyao Wang Yin Wang Qingyong Hu Jiaqi Tang Yuting Zhang Kaishen Yuan Bin Huang Zitong Yu Dengbo He Shuiguang Deng Hao Chen Yingcong Chen Shiguang Shan The Hong Kong University of Science & Technology (Guangzhou) The Hong Kong University of Science & Technology Beijing Institute for General Artificial Intelligence Zhejiang University Great Bay University Hangzhou Research Institute Beihang University Chinese Academy of Sciences Institute of Computing Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multimodal large language models (MLLMs) are designed to process and integrate information from multiple sources, such as text, speech, images, and videos. Despite its success in language understanding, it is critical to evaluate the performance of downstream tasks for better human-centric applications. This paper assesses the application of MLLMs with 5 crucial abilities for affective computing, spanning from visual affective tasks and reasoning tasks. The results show that GPT-4V has high accuracy in facial action unit recognition and micro-expression detection while its general facial expression recognition performance is not accurate. We also highlight the challenges of achieving fine-grained micro-expression recognition and the potential for further study and demonstrate the versatility and potential of GPT-4V for handling advanced tasks in emotion recognition and related fields by integrating with task-related agents for more complex tasks, such as heart rate estimation through signal processing. In conclusion, this paper provides valuable insights into the potential applications and challenges of MLLMs in human-centric computing. Our interesting examples are at https://***/Envision-Research/GPT4Affectivity.

关键词： Heart rate Emotion recognition Visualization computer vision Affective computing Accuracy Face recognition

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Multi-modal Aerial View Image Challenge: Sensor Domain Translation

Multi-modal Aerial View Image Challenge: Sensor Domain Trans...

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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 ESPOL Polytechnic University Ecuador Computer Vision Center Spain Air Force Research Laboratory Rome NY University of Utah Salt Lake UT

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper describes the design, outcomes, and top methods of the 2nd annual Multi-modal Aerial View Image Challenge (MAVIC) aimed at cross modality aerial image translation. The primary objective of this competition is to stimulate research efforts towards the development of models capable of translating co-aligned images between multiple modalities. Specifically, the challenge centers on translation between synthetic aperture radar (SAR), electro-optical (EO), camera (RGB), and infrared (IR) sensor modalities, a budding area of research that has begun to garner attention. While last year’s inaugural challenge demonstrated the feasibility of SAR→EO translation, this year’s challenge made significant improvements in dataset coverage, sensor variation, experimental design, and methods covering the tasks of SAR→EO, SAR→RGB, SAR→IR, RGB→IR introducing a new dataset called translation. By Multi-modal Aerial Gathered Image Composites (MAGIC); multimodal image translation is available for different comparisons. With a more rigorous set of translation performance metrics, winners were determined from aggregation of L1-norm, LPIPS (Learned Perceptual Image Patch Similarity, and FID (Frechet Inception Distance) scores. The wining methods included the pix2pixHD and LPIPS metrics as loss functions with an aggregated score 5% better separated by the SAR→EO and RGB→IR translation scores.

关键词： Measurement computer vision Design methodology conferences Cameras pattern recognition Synthetic aperture radar

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Cross-view Aggregation Network For Stereo Image Super-Resolution

Cross-view Aggregation Network For Stereo Image Super-Resolu...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Zhitao Chen Tao Lu Kanghui Zhao Bolin Zhu Zhen Li Jiaming Wang Yanduo Zhang Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Although stereo image super-resolution has been extensively studied, many existing works only rely on attention in a single epipolar direction to reconstruct stereo images. In the case of asymmetric parallax images, these methods often struggle to capture reliable stereo correspondence, resulting in reconstructed images suffering from blurring and artifacts. In this paper, we propose a novel method called Cross-View Aggregation Network for Stereo Image Super-Resolution (CANSSR) and explore the relationship between multi-directional epipolar lines to construct reliable stereo correspondence. Specifically, we propose a multidirectional cross-view aggregation module (MCAM) that effectively captures multi-directional stereo correspondence and obtains cross-view complementary information. Furthermore, we design a channel-spatial aggregation module (CSAM) that aggregates multi-order global-local information in intra-view to reconstruct clearer texture features. In addition, we equip a large kernel convolution in the Feedforward Network to acquire richer detailed texture information. The extensive experiments conclusively demonstrate that CANSSR outperforms the state-of-the-art method both qualitatively and quantitatively in terms of stereo image super-resolution on the Flickr 1024 and Middlebury datasets.

关键词： Image databases Convolution computer network reliability Superresolution pattern recognition Reliability Web sites

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