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Unknown Sample Discovery for Source Free Open Set Domain Adaptation

Unknown Sample Discovery for Source Free Open Set Domain Ada...

引用

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

作者： Chowdhury Sadman Jahan Andreas Savakis Center for Imaging Science Rochester Institute of Technology Rochester NY USA Department of Computer Engineering Rochester Institute of Technology Rochester NY USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Open Set Domain Adaptation (OSDA) aims to adapt a model trained on a source domain to a target domain that undergoes distribution shift and contains samples from novel classes outside the source domain. Test-time or source-free OSDA (SF-OSDA) techniques eliminate the need to access source domain samples, but current SF-OSDA methods utilize only the known classes in the target domain for adaptation, and require access to the entire target domain even during inference after adaptation, to distinguish between known and unknown samples. In this paper, we introduce Unknown Sample Discovery (USD) as an SF-OSDA method that utilizes a temporally ensembled teacher model to conduct known-unknown target sample separation and adapts the student model to the target domain over all classes using co-training and temporal consistency between the teacher and the student. USD promotes Jensen-Shannon distance (JSD) as an effective measure for known-unknown sample separation. Our teacher-student framework significantly reduces error accumulation resulting from imperfect known-unknown sample separation, while curriculum guidance helps to reliably learn the distinction between target known and target unknown subspaces. USD appends the target model with an unknown class node, thus readily classifying a target sample into any of the known or unknown classes in subsequent post-adaptation inference stages. Empirical results show that USD is superior to existing SF-OSDA methods and is competitive with current OSDA models that require both source and target domains during adaptation.

关键词： Adaptation models computer vision conferences Computational modeling Data models pattern recognition Reliability

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A web-based intelligence platform for diagnosis of malaria in thick blood smear images: a case for a developing country

A web-based intelligence platform for diagnosis of malaria i...

引用

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

作者： Rose Nakasi Jeremy Francis Tusubira Aminah Zawedde Ali Mansourian Ernest Mwebaze Makerere University Kampala Uganda Lund University Lund Sweden

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Malaria is a public health problem which affects developing countries world-wide. Inadequate skilled lab technicians in remote areas of developing countries result in untimely diagnosis of malaria parasites making it hard for effective control of the disease in highly endemic areas. The development of remote systems that can provide fast, accurate and timely diagnosis is thus a necessary innovation. With availability of internet, mobile phones and computers, rapid dissemination and timely reporting of medical image analytics is possible. This study aimed at developing and implementing an automated web-based Malaria diagnostic system for thick blood smear images under light microscopy to identify parasites. We implement an image processing algorithm based on a pre-trained model of Faster Convolutional Neural Network (Faster R-CNN) and then integrate it with web-based technology to allow easy and convenient online identification of parasites by medical practitioners. Experiments carried out on the online system with test images showed that the system could identify pathogens with a mean average precision of 0.9306. The system holds the potential to improve the efficiency and accuracy in malaria diagnosis, especially in remote areas of developing countries that lack adequate skilled labor.

关键词： Diseases Blood Microscopy Load modeling Computational modeling Object detection computer vision

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Trust Your IMU: Consequences of Ignoring the IMU Drift

Trust Your IMU: Consequences of Ignoring the IMU Drift

引用

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

作者： Marcus Valtonen Ö rnhag Patrik Persson Må rten Wadenbä ck Kalle Å strö m Anders Heyden Centre for Mathematical Sciences Lund University Department of Electrical Engineering Link&#x00F6 ping University

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

In this paper, we argue that modern pre-integration methods for inertial measurement units (IMUs) are accurate enough to ignore the drift for short time intervals. This allows us to consider a simplified camera model, which in turn admits further intrinsic calibration. We develop the first-ever solver to jointly solve the relative pose problem with unknown and equal focal length and radial distortion profile while utilizing the IMU data. Furthermore, we show significant speed-up compared to state-of-the-art algorithms, with small or negligible loss in accuracy for partially calibrated *** proposed algorithms are tested on both synthetic and real data, where the latter is focused on navigation using unmanned aerial vehicles (UAVs). We evaluate the proposed solvers on different commercially available low-cost UAVs, and demonstrate that the novel assumption on IMU drift is feasible in real-life applications. The extended intrinsic auto-calibration enables us to use distorted input images, making tedious calibration processes obsolete, compared to current state-of-the-art methods. Code available at: https://***/marcusvaltonen/DronePoseLib. 1

关键词： computer vision Measurement units Codes conferences Inertial navigation Distortion Real-time systems

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A Novel vision based Finger-writing Character recognition System

A Novel Vision based Finger-writing Character Recognition Sy...

引用

International conference on pattern recognition

作者： Lianwen Jin Duanduan Yang Li-Xin Zhen Jian-Cheng Huang School of Electronic and Information Engineering South China University of Technology Guangzhou China Motorola China Research Center Shanghai China

ISBN: (纸本)0769525210

A new vision based finger writing character recognition system (FWCRS) is proposed in this paper. The FWCRS allows people to write characters virtually just using his finger-tip (we call this "finger-writing"). The trajectories of the finger-tip are tracked and reconstructed as a kind of inkless character pattern and finally recognized by a classifier. In this paper, a simple but effective background model is built for the FWCRS to segment human finger from cluttered background. A robust fingertip detection algorithm based on feature matching is presented. The finger-writing character is finally recognized by a DTW classifier. Experiments show that the FWCRS can recognize finger-writing uppercase & lowercase English characters with the accuracy of 95.6%, 98.5% respectively

关键词： Character recognition Fingers pattern recognition Human computer interaction computer vision Digital cameras Trajectory Equations Writing Robustness

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An Adaptive Appearance Model Approach for Model-based Articulated Object Tracking

An Adaptive Appearance Model Approach for Model-based Articu...

引用

conference on computer vision and pattern recognition (CVPR)

作者： A.O. Balan M.J. Black Department of Computer Science Brown University Providence RI USA

The detection and tracking of three-dimensional human body models has progressed rapidly but successful approaches typically rely on accurate foreground silhouettes obtained using background segmentation. There are many practical applications where such information is imprecise. Here we develop a new image likelihood function based on the visual appearance of the subject being tracked. We propose a robust, adaptive, appearance model based on the Wandering-Stable-Lost framework extended to the case of articulated body parts. The method models appearance using a mixture model that includes an adaptive template, frame-to-frame matching and an outlier process. We employ an annealed particle filtering algorithm for inference and take advantage of the 3D body model to predict selfocclusion and improve pose estimation accuracy. Quantitative tracking results are presented for a walking sequence with a 180 degree turn, captured with four synchronized and calibrated cameras and containing significant appearance changes and self-occlusion in each view.

关键词： Biological system modeling Humans Cameras Robustness Annealing Filtering algorithms Predictive models Legged locomotion computer science Object detection

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PointPrompt: A Multi-modal Prompting Dataset for Segment Anything Model

PointPrompt: A Multi-modal Prompting Dataset for Segment Any...

引用

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

作者： Jorge Quesada Mohammad Alotaibi Mohit Prabhushankar Ghassan AlRegib OLIVES Lab Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The capabilities of foundation models, most recently the Segment Anything Model, have gathered a large degree of attention for providing a versatile framework for tackling a wide array of image segmentation tasks. However, the interplay between human prompting strategies and the segmentation performance of these models remains understudied, as does the role played by the domain knowledge that humans (by previous exposure) and models (by pretraining) bring to the prompting process. To bridge this gap, we present the PointPrompt dataset compiled across multiple image modalities as well as multiple prompting annotators per modality. We collected a total of 16 image datasets from the natural, underwater, medical and seismic domain in order to create a comprehensive resource to facilitate the study of prompting behavior and agreement across modalities. Overall, our prompting dataset contains 158880 inclusion points and 52594 exclusion points over a total of 6000 images. Our analysis highlights the following: (i) viability of prompts across heterogeneous data, (ii) that point prompts are a valuable resource in the effort for enhancing the robustness and generalizability of segmentation models across diverse domains, (iii) prompts facilitate an understanding of the dynamics between annotation strategies and neural network outcomes. Information on downloading the dataset, images, and prompting tool is provided on our project website https://***/pointprompt/.

关键词： Bridges Image segmentation computer vision Computational modeling conferences Neural networks Dynamic scheduling

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Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression

Reactive Model Correction: Mitigating Harm to Task-Relevant ...

引用

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

作者： Dilyara Bareeva Maximilian Dreyer Frederik Pahde Wojciech Samek Sebastian Lapuschkin Fraunhofer Heinrich Hertz Institute Technical University of Berlin BIFOLD – Berlin Institute for the Foundations of Learning and Data

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep Neural Networks are prone to learning and relying on spurious correlations in the training data, which, for high-risk applications, can have fatal consequences. Various approaches to suppress model reliance on harmful features have been proposed that can be applied post-hoc without additional training. Whereas those methods can be applied with efficiency, they also tend to harm model performance by globally shifting the distribution of latent features. To mitigate unintended overcorrection of model behavior, we propose a reactive approach conditioned on model-derived knowledge and eXplainable Artificial Intelligence (XAI) insights. While the reactive approach can be applied to many post-hoc methods, we demonstrate the incorporation of reactivity in particular for P-ClArC (Projective Class Artifact Compensation), introducing a new method called R-ClArC (Reactive Class Artifact Compensation). Through rigorous experiments in controlled settings (FunnyBirds) and with a real-world dataset (ISIC2019), we show that introducing reactivity can minimize the detrimental effect of the applied correction while simultaneously ensuring low reliance on spurious features.

关键词： Training computer vision Correlation Explainable AI conferences Training data Artificial neural networks

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Making a Long Video Short: Dynamic Video Synopsis

Making a Long Video Short: Dynamic Video Synopsis

引用

conference on computer vision and pattern recognition (CVPR)

作者： A. Rav-Acha Y. Pritch S. Peleg Computer Science and Engineering Hebrew University of Jerusalem Jerusalem Israel

The power of video over still images is the ability to represent dynamic activities. But video browsing and retrieval are inconvenient due to inherent spatio-temporal redundancies, where some time intervals may have no activity, or have activities that occur in a small image region. Video synopsis aims to provide a compact video representation, while preserving the essential activities of the original video. We present dynamic video synopsis, where most of the activity in the video is condensed by simultaneously showing several actions, even when they originally occurred at different times. For example, we can create a "stroboscopic movie", where multiple dynamic instances of a moving object are played simultaneously. This is an extension of the still stroboscopic picture. Previous approaches for video abstraction addressed mostly the temporal redundancy by selecting representative key-frames or time intervals. In dynamic video synopsis the activity is shifted into a significantly shorter period, in which the activity is much denser. Video examples can be found online in http://***/synopsis

关键词： Birds Videoconference Object detection Legged locomotion Displays Layout Video sequences computer science Power engineering and energy Image retrieval

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DeDoDe v2: Analyzing and Improving the DeDoDe Keypoint Detector

DeDoDe v2: Analyzing and Improving the DeDoDe Keypoint Detec...

引用

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

作者： Johan Edstedt Georg Bökman Zhenjun Zhao Linköping University Chalmers University of Technology The Chinese University of Hong Kong Texas A&M University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we analyze and improve into the recently proposed DeDoDe keypoint detector. We focus our analysis on some key issues. First, we find that DeDoDe keypoints tend to cluster together, which we fix by performing nonmax suppression on the target distribution of the detector during training. Second, we address issues related to data augmentation. In particular, the DeDoDe detector is sensitive to large rotations. We fix this by including 90-degree rotations as well as horizontal flips. Finally, the decoupled nature of the DeDoDe detector makes evaluation of downstream usefulness problematic. We fix this by matching the keypoints with a pretrained dense matcher (RoMa) and evaluating two-view pose estimates. We find that the original long training is detrimental to performance, and therefore propose a much shorter training schedule. We integrate all these improvements into our proposed detector DeDoDe v2 and evaluate it with the original DeDoDe descriptor on the MegaDepth-1500 and IMC2022 benchmarks. Our proposed detector significantly increases pose estimation results, notably from 75.9 to 78.3 mAA on the IMC2022 challenge. Code and weights are available at ***/Parskatt/DeDoDe.

关键词： Training Schedules computer vision Codes conferences Pose estimation Detectors

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iEdit: Localised Text-guided Image Editing with Weak Supervision

iEdit: Localised Text-guided Image Editing with Weak Supervi...

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

作者： Rumeysa Bodur Erhan Gundogdu Binod Bhattarai Tae-Kyun Kim Michael Donoser Loris Bazzani Imperial College London UK Amazon University of Aberdeen UK KAIST South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Diffusion models (DMs) can generate realistic images with text guidance using large-scale datasets. However, they demonstrate limited controllability on the generated images. We introduce iEdit, a novel method for text-guided image editing conditioned on a source image and textual prompt. As a fully-annotated dataset with target images does not exist, previous approaches perform subject-specific fine-tuning at test time or adopt contrastive learning without a target image, leading to issues on preserving source image fidelity. We propose to automatically construct a dataset derived from LAION-5B, containing pseudo-target images and descriptive edit prompts. The dataset allows us to incorporate a weakly-supervised loss function, generating the pseudo-target image from the source image’s latent noise conditioned on the edit prompt. To encourage localised editing we propose a loss function that uses segmentation masks to guide the editing during training and optionally at inference. Trained with limited GPU resources on the constructed dataset, our model outperforms counterparts in image fidelity, CLIP alignment score, and qualitatively for both generated and real images.

关键词： Training Image segmentation computer vision conferences Noise Graphics processing units Contrastive learning

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