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On Moving Object Segmentation from Monocular Video with Transformers

On Moving Object Segmentation from Monocular Video with Tran...

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International conference on computer vision workshops (ICCV workshops)

作者： Christian Homeyer Christoph Schnörr Robert Bosch GmbH Corporate Research Computer Vision Lab Hildesheim Germany Image and Pattern Analysis Group Heidelberg University Germany Image and Pattern Analysis Group Heidelberg University Germany

Moving object detection and segmentation from a single moving camera is a challenging task, requiring an understanding of recognition, motion and 3D geometry. Combining both recognition and reconstruction boils down to a fusion problem, where appearance and motion features need to be combined for classification and *** this paper, we present a novel fusion architecture for monocular motion segmentation - M 3 Former, which leverages the strong performance of transformers for segmentation and multi-modal fusion. As reconstructing motion from monocular video is ill-posed, we systematically analyze different 2D and 3D motion representations for this problem and their importance for segmentation performance. Finally, we analyze the effect of training data and show that diverse datasets are required to achieve SotA performance on Kitti and Davis. Code will be released upon publication.

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Supervised Contrastive Learning for Generalizable and Explainable DeepFakes Detection

Supervised Contrastive Learning for Generalizable and Explai...

引用

22nd ieee/CVF Winter conference on Applications of computer vision (WACV)

作者： Xu, Ying Raja, Kiran Pedersen, Marius Norwegian Univ Sci & Technol NTNU Trondheim Norway

ISBN: (纸本)9781665458245

DeepFakes detection approaches have to be agnostic across generation type, quality, and appearance to provide a generalizable DeepFakes detector. Limited generalizability will hinder wide-scale deployment of detectors if they cannot handle unseen attacks in an open set scenario. We propose a generalizable detection model that can detect novel and unknown/unseen DeepFakes using a supervised contrastive (SupCon) loss. As DeepFakes can resemble the original image/video to a greater extent in terms of appearance and it becomes challenging to secern them, we propose to exploit the contrasts in the representation space to learn a generalizable detector. We further investigate the features learnt from our proposed approach for explainability. The analysis for explainability of the models advocates the need for fusion and motivated by this, we fuse the scores from the proposed SupCon model and the Xception network to exploit the variability from different architectures. The proposed model consistently performs better compared to the single model on both known data and unknown attacks consistently in a seen data setting and an unseen data setting, with generalizability and explainability as a basis. We obtain the highest accuracy of 78.74% using proposed SupCon model and an accuracy of 83.99% with proposed fusion in a true open-set evaluation scenario where the test class is unknown at the training phase. The paper also aligns with reproducible research by making the code available (1).

关键词： Training Analytical models Visualization Fuses Detectors computer architecture Media

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Diversified and Multi-Class Controllable Industrial Defect Synthesis for Data Augmentation and Transfer

Diversified and Multi-Class Controllable Industrial Defect S...

引用

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

作者： Jing Wei Fei Shen Chengkan Lv Zhengtao Zhang Feng Zhang Huabin Yang Institute of Automation Chinese Academy of Sciences The School of Artificial Intelligence University of Chinese Academy of Sciences CASI Vision Technology CO.

Data augmentation is crucial to solve few-sample issues in industrial inspection based on deep learning. However, current industrial data augmentation methods have not yet demonstrated on-par ability in the synthesis of complex defects with pixel-level annotations. This paper proposes a new defect synthesis framework to fill the gap. Firstly, DCDGANc (Diversified and multi-class Controllable Defect Generation Adversarial Networks based on constant source images) is proposed to employ class labels to construct source inputs to control the category and random codes to generate diversified styles of defects. DCDGANc can generate defect content images with pure backgrounds, which avoids the influence of non-defect information and makes it easy to obtain binary masks by segmentation. Secondly, the Poisson blending is improved to avoid content loss when blending generated defect contents to the normal backgrounds. Finally, the complete defect samples and accurate pixel-level annotations are obtained by fine image processing. Experiments are conducted to verify the effectiveness of our work in wood, fabric, metal, and marble. The results show that our methods yield significant improvement in the segmentation performance of industrial products. Moreover, our work enables zero-shot inspection by facilitating defect transfer between datasets with different backgrounds but similar defects, which can greatly reduce the cost of data collection in industrial inspection.

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Tracked-Vehicle Retrieval by Natural Language Descriptions with Multi-Contextual Adaptive Knowledge

Tracked-Vehicle Retrieval by Natural Language Descriptions w...

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

作者： Huy Dinh-Anh Le Quang Qui-Vinh Nguyen Duc Trung Luu Truc Thi-Thanh Chau Nhat Minh Chung Synh Viet-Uyen Ha School of Computer Science and Engineering International University Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam

This paper introduces our solution for Track 2 in AI City Challenge 2023. The task is tracked-vehicle retrieval by natural language descriptions with a real-world dataset of various scenarios and cameras. Our solution mainly focuses on four points: (1) To address the linguistic ambiguity in the language query, we leverage our proposed standardized version for text descriptions for the domain-adaptive training and post-processing stage. (2) Our baseline vehicle retrieval model utilizes CLIP to extract robust visual and textual feature representations to learn the unified cross-modal representations between textual and visual features. (3) Our proposed semi-supervised domain adaptive (SSDA) training method is leveraged to address the domain gap between the train and test set. (4) Finally, we propose a multi-contextual post-processing technique that prunes out the wrong results based on multi-contextual attributes information that effectively boosts the final retrieval results. Our proposed framework has yielded a competitive performance of 82.63% MRR accuracy on the test set, achieving 1st place in the competition. Codes will be available at https://***/zef1611/AIC23_NLRetrieval_HCMIU_CVIP

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Relational Edge-Node Graph Attention Network for Classification of Micro-Expressions

Relational Edge-Node Graph Attention Network for Classificat...

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

作者： Ankith Jain Rakesh Kumar Bir Bhanu Department of Electrical and Computer Engineering University of California Riverside

Facial micro-expressions (MEs) refer to subtle, transient, and involuntary muscle movements expressing a person’s true feelings. This paper presents a novel two-stream relational edge-node graph attention network-based approach to classify MEs in a video by selecting the high-intensity frames and edge-node features that can provide valuable information about the relationship between nodes and structural information in a graph structure. The paper examines the impact of different edge-node features and their relationships on the graphs. The first step involves extracting high-intensity-emotion frames from the video using optical flow. Second, node feature embeddings are calculated using the node location coordinate features and the patch size information of the optical flow across each node location. Additionally, we obtain the global and local structural similarity score using the jaccard’s similarity score and radial basis function as the edge features. Third, a self-attention graph pooling layer helps to remove the nodes with lower attention scores based on the top-k selection. As the final step, the network employs a two-stream edge-node graph attention network that focuses on finding correlations among the edge and node features, such as landmark coordinates, optical flow, and global and local edge features. A three-frame graph structure is designed to obtain spatiotemporal information. For 3 and 5 expression classes, the results are compared for SMIC and CASME II databases.

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Bush Detection for vision-based UGV Guidance in Blueberry Orchards: Data Set and Methods

Bush Detection for Vision-based UGV Guidance in Blueberry Or...

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

作者： Vladan Filipović Dimitrije Stefanović Nina Pajević Željana Grbović Nemanja Djuric Marko Panić BioSense Institute Novi Sad Serbia

Object detection has reached strong performance in the last decade, having seen its usage spreading to various application areas, such as medicine, transportation, sports, and others. However, one of the more underutilized areas where advanced detection methods have yet to fully fulfill their promise is in the area of agriculture, where a strong potential exists for applying learned models to achieve practical, real-world impact affecting a large number of people. In this work, we focus on this application area and consider the problem of orchard guidance for ground robots, focusing on obstacle and plant detection from RGB camera images. First, we present an overview of public data sets used to train models to detect relevant objects from camera images and other sensor inputs. Then we introduce a novel data set collected in blueberry orchards that contains camera images in various conditions and provides blueberry bushes as targets for detection. The introduced data set provides the research community with a novel task of blueberry bush detection, which was not commonly considered thus far due to the lack of relevant data sets. We describe a detailed analysis of the data set, and finally provide an experimental study with several state-of-the-art deep object detection models, that set a baseline for the performance on this novel data set. The data set is made available online, enriching the variability of the existing tasks in the field and supporting further development of smart agriculture applications.

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Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery

Region-based Appearance and Flow Characteristics for Anomaly...

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

作者： Yona Falinie A. Gaus Neelanjan Bhowmik Brian K. S. Isaac-Medina Hubert P. H. Shum Amir Atapour-Abarghouei Toby P. Breckon Department of Computer Science Durham University Durham UK Department of Engineering Durham University Durham UK

Anomaly detection is a classical problem within automated visual surveillance, namely the determination of the normal from the abnormal when operational data availability is highly biased towards one class (normal) due to both insufficient sample size, and inadequate distribution coverage for the other class (abnormal). In this work, we propose the dual use of both visual appearance and localized motion characteristics, derived from optic flow, applied on a per-region basis to facilitate object-wise anomaly detection within this context. Leveraging established object localization techniques from a region proposal network, optic flow is extracted from each object region and combined with appearance in the far infrared (thermal) band to give a 3-channel spatiotemporal tensor representation for each object (1 × thermal - spatial appearance; 2 × optic flow magnitude as x and y components - temporal motion). This formulation is used as the basis for training contemporary semi-supervised anomaly detection approaches in a region-based manner such that anomalous objects can be detected as a combination of appearance and/or motion within the scene. Evaluation is performed using the Long-Term infrared (thermal) Imaging (LTD) benchmark dataset against which successful detection of both anomalous object appearance and motion characteristics are demonstrated using a range of semi-supervised anomaly detection approaches.

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FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks

FewSOME: One-Class Few Shot Anomaly Detection with Siamese N...

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

作者： Niamh Belton Misgina Tsighe Hagos Aonghus Lawlor Kathleen M. Curran Science Foundation Ireland Centre for Research Training in Machine Learning School of Medicine University College Dublin School of Computer Science University College Dublin Insight Centre for Data Analytics University College Dublin Dublin Ireland

Recent Anomaly Detection techniques have progressed the field considerably but at the cost of increasingly complex training pipelines. Such techniques require large amounts of training data, resulting in computationally expensive algorithms that are unsuitable for settings where only a small amount of normal samples are available for training. We propose ‘Few Shot anOMaly dEtection’ (FewSOME), a deep One-Class Anomaly Detection algorithm with the ability to accurately detect anomalies having trained on ‘few’ examples of the normal class and no examples of the anomalous class. We describe FewSOME to be of low complexity given its low data requirement and short training time. FewSOME is aided by pretrained weights with an architecture based on Siamese Networks. By means of an ablation study, we demonstrate how our proposed loss, ‘Stop Loss’, improves the robustness of FewSOME. Our experiments demonstrate that FewSOME performs at stateof-the-art level on benchmark datasets MNIST, CIFAR-10, F-MNIST and MVTec AD while training on only 30 normal samples, a minute fraction of the data that existing methods are trained on. Moreover, our experiments show FewSOME to be robust to contaminated datasets. We also report F1 score and balanced accuracy in addition to AUC as a benchmark for future techniques to be compared against. Code available; https://***/niamhbelton/FewSOME.

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Beyond the Screen: Evaluating Deepfake Detectors under Moiré pattern Effects

Beyond the Screen: Evaluating Deepfake Detectors under Moir...

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

作者： Razaib Tariq Minji Heo Simon S. Woo Shahroz Tariq Sungkyunkwan University South Korea CSIRO’s Data61 Australia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The detection of deepfakes is crucial for mitigating the societal impact of falsified video content. Despite the development of various algorithms for this purpose, challenges arise for detectors in real-world scenarios, especially when users capture deepfake content from screens and upload it online or when detectors operate on external devices like smartphones, requiring the capture of potential deepfakes through the camera for evaluation. A significant challenge in these scenarios is the presence of Moiré patterns, which degrade image quality and complicate conventional classification methods, notably deep neural networks (DNNs). However, the impact of Moiré patterns on the effectiveness of deepfake detection systems has not been adequately explored. This study aims to investigate how capturing deepfake videos via digital screen cameras affects the accuracy of detection mechanisms. We introduced the Moiré patterns by capturing the display of a monitor using a smartphone camera and conducted empirical evaluations using four widely recognized datasets: CelebDF, DFD, DFDC, and FF++. We compare the performance of twelve SOTA detectors on deepfake videos captured under the influence of Moiré patterns. Our findings reveal a performance decrease of up to 33.1 and 31.3 percentage points for image- and video-based detectors. Therefore, highlighting the challenges posed by Moiré patterns and other naturally induced artifacts is critical for improving the effectiveness of real-world deepfake detection efforts. To facilitate further research, we will release the Moiré pattern impact version of CelebDF, DFD, DFDC, and FF++ datasets with this paper. Our code is available here: https://***/Razaib-Tariq/deepmoire

关键词： Image quality Deepfakes computer vision conferences Refining Detectors Cameras

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CNT-NeRF: Carbon Nanotube Forest Depth Layer Decomposition in SEM Imagery using Generative Adversarial Networks

CNT-NeRF: Carbon Nanotube Forest Depth Layer Decomposition i...

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

作者： Nguyen P. Nguyen Ramakrishna Surya Prasad Calyam Kannappan Palaniappan Matthew Maschmann Filiz Bunyak Department of Electrical Engineering and Computer Science University of Missouri Columbia MO USA Department of Mechanical and Aerospace Engineering University of Missouri Columbia MO USA

Carbon nanotube (CNT) forests are imaged using scanning electron microscopes (SEMs) that project their multilayered 3D structure into a single 2D image. Image analytics, particularly instance segmentation is needed to quantify structural characteristics and to predict correlations between structural morphology and physical properties. The inherent complexity of individual CNT structures is further increased in CNT forests due to density of CNTs, interactions between CNTs, occlusions, and lack of 3D information to resolve correspondences when multiple CNTs from different depths appear to cross in 2D. In this paper, we propose CNT-NeRF, a generative adversarial network (GAN) for simultaneous depth layer decomposition and segmentation of CNT forests in SEM images. The proposed network is trained using a multi-layer, photo-realistic synthetic dataset obtained by transferring the style of real CNT images to physics-based simulation data. Experiments show promising depth layer decomposition and accurate CNT segmentation results not only for the front layer but also for the partially occluded middle and back layers. This achievement is a significant step towards automated, image-based CNT forest structure characterization and physical property prediction.

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