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检索条件"任意字段=IEEE Conference on Applications of Computer Vision"

共 17180 条记录，以下是61-70 订阅

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From Chaos to Calibration: A Geometric Mutual Information Approach to Target-Free Camera LiDAR Extrinsic Calibration

From Chaos to Calibration: A Geometric Mutual Information Ap...

引用

ieee/CVF Winter conference on applications of computer vision (WACV)

作者： Borer, Jack Tschirner, Jeremy Oelsner, Florian Milz, Stefan Spleenlab GmbH Saalburg Ebersdorf Germany

ISBN: (纸本)9798350318920;9798350318937

Sensor fusion is vital for the safe and robust operation of autonomous vehicles. Accurate extrinsic sensor to sensor calibration is necessary to accurately fuse multiple sensor's data in a common spatial reference frame. In this paper, we propose a target free extrinsic calibration algorithm that requires no ground truth training data, artificially constrained motion trajectories, hand engineered features or offline optimization and that is accurate, precise and extremely robust to initialization error. Most current research on online camera-LiDAR extrinsic calibration requires ground truth training data which is impossible to capture at scale. We revisit analytical mutual information based methods first proposed in 2012 and demonstrate that geometric features provide a robust information metric for camera-LiDAR extrinsic calibration. We demonstrate our proposed improvement using the KITTI and KITTI-360 fisheye data set.

关键词： applications applications applications Autonomous Driving Remote Sensing Robotics

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Challenges in Video-Based Infant Action Recognition: A Critical Examination of the State of the Art

Challenges in Video-Based Infant Action Recognition: A Criti...

引用

ieee/CVF Winter conference on applications of computer vision (WACV)

作者： Hatamimajoumerd, Elaheh Kakhaki, Pooria Daneshvar Huang, Xiaofei Luan, Lingfei Amraee, Somaieh Ostadabbas, Sarah Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA Northeastern Univ Roux Inst Portland ME USA Univ Minnesota Minneapolis MN USA

ISBN: (纸本)9798350370287;9798350370713

Automated human action recognition, a burgeoning field within computer vision, boasts diverse applications spanning surveillance, security, human-computer interaction, tele-health, and sports analysis. Precise action recognition in infants serves a multitude of pivotal purposes, encompassing safety monitoring, developmental milestone tracking, early intervention for developmental delays, fostering parent-infant bonds, advancing computer-aided diagnostics, and contributing to the scientific comprehension of child development. This paper delves into the intricacies of infant action recognition, a domain that has remained relatively uncharted despite the accomplishments in adult action recognition. In this study, we introduce a ground-breaking dataset called "InfActPrimitive", encompassing five significant infant milestone action categories, and we incorporate specialized preprocessing for infant data. We conducted an extensive comparative analysis employing cutting-edge skeleton-based action recognition models using this dataset. Our findings reveal that, although the PoseC3D model achieves the highest accuracy at approximately 71%, the remaining models struggle to accurately capture the dynamics of infant actions. This highlights a substantial knowledge gap between infant and adult action recognition domains and the urgent need for data-efficient pipeline models

关键词： computer vision

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NOMAD: A Natural, Occluded, Multi-scale Aerial Dataset, for Emergency Response Scenarios

NOMAD: A Natural, Occluded, Multi-scale Aerial Dataset, for ...

引用

ieee/CVF Winter conference on applications of computer vision (WACV)

作者： Bernal, Arturo Miguel Russell Scheirer, Walter Cleland-Huang, Jane Univ Notre Dame Comp Sci & Engn Dept Notre Dame IN 46556 USA

ISBN: (纸本)9798350318920;9798350318937

With the increasing reliance on small Unmanned Aerial Systems (sUAS) for Emergency Response Scenarios, such as Search and Rescue, the integration of computer vision capabilities has become a key factor in mission success. Nevertheless, computer vision performance for detecting humans severely degrades when shifting from ground to aerial views. Several aerial datasets have been created to mitigate this problem, however, none of them has specifically addressed the issue of occlusion, a critical component in Emergency Response Scenarios. Natural Occluded Multi-scale Aerial Dataset (NOMAD) presents a benchmark for human detection under occluded aerial views, with five different aerial distances and rich imagery variance. NOMAD is composed of 100 different Actors, all performing sequences of walking, laying and hiding. It includes 42,825 frames, extracted from 5.4k resolution videos, and manually annotated with a bounding box and a label describing 10 different visibility levels, categorized according to the percentage of the human body visible inside the bounding box. This allows computer vision models to be evaluated on their detection performance across different ranges of occlusion. NOMAD is designed to improve the effectiveness of aerial search and rescue and to enhance collaboration between sUAS and humans, by providing a new benchmark dataset for human detection under occluded aerial views.

关键词： Algorithms applications applications Datasets and evaluations Robotics Social good

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CPSeg: Finer-grained Image Semantic Segmentation via Chain-of-Thought Language Prompting

CPSeg: Finer-grained Image Semantic Segmentation via Chain-o...

引用

ieee/CVF Winter conference on applications of computer vision (WACV)

作者： Li, Lei Univ Copenhagen Copenhagen Denmark

ISBN: (纸本)9798350318920;9798350318937

Natural scene analysis and remote sensing imagery offer immense potential for advancements in large-scale language-guided context-aware data utilization. This potential is particularly significant for enhancing performance in downstream tasks such as object detection and segmentation with designed language prompting. In light of this, we introduce the CPSeg (Chain-of-Thought Language Prompting for Finer-grained Semantic Segmentation), an innovative framework designed to augment image segmentation performance by integrating a novel "Chain-of-Thought" process that harnesses textual information associated with images. This groundbreaking approach has been applied to a flood disaster scenario. CPSeg encodes prompt texts derived from various sentences to formulate a coherent chain-of-thought. We use a new vision-language dataset, FloodPrompt, which includes images, semantic masks, and corresponding text information. This not only strengthens the semantic understanding of the scenario but also aids in the key task of semantic segmentation through an interplay of pixel and text matching maps. Our qualitative and quantitative analyses validate the effectiveness of CPSeg.

关键词： Algorithms Algorithms applications Image recognition and understanding Remote Sensing vision + language and/or other modalities

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UNSPAT: Uncertainty-Guided SpatioTemporal Transformer for 3D Human Pose and Shape Estimation on Videos

UNSPAT: Uncertainty-Guided SpatioTemporal Transformer for 3D...

引用

ieee/CVF Winter conference on applications of computer vision (WACV)

作者： Lee, Minsoo Lee, Hyunmin Kim, Bumsoo Kim, Seunghwan LG AI Res Seoul South Korea

ISBN: (纸本)9798350318920;9798350318937

We propose an efficient framework for 3D human pose and shape estimation from a video, named Uncertainty-Guided SpatioTemporal Transformer (UNSPAT). Unlike previous video-based methods that consider temporal relationships with global average pooled features, our approach incorporates both spatial and temporal dimensions without compromising spatial information. We address the excessive complexity of spatiotemporal attention through two modules: Spatial Alignment Module (SAM) and Space2Batch. The modules align input features and compute temporal attention at every spatial position in a batch-wise manner. Furthermore, our uncertainty-guided attention re-weighting module improves performance by diminishing the impact of artifacts. We demonstrate the effectiveness of the UNSPAT on widely used benchmark datasets and achieve state-of-the-art performance. Our method is robust to challenging scenes, such as occlusion, and cluttered backgrounds, showing its potential for real-world applications.

关键词： 3D computer vision Algorithms Algorithms Biometrics body pose face gesture

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MIVC: Multiple Instance Visual Component for Visual-Language Models

MIVC: Multiple Instance Visual Component for Visual-Language...

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ieee/CVF Winter conference on applications of computer vision (WACV)

作者： Wu, Wenyi Li, Qi Zhong, Wenliang Huang, Junzhou Amazon 424 5th Ave New York NY 10018 USA Amazon 410 Terry Ave N Seattle WA USA Univ Texas Arlington 500 UTA Blvd Arlington TX USA

ISBN: (纸本)9798350318920;9798350318937

vision-language models have been widely explored across a wide range of tasks and achieve satisfactory performance. However, it's under-explored how to consolidate entity understanding through a varying number of images and to align it with the pre-trained language models for generative tasks. In this paper, we propose MIVC, a general multiple instance visual component to bridge the gap between various image inputs with off-the-shelf vision-language models by aggregating visual representations in a permutation-invariant fashion through a neural network. We show that MIVC could be plugged into the visual-language models to improve the model performance consistently on visual question answering, classification and captioning tasks on a public available e-commerce dataset with multiple images per product. Furthermore, we show that the component provides insight into the contribution of each image to the downstream tasks.

关键词： Algorithms applications Commercial / retail Image recognition and understanding

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Seeing Stars: Learned Star Localization for Narrow-Field Astrometry

Seeing Stars: Learned Star Localization for Narrow-Field Ast...

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ieee/CVF Winter conference on applications of computer vision (WACV)

作者： Felt, Violet Fletcher, Justin US Space Force 550 Lipoa Pkwy Kihei HI 96753 USA

ISBN: (纸本)9798350318920;9798350318937

Star localization in astronomical imagery is a computer vision task that underpins satellite tracking. Astronomical star extraction techniques often struggle to detect stars when applied to satellite tracking imagery due to the narrower fields of view and rate track observational modes of satellite tracking telescopes. We present a large dataset of real narrow-field rate-tracked imagery with ground truth stars, created using a combination of existing star detection techniques, an astrometric engine, and a star catalog. We train three state of the art object detection, instance segmentation, and line segment detection models on this dataset and evaluate them with object-wise, pixel-wise, and astrometric metrics. Our proposed approaches require no metadata;when paired with a lost-in-space astrometric engine, they find astrometric fits based solely on uncorrected image pixels. Experimental results on real data indicate the effectiveness of learned star detection: we report astrometric fit rates over double that of classical star detection algorithms, improved dim star recall, and comparable star localization residuals.

关键词： Algorithms applications Datasets and evaluations Remote Sensing

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TriCoLo: Trimodal Contrastive Loss for Text to Shape Retrieval

TriCoLo: Trimodal Contrastive Loss for Text to Shape Retriev...

引用

ieee/CVF Winter conference on applications of computer vision (WACV)

作者： Ruan, Yue Lee, Han-Hung Zhang, Yiming Zhang, Ke Chang, Angel X. Simon Fraser Univ Burnaby BC Canada Alberta Machine Intelligence Inst Amii Edmonton AB Canada

ISBN: (纸本)9798350318920;9798350318937

Text-to-shape retrieval is an increasingly relevant problem with the growth of 3D shape data. Recent work on contrastive losses for learning joint embeddings over multimodal data [45] has been successful at tasks such as retrieval and classification. Thus far, work on joint representation learning for 3D shapes and text has focused on improving embeddings through modeling of complex attention between representations [53], or multi-task learning [25]. We propose a trimodal learning scheme over text, multi-view images and 3D shape voxels, and show that with large batch contrastive learning we achieve good performance on text-to-shape retrieval without complex attention mechanisms or losses. Our experiments serve as a foundation for follow-up work on building trimodal embeddings for text-image-shape.

关键词： 3D computer vision Algorithms Algorithms vision + language and/or other modalities

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ArcAid: Analysis of Archaeological Artifacts using Drawings

ArcAid: Analysis of Archaeological Artifacts using Drawings

引用

ieee/CVF Winter conference on applications of computer vision (WACV)

作者： Hayon, Offry Munger, Stefan Shimshoni, Ilan Tal, Ayellet Technion Haifa Israel Univ Bern Bern Switzerland Univ Haifa Haifa Israel

ISBN: (纸本)9798350318920;9798350318937

Archaeology is an intriguing domain for computer vision. It suffers not only from shortage in (labeled) data, but also from highly-challenging data, which is often extremely abraded and damaged. This paper proposes a novel semi-supervised model for classification and retrieval of images of archaeological artifacts. This model utilizes unique data that exists in the domain-manual drawings made by special artists. These are used during training to implicitly transfer the domain knowledge from the drawings to their corresponding images, improving their classification results. We show that while learning how to classify, our model also learns how to generate drawings of the artifacts, an important documentation task, which is currently performed manually. Last but not least, we collected a new dataset of stamp-seals of the Southern Levant. Our code(1) and dataset(2) are publicly available.

关键词： Algorithms Algorithms and algorithms applications Arts / games / social media Datasets and evaluations formulations Machine learning architectures

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MAdVerse: A Hierarchical Dataset of Multi-Lingual Ads from Diverse Sources and Categories

MAdVerse: A Hierarchical Dataset of Multi-Lingual Ads from D...

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ieee/CVF Winter conference on applications of computer vision (WACV)

作者： Sagar, Amruth Srivastava, Rishabh Rakshitha, R. T. Venna, Venkata Kesav Sarvadevabhatla, Ravi Kiran IIIT Hyderabad CVIT Hyderabad India KLE Tech Hubballi India

ISBN: (纸本)9798350318920;9798350318937

The convergence of computer vision and advertising has sparked substantial interest lately. Existing advertisement datasets are either subsets of existing datasets with specialized annotations or feature diverse annotations without a cohesive taxonomy among ad images. Notably, no datasets encompass diverse advertisement styles or semantic grouping at various levels of granularity. Our work addresses this gap by introducing MAdVerse, an extensive, multilingual compilation of more than 50,000 ads from the web, social media websites, and e-newspapers. Advertisements are hierarchically grouped with uniform granularity into 11 categories, divided into 51 sub-categories, and 524 finegrained brands at leaf level, each featuring ads in various languages. We provide comprehensive baseline classification results for prediction tasks within the realm of advertising analysis. These tasks include hierarchical ad classification, source classification, multilingual classification, and inducing hierarchy in existing ad datasets.

关键词： Algorithms applications Commercial / retail Datasets and evaluations

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