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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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Depth from Camera Motion and Object Detection

Depth from Camera Motion and Object Detection

引用

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

作者： Griffin, Brent A. Corso, Jason J. Univ Michigan Ann Arbor MI 48109 USA Stevens Inst Artificial Intelligence Hoboken NJ USA

ISBN: (纸本)9781665445092

This paper addresses the problem of learning to estimate the depth of detected objects given some measurement of camera motion (e.g., from robot kinematics or vehicle odometry). We achieve this by 1) designing a recurrent neural network (DBox) that estimates the depth of objects using a generalized representation of bounding boxes and uncalibrated camera movement and 2) introducing the Object Depth via Motion and Detection Dataset (ODMD). ODMD training data are extensible and configurable, and the ODMD benchmark includes 21,600 examples across four validation and test sets. These sets include mobile robot experiments using an end-effector camera to locate objects from the YCB dataset and examples with perturbations added to camera motion or bounding box data. In addition to the ODMD benchmark, we evaluate DBox in other monocular application domains, achieving state-of-the-art results on existing driving and robotics benchmarks and estimating the depth of objects using a camera phone.

关键词： Training Perturbation methods Robot kinematics Robot vision systems Training data Object detection Benchmark testing

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Exploiting Aliasing for Manga Restoration

Exploiting Aliasing for Manga Restoration

引用

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

作者： Xie, Minshan Xia, Menghan Wong, Tien-Tsin Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Chinese Acad Sci Shenzhen Key Lab Virtual Real & Human Interact Te SIAT Shenzhen Peoples R China

ISBN: (纸本)9781665445092

As a popular entertainment art form, manga enriches the line drawings details with bitonal screentones. However, manga resources over the Internet usually show screen-tone artifacts because of inappropriate scanning/rescaling resolution. In this paper, we propose an innovative two-stage method to restore quality bitonal manga from degraded ones. Our key observation is that the aliasing induced by downsampling bitonal screentones can be utilized as informative clues to infer the original resolution and screentones. First, we predict the target resolution from the degraded manga via the Scale Estimation Network (SE-Net) with spatial voting scheme. Then, at the target resolution, we restore the region-wise bitonal screentones via the Manga Restoration Network (MR-Net) discriminatively, depending on the degradation degree. Specifically, the original screentones are directly restored in pattern-identifiable regions, and visually plausible screentones are synthesized in pattern-agnostic regions. Quantitative evaluation on synthetic data and visual assessment on real-world cases illustrate the effectiveness of our method.

关键词： Degradation Deep learning Visualization computer vision Estimation Entertainment industry Image restoration

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Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion

Deep RGB-D Saliency Detection with Depth-Sensitive Attention...

引用

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

作者： Sun, Peng Zhang, Wenhu Wang, Huanyu Li, Songyuan Li, Xi Zhejiang Univ Coll Comp Sci & Technol Shanghai Inst Adv Study Hangzhou Peoples R China

ISBN: (纸本)9781665445092

RGB-D salient object detection (SOD) is usually formulated as a problem of classification or regression over two modalities, i.e., RGB and depth. Hence, effective RGB-D feature modeling and multi-modal feature fusion both play a vital role in RGB-D SOD. In this paper, we propose a depth-sensitive RGB feature modeling scheme using the depth-wise geometric prior of salient objects. In principle, the feature modeling scheme is carried out in a depth-sensitive attention module, which leads to the RGB feature enhancement as well as the background distraction reduction by capturing the depth geometry prior. Moreover, to perform effective multi-modal feature fusion, we further present an automatic architecture search approach for RGB-D SOD, which does well in finding out a feasible architecture from our specially designed multi-modal multi-scale search space. Extensive experiments on seven standard benchmarks demonstrate the effectiveness of the proposed approach against the state-of-the-art.

关键词： Geometry Visualization computer vision computer architecture Object detection Benchmark testing pattern recognition

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Weighted Multi-Kernel Prediction Network for Burst Image Super-Resolution

Weighted Multi-Kernel Prediction Network for Burst Image Sup...

引用

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

作者： Cho, Wooyeong Son, Sanghyeok Kim, Dae-Shik Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665448994

Burst image super-resolution is an ill-posed problem tha' aims to restore a high-resolution (HR) image from a sequence of low-resolution (LR) burst images. To restore a photo-realistic HR image using their abundant information, it is essential to align each burst of frames containing random hand-held motion. Some kernel prediction networks (KPNs) that are operated without external motion compensation such as optical flow estimation have been applied to burst image processing as implicit image alignment modules. However, the existing methods do not consider the interdependencies among the kernels of different sizes that have a significant effect on each pixel. In this paper, we propose a novel weighted multi-kernel prediction network (WMKPN) that can learn the discriminative features on each pixel for burst image super-resolution. Our experimental results demonstrate that WMKPN improves the visual quality of super-resolved images. To the best of our knowledge, it outperforms the state-of-the-art within kernel prediction methods and multiple frame super-resolution (MFSR) on both the Zurich RAW to RGB and BurstSR datasets.

关键词： Visualization Computational modeling Superresolution Prediction methods Motion compensation Image restoration pattern recognition

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Instance Segmentation-based Identification of Pelagic Species in Acoustic Backscatter Data

Instance Segmentation-based Identification of Pelagic Specie...

引用

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

作者： Marques, Tunai Porto Cote, Melissa Rezvanifar, Alireza Albu, Alexandra Branzan Ersahin, Kaan Mudge, Todd Gauthier, Stephane Univ Victoria Elect & Comp Engn Victoria BC Canada ASL Environm Sci Victoria BC Canada Fisheries & Oceans Canada Sidney BC Canada

ISBN: (纸本)9781665448994

This paper addresses the automatic identification of pelagic species in acoustic backscatter data. Large quantities of data acquired during underwater acoustic surveys for environmental monitoring and resources management, visualized as echograms, are typically analyzed manually or semi-automatically by marine biologists, which is time-consuming and prone to errors and inter-expert disagreements. In this paper, we propose to detect pelagic species (schools of herring and of juvenile salmon) from echograms with a deep learning (DL) framework based on instance segmentation, allowing us to carefully study the acoustic properties of the targets and to address specific challenges such as close proximity between schools and varying size. Experimental results demonstrate our system's ability to correctly detect pelagic species from echograms and to outperform an existing object detection framework designed for schools of herring in terms of detection performance and computational resources utilization. Our pixel-level detection method has the advantage of generating a precise identification of the pixel groups forming each detection, opening up many possibilities for automatic biological analyses.

关键词： Training Image segmentation Semantics Object detection Standardization Biology pattern recognition

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Accept the Modality Gap: An Exploration in the Hyperbolic Space

Accept the Modality Gap: An Exploration in the Hyperbolic Sp...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Sameera Ramasinghe Violetta Shevchenko Gil Avraham Ajanthan Thalaiyasingam Amazon Australia

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Recent advancements in machine learning have spotlighted the potential of hyperbolic spaces as they effectively learn hierarchical feature representations. While there has been progress in leveraging hyperbolic spaces in single-modality contexts, its exploration in multimodal settings remains under explored. A recent work has sought to transpose Euclidean multimodal learning techniques to hyperbolic spaces, by adopting a geodesic distance based contrastive loss. However, we show both theoretically and empirically that such spatial proximity based contrastive loss significantly disrupts hierarchies in the latent space. To remedy this, we advocate that the cross-modal representations should accept the inherent modality gap between text and images, and introduce a novel approach to measure cross-modal similarity that does not enforce spatial proximity. Our approach shows remarkable capabilities in preserving unimodal hierarchies while aligning the two modalities. Our experiments on a series of downstream tasks demonstrate that a better latent structure emerges with our objective function while being superior in text-to-image and image-to-text retrieval tasks.

关键词： computer vision Text to image Machine learning Linear programming pattern recognition

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Development and Analysis of computer vision Based SSL Detection Technology for Marine Environment

Development and Analysis of Computer Vision Based SSL Detect...

引用

Software Engineering, Social Network Analysis and Intelligent Computing (SSAIC), Asia-Pacific conference on

作者： Yuxiang Cui Dalian University of Technology Dalian China

ISBN: (数字)9798350393682

ISBN: (纸本)9798350393699

computer vision has been widely used in the field of navigation safety, including ship identification, course prediction, and other applications, as a result of the fast development of pattern recognition and intelligent information processing technology. This has been made possible by the widespread adoption of computer vision technology. The sea sky line (SSL) is a linear interval that differentiates the area of the sky from the surface of the water. It is also an important reference in the perception of the marine environment, which enables vessels that are equipped with visual perception equipment to use visual technology perception equipment to obtain information about the environment around them. When contrasted with the environment of land, the environment of the sea surface is characterized by sudden shifts in the weather and the presence of noticeable waves. Consequently, there are still certain challenges involved in the process of detecting the SSL using computer vision technology. In this study, a comprehensive analysis and summary of the sea-sky line detection algorithms used by several modal cameras is presented.

关键词： computer vision Sea surface Visualization Surface waves Social networking (online) Safety pattern recognition

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Adapt Before Comparison: A New Perspective on Cross-Domain Few-Shot Segmentation

Adapt Before Comparison: A New Perspective on Cross-Domain F...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Jonas Herzog Zhejiang University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Few-shot segmentation performance declines substantially when facing images from a domain different than the training domain, effectively limiting real-world use cases. To alleviate this, recently cross-domain few-shot segmentation (CD-FSS) has emerged. Works that address this task mainly attempted to learn segmentation on a source domain in a manner that generalizes across domains. Surprisingly, we can outperform these approaches while eliminating the training stage and removing their main segmentation network. We show test-time task-adaption is the key for successful CD-FSS instead. Task-adaption is achieved by appending small networks to the feature pyramid of a conventionally classification-pretrained backbone. To avoid overfitting to the few labeled samples in supervised fine-tuning, consistency across augmented views of input images serves as guidance while learning the parameters of the attached layers. Despite our self-restriction not to use any images other than the few labeled samples at test time, we achieve new state-of-the-art performance in CD-FSS, evidencing the need to rethink approaches for the task. Code is available at https://***/vision-Kek/ABCDFSS.

关键词： Training Image segmentation computer vision Adaptation models Limiting Codes pattern recognition

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Multi-modal Aerial View Image Challenge: SAR Classification

Multi-modal Aerial View Image Challenge: SAR Classification

引用

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

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This manuscript delineates the outcomes of the fourth Multi-modal Aerial View Image Challenge - Classification (MAVIC-C). The challenge is aimed at advancing the development of recognition models that leverage Synthetic Aperture Radar (SAR) and Electro-Optical (EO) imagery. Encouraging the integration of data from these two distinct modalities, the challenge seeks to foster the creation of multi-modal approaches that complement characteristics of SAR and EO information. Building upon the precedents set in previous years, the 2021 MAVOC challenge validated the potential of integrating SAR and EO modalities. The subsequent 2022 and 2023 challenges further explored the capabilities of multi-modal frameworks. In its latest iteration, the 2024 challenge presents an enhanced UNIfied COincident Optical and Radar for recognition (UNICORN) dataset alongside a revised competition format, focused on the task of SAR classification. The 2024 challenge evaluates model robustness through out-of-distribution measures, alongside traditional accuracy metrics. The core of this paper is devoted to analyzing the methodologies of the top-performing entries and their performance metrics on a blind test set.

关键词： Measurement Laser radar Image recognition Pipelines Optical imaging Robustness pattern recognition

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Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Image Models

Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Ima...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Gihyun Kwon Simon Jenni Dingzeyu Li Joon-Young Lee Jong Chul Ye Fabian Caba Heilbron KAIST Adobe

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

While there has been significant progress in customizing text-to-image generation models, generating images that combine multiple personalized concepts remains challenging. In this work, we introduce Concept Weaver, a method for composing customized text-to-image diffusion models at inference time. Specifically, the method breaks the process into two steps: creating a template image aligned with the semantics of input prompts, and then personalizing the template using a concept fusion strategy. The fusion strategy incorporates the appearance of the target concepts into the template image while retaining its structural details. The results indicate that our method can generate multiple custom concepts with higher identity fidelity compared to alternative approaches. Furthermore, the method is shown to seamlessly handle more than two concepts and closely follow the semantic meaning of the input prompt without blending appearances across different subjects.

关键词： computer vision Fuses Semantics Text to image Diffusion models pattern recognition Optimization

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