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CLIP-Guided vision-Language Pre-training for Question Answering in 3D Scenes

CLIP-Guided Vision-Language Pre-training for Question Answer...

引用

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

作者： Maria Parelli Alexandros Delitzas Nikolas Hars Georgios Vlassis Sotirios Anagnostidis Gregor Bachmann Thomas Hofmann ETH Zurich Switzerland

Training models to apply linguistic knowledge and visual concepts from 2D images to 3D world understanding is a promising direction that researchers have only recently started to explore. In this work, we design a novel 3D pre-training vision-Language method that helps a model learn semantically meaningful and transferable 3D scene point cloud representations. We inject the representational power of the popular CLIP model into our 3D encoder by aligning the encoded 3D scene features with the corresponding 2D image and text embeddings produced by CLIP. To assess our model’s 3D world reasoning capability, we evaluate it on the downstream task of 3D Visual Question Answering. Experimental quantitative and qualitative results show that our pre-training method outperforms state-of-the-art works in this task and leads to an interpretable representation of 3D scene features.

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Unsupervised Modeling of Signs Embedded in Continuous Sentences

Unsupervised Modeling of Signs Embedded in Continuous Senten...

引用

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

作者： S. Nayak S. Sarkar B. Loeding Department of Computer Science & Engineering University of South Florida Tampa FL USA Department of Special Education University of South Florida Lakeland FL USA

The common practice in sign language recognition is to ?rst construct individual sign models, in terms of discrete state transitions, mostly represented using Hidden Markov Models, from manually isolated sign samples and then to use it to recognize signs in continuous sentences. In this paper we (i) propose a continuous state space model, where the states are based on purely image-based features, without the use of special gloves, and (ii) present an unsupervised approach to both extract and learn models for continuous basic units of signs, which we term as signemes, from continuous sentences. Given a set of sentences with a common sign, we can automatically learn the model for part of the sign, or signeme, that is least affected by coarticulation effects. While there are coarticulation effects in speech recognition, these effects are even stronger in sign language. The model itself is in term of traces in a space of Relational Distributions. Each point in this space represents a Relational Distribution, capturing the spatial relationships between low-level features, such as edge points. We perform speed normalization and then incrementally extract the common sign between sentences, or signemes, with a dynamic programming framework at the core to compute warped distance between two subsentences. We test our idea using the publicly available Boston SignStream Dataset by building signeme models of 18 signs. We test the quality of the models by considering how well we can localize the sign in a new sentence. We also present preliminary results for the ability to generalize across signers.

关键词： Handicapped aids Hidden Markov models Speech recognition Automatic speech recognition State-space methods Lakes Testing computer science computer science education Continuing education

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conference organization

Conference organization

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conference on computer vision and pattern recognition (CVPR)

Provides a listing of current committee members and society officers.

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Learning Cross-Spectral Similarity Measures with Deep Convolutional Neural Networks

Learning Cross-Spectral Similarity Measures with Deep Convol...

引用

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

作者： Cristhian A. Aguilera Francisco J. Aguilera Angel D. Sappa Cristhian Aguilera Ricardo Toledo Computer Vision Center Campus UAB Bellaterra Spain DCC Universitat Autònoma de Barcelona Campus UAB Bellaterra Spain DIE University of Concepción Concepción Chile Escucla Superior Politécnica del Litoral ESPOL FIEC Guayaquil Ecuador DIEE University of Bío-Bío Concepción Chile

The simultaneous use of images from different spectra can be helpful to improve the performance of many computer vision tasks. The core idea behind the usage of cross-spectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN architectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Experimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Additionally, our experiments show that some CNN architectures are capable of generalizing between different cross-spectral domains.

关键词： Measurement Network architecture Training computer architecture Convolution Feature extraction computer vision

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Estimating correspondences of deformable objects "in-the-wild"

Estimating correspondences of deformable objects "in-the-wil...

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2016 ieee conference on computer vision and pattern recognition, CVPR 2016

作者： Zhou, Yuxiang Antonakos, Epameinondas Alabort-I-medina, Joan Roussos, Anastasios Zafeiriou, Stefanos Department of Computing Imperial College London United Kingdom Center for Machine Vision and Signal Analysis University of Oulu Finland

ISBN: (纸本)9781467388511

During the past few years we have witnessed the development of many methodologies for building and fitting Statistical Deformable Models (SDMs). The construction of accurate SDMs requires careful annotation of images with regards to a consistent set of landmarks. However, the manual annotation of a large amount of images is a tedious, laborious and expensive procedure. Furthermore, for several deformable objects, e.g. human body, it is difficult to define a consistent set of landmarks, and, thus, it becomes impossible to train humans in order to accurately annotate a collection of images. Nevertheless, for the majority of objects, it is possible to extract the shape by object segmentation or even by shape drawing. In this paper, we show for the first time, to the best of our knowledge, that it is possible to construct SDMs by putting object shapes in dense correspondence. Such SDMs can be built with much less effort for a large battery of objects. Additionally, we show that, by sampling the dense model, a part-based SDM can be learned with its parts being in correspondence. We employ our framework to develop SDMs of human arms and legs, which can be used for the segmentation of the outline of the human body, as well as to provide better and more consistent annotations for body joints.

关键词： Deformation

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Research on the Web information system development platform based on MVC design pattern

Research on the Web information system development platform ...

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2008 ieee/WIC/ACM International conference on Web Intelligence and Intelligent Agent Technology - workshops, WI-IAT workshops 2008

作者： Ning, Wang Liming, Li Yanzhang, Wang Yi-Bing, Wang Jing, Wang Management School Dalian University of Technology Dalian Liaoning Province China

ISBN: (纸本)9780769534961

In order to make the Web information system development more efficient, improving the reusability, stability and scalability of system, features of a Web information system development are studied. A Web information system development platform adapting to operational requests and achieving quick development based on MVC design pattern is constructed. After introducing the platform's framework system, the design and realization of its main parts, design philosophy and basic framework of the platform are given, integrating application examples. © 2008 ieee.

关键词： Reusability

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Avatar: A virtual reality based tool for collaborative production of theater shows

Avatar: A virtual reality based tool for collaborative produ...

引用

3rd Canadian conference on computer and Robot vision, CRV 2006

作者： Dompierre, Christian Laurendeau, Denis Computer Vision and System Lab. Lavai University Quebec City Que. G1K 7P4 Canada

ISBN: (纸本)0769525423

One of the more important limitations of actual tools for performing arts production and design is that collaboration between designers is hard to achieve. In fact, designers must actually be co-located to collaborate in the design of a show, something that is not always possible. While teleconference tools could be used to partially solve this problem, this solution offers no direct interactivity and no synchronization between designers. Also some problems like perspective effects and single viewpoint constrained by the camera are inherent to this solution. Specialized software for performing arts design (e.g. "Life Forms") do not generally provide real-time collaboration and are not really convenient for collaborative work. Also, these systems are often expensive and complex to operate. A more adapted solution combining concepts from virtual reality, network technology, and computer vision has then been specifically developed for collaborative work by performing arts designers. This paper presents a virtual reality application for supporting distributed collaborative production of theater shows resulting from our research. Among other constraints, this application has to ensure that the virtual scene that is being shared between multiple designers is always in sync (by use of computer vision) with a real counterpart and that this synchronization is achieved in real-time. Also, system cost must be kept as low as possible, platform independence must be achieved whenever possible and, since it is to be used by people that are not computer experts, the application has to be user-friendly. © 2006 ieee.

关键词： Virtual reality

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Fusion of Visual and Ultrasonic Information for Environmental Modelling

Fusion of Visual and Ultrasonic Information for Environmenta...

引用

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

作者： C. Town Computer Laboratory University of Cambridge Cambridge UK

Information obtained from calibrated cameras by means of computer vision is integrated with location events from an ultrasonic tracking system deployed in an indoor office. This results in improved estimates of state and location which are used to augment the environmental model maintained by a sentient computing system. Fusion of the different sources of information takes place at a high level using Bayesian networks to model dependencies and reliabilities of the multi-modal variables. Context is represented using a world model of both the static and dynamic environment. The world model serves both as an ontology of prior information for multi-modal integration and as a source of context for applications.

关键词： computer vision Context modeling Pulse measurements Cameras Maintenance Bayesian methods Ontologies Context-aware services Sensor systems Machine vision

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Overtaking Vehicle Detection Using Dynamic and Quasi-Static Background Modeling

Overtaking Vehicle Detection Using Dynamic and Quasi-Static ...

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

作者： Junxian Wang G. Bebis R. Miller Computer Vision Laboratory University of Nevada Reno NV USA Vehicle Design R&A Department Ford Motor Company Limited Dearborn MI USA

Robust and reliable detection of overtaking vehicles is an important component of any on-board driver assistance system. Optical flow, with the abundant motion information present in image sequences, has been studied extensively for vehicle detection. However, using dense optical ?ow for vehicle detection is sensitive to shocks and vibrations of the mobile camera; image outliers caused by illumination changes; and high computational complexity. To improve vehicle detection performance and reduce computational complexity, we propose an efficient and robust methodology for overtaking vehicle detection based on homogeneous sparse optical flow and eigenspace modeling. Specifically, our method models the background into dynamic and quasi-static regions. Instead of using dense optical flow to model the dynamic parts of the background, we employ homogeneous sparse optical flow, which makes detection more robust to camera shocks and vibrations. Moreover, to make detection robust to illumination changes, we employ a blockbased eigenspace approach to represent quasi-static regions in the background. A region-based hysteresis-thresholding approach, augmented by a localized spatial segmentation procedure, attains a good tradeoff between true detections and false positives. The proposed methodology has been evaluated using challenging traf?c scenes illustrating good performance.

关键词： Vehicle detection Vehicle dynamics Optical sensors Robustness Image motion analysis Electric shock Cameras Lighting Computational complexity Vehicle driving

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An Introduction to the 3rd Workshop on Egocentric (First-Person) vision

An Introduction to the 3rd Workshop on Egocentric (First-Per...

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

作者： Steve Mann Kris M. Kitani Yong Jae Lee M. S. Ryoo Alireza Fathi Department of Electronic and Electrical Engineering University College London London U.K. BlueCrest Capital Management (U.K.) LLP London U.K

Egocentric vision provides a unique perspective of the visual world that is inherently human-centric. Since egocentric cameras are mounted on the user (typically on the user's head), they are naturally primed to gather visual information from our everyday interactions, and can even act on that information in real-time (e.g. for a vision aid). We believe that this human-centric characteristic of egocentric vision can have a large impact on the way we approach central computer vision tasks such as visual detection, recognition, prediction, and socio-behavioral analysis. By taking advantage of the first-person point-of-view paradigm, there have been recent advances in areas such as personalized video summarization, understanding concepts of social saliency, activity analysis with inside-out cameras (a camera to capture eye gaze and an outward-looking camera), recognizing human interactions and modeling focus of attention. However, in many ways people are only beginning to understand the full potential (and limitations) of the first-person paradigm. In the 3rd workshop on Egocentric (First-Person) vision, we bring together researchers to discuss emerging topics such as: Personalization of visual analysis; Socio-behavioral modeling; Understanding group dynamics and interactions; Egocentric video as big data; First-person vision for robotics; and Egographical User Interfaces (EUIs).

关键词： Cameras Surveillance conferences computer vision Sensors Visualization Privacy

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