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检索条件"任意字段=IEEE Information Visualization Conference/IEEE Visualization Conference"

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Blast visualization Using NBOS

Blast Visualization Using NBOS

引用

ieee Research and Applications of Photonics In Defense conference (RAPID)

作者： Manpreet Singh Scientist G Retired Photonics Group TBRL DRDO Chandigarh India

NBOS — Natural Background Oriented Shadowgraph technique has been developed for large scale Blast visualization for qualitative as well as quantitative understanding of blast phenomenon and measurement of blast param...

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Deep Network for Extremely Low-Resolution Human Action Recognition 18

Deep Network for Extremely Low-Resolution Human Action Recog...

引用

18th ieee International conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Chaudhary, Sachin Patil, Prashant W. Dudhane, Akshay Murala, Subrahmanyam BML Munjal Univ Kapriwas Haryana India Deakin Univ A2I2 Geelong Vic Australia MBZUAI Abu Dhabi U Arab Emirates Indian Inst Technol Ropar CVPR Lab Rupnagar Punjab India

ISBN: (纸本)9781665463829

Due to advancement in automated applications, privacy-preserving is an emerging concern. This concern is more significant in the case of human-centred surveillance application like human action recognition (HAR). Along with privacy concern, the computational complexity due to the huge size of video data is another major concern. To overcome these limitations, an attempt is made to examine the domain of human action recognition in low-resolution (LR) videos. The extremely LR video data ensures sufficient distortion in visual information to hide the identity of the person. Therefore, working with LR videos can resolve the above mentioned concerns of privacy preserving and computational complexity up to a certain extent. In this paper, a new generative adversarial network (GAN) based neural architecture is proposed for HAR in extremely low-resolution videos. The extensive results analysis with ablation study on the state-of-the-art datasets proves the effectiveness of the proposed method over the existing methods for LR-HAR.

关键词： Privacy visualization Surveillance Superresolution Generative adversarial networks Feature extraction Distortion

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Vicinal Counting Networks

Vicinal Counting Networks

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ranjan, Viresh Minh Hoai SUNY Stony Brook Stony Brook NY 11794 USA VinAI Res Hanoi Vietnam

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We tackle the task of Few-Shot Counting. Given an image containing multiple objects of a novel visual category and few exemplar bounding boxes depicting the visual category of interest, we want to count all of the instances of the desired visual category in the image. A key challenge in building an accurate few-shot visual counter is the scarcity of annotated training data due to the laborious effort needed for collecting and annotating the data. To address this challenge, we propose Vicinal Counting Networks, which learn to augment the existing training data along with learning to count. A Vicinal Counting Network consists of a generator and a counting network. The generator takes as input an image along with a random noise vector and generates an augmented version of the input image. The counting network learns to count the objects in the original and augmented images. The training signal for the generator comes from the counting loss of the counting network, and the generator aims to synthesize images which result in a small counting loss. Unlike GANs which are trained in an adversarial setting, Vicinal Counting Networks are trained in a cooperative setting where the generator aims to help the counting network in achieving accurate predictions on the synthesized images. We also show that our proposed data augmentation framework can be extended to other counting tasks like crowd counting.

关键词： Training visualization Computer vision conferences Buildings Training data Generators

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Feature Query Networks: Neural Surface Description for Camera Pose Refinement

Feature Query Networks: Neural Surface Description for Camer...

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Germain, Hugo DeTone, Daniel Pascoe, Geoffrey Schmidt, Tanner Novotny, David Newcombe, Richard Sweeney, Chris Szeliski, Richard Balntas, Vasileios Ecole Ponts ParisTech Paris France Meta Real Labs Menlo Pk CA USA Facebook AI Res Menlo Pk CA USA Univ Washington Seattle WA 98195 USA Real Labs Menlo Pk CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Accurate 6-DoF camera pose estimation in known environments can be a very challenging task, especially when the query image was captured at viewpoints strongly differing from the set of reference camera poses. While structure-based methods have proved to deliver accurate camera pose estimates, they rely on pre-computed 3D descriptors coming from reference images often misaligned with query images. This discrepancy can subsequently harm downstream camera pose estimation tasks. In this paper we introduce the Feature Query Network (FQN), a ray-based descriptor regressor that can be used to query descriptors at known 3D locations under novel viewpoints. We show that the FQN is able to model viewpoint-dependency of high-dimensional keypoint descriptors and bring significant relative improvements to structure-based visual localization baselines.

关键词： Location awareness visualization Three-dimensional displays conferences Pose estimation Neural networks Force

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Effective conditioned and composed image retrieval combining CLIP-based features

Effective conditioned and composed image retrieval combining...

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Baldrati, Alberto Bertini, Marco Uricchio, Tiberio Del Bimbo, Alberto Univ Firenze MICC Florence Italy Univ Pisa Pisa Italy

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Conditioned and composed image retrieval extend CBIR systems by combining a query image with an additional text that expresses the intent of the user, describing additional requests w.r.t. the visual content of the query image. This type of search is interesting for e-commerce applications, e.g. to develop interactive multimodal searches and chatbots. In this demo, we present an interactive system based on a combiner network, trained using contrastive learning, that combines visual and textual features obtained from the OpenAI CLIP network to address conditioned CBIR. The system can be used to improve e-shop search engines. For example, considering the fashion domain it lets users search for dresses, shirts and toptees using a candidate start image and expressing some visual differences w.r.t. its visual content, e.g. asking to change color, pattern or shape. The proposed network obtains state-of-the-art performance on the FashionIQ dataset and on the more recent CIRR dataset, showing its applicability to the fashion domain for conditioned retrieval, and to more generic content considering the more general task of composed image retrieval.

关键词： visualization Shape Image retrieval Pipelines Search engines Pattern recognition Electronic commerce

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Egocentric Deep Multi-Channel Audio-Visual Active Speaker Localization

Egocentric Deep Multi-Channel Audio-Visual Active Speaker Lo...

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jiang, Hao Murdock, Calvin Ithapu, Vamsi Krishna Meta Real Labs Res Menlo Pk CA 94025 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Augmented reality devices have the potential to enhance human perception and enable other assistive functionalities in complex conversational environments. Effectively capturing the audio-visual context necessary for understanding these social interactions first requires detecting and localizing the voice activities of the device wearer and the surrounding people. These tasks are challenging due to their egocentric nature: the wearer's head motion may cause motion blur, surrounding people may appear in difficult viewing angles, and there may be occlusions, visual clutter, audio noise, and bad lighting. Under these conditions, previous state-of-the-art active speaker detection methods do not give satisfactory results. Instead, we tackle the problem from a new setting using both video and multi-channel microphone array audio. We propose a novel end-to-end deep learning approach that is able to give robust voice activity detection and localization results. In contrast to previous methods, our method localizes active speakers from all possible directions on the sphere, even outside the camera's field of view, while simultaneously detecting the device wearer's own voice activity. Our experiments show that the proposed method gives superior results, can run in real time, and is robust against noise and clutter.

关键词： Location awareness Voice activity detection visualization Machine vision Lighting Real-time systems Microphone arrays

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Interaction Classification with Key Actor Detection in Multi-Person Sports Videos

Interaction Classification with Key Actor Detection in Multi...

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Askari, Farzaneh Ramaprasad, Rohit Clark, James J. Levine, Martin D. McGill Univ Montreal PQ Canada Birla Inst Technol & Sci Pilani Rajasthan India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Interaction recognition from multi-person videos is a challenging yet essential task in computer vision. Often the videos depict actions with multiple actors involved, some of whom participate in the main event, and the rest are present in the scene without being part of the actual event. This paper proposes a model to tackle the problem of interaction recognition from multi-person videos. Our model consists of a Recurrent Neural Network (RNN) equipped with a time-varying attention mechanism. It receives scene features and localized actors features to predict the interaction class. Additionally, the attention model identifies the people responsible for the main event. We chose penalty classification from ice hockey broadcast videos as our application. These videos are multi-persons and depict complex interactions between players in a non-laboratory recording setup. We evaluate our model on a new dataset of ice hockey penalty videos and report 93.93% classification accuracy. We include a qualitative analysis of the attention mechanism by visualizing the attention weights. Our code is publicly available (1).

关键词： visualization Computer vision Recurrent neural networks Annotations Computational modeling Ice Recording

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Exploration of Knowledge Graphs via Online Aggregation 38

Exploration of Knowledge Graphs via Online Aggregation

引用

38th ieee International conference on Data Engineering (ICDE)

作者： Kalinsky, Oren Hogan, Aidan Mishali, Oren Etsion, Yoav Kimelfeld, Benny Amazon Haifa Israel Univ Chile DCC Santiago Chile IMFD Santiago Chile Technion Israel Inst Technol Haifa Israel

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

Exploration systems over large-scale RDF knowledge graphs often rely on aggregate count queries to indicate how many results the user can expect for the possible next steps of exploration. Such systems thus encounter a challenging computational problem: evaluating aggregate count queries efficiently enough to allow for interactive exploration. Given that precise results are not always necessary, a promising alternative is to apply online aggregation, where initially imprecise results converge towards more precise results over time. However, stateof-the-art online aggregation algorithms, such as Wander Join, fail to provide accurate results due to frequent rejected paths that slow convergence. We thus devise an algorithm for online aggregation that specializes in exploration queries on knowledge graphs;our proposal leverages the low dimension of RDF graphs, and the low selectivity of exploration queries, by augmenting random walks with exact partial computations using a worstcase optimal join algorithm. This approach reduces the number of rejected paths encountered while retaining a fast sample time. In an experimental study with random interactions exploring two large-scale knowledge graphs, our algorithm shows a clear reduction in error over time versus Wander Join.

关键词： Knowledge engineering visualization Runtime Aggregates Semantics Resource description framework Proposals

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A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation

A Simple Multi-Modality Transfer Learning Baseline for Sign ...

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chen, Yutong Wei, Fangyun Sun, Xiao Wu, Zhirong Lin, Stephen Tsinghua Univ Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

dThis paper proposes a simple transfer learning baseline for sign language translation. Existing sign language datasets (e.g. PHOENIX-2014T, CSL-Daily) contain only about 10K-20K pairs of sign videos, gloss annotations and texts, which are an order of magnitude smaller than typical parallel data for training spoken language translation models. Data is thus a bottleneck for training effective sign language translation models. To mitigate this problem, we propose to progressively pretrain the model from generaldomain datasets that include a large amount of external supervision to within-domain datasets. Concretely, we pretrain the sign-to-gloss visual network on the general domain of human actions and the within-domain of a sign-to-gloss dataset, and pretrain the gloss-to-text translation network on the general domain of a multilingual corpus and the within-domain of a gloss-to-text corpus. The joint model is fine-tuned with an additional module named the visuallanguage mapper that connects the two networks. This simple baseline surpasses the previous state-of-the-art results on two sign language translation benchmarks, demonstrating the effectiveness of transfer learning. With its simplicity and strong performance, this approach can serve as a solid baseline for future research.

关键词： Training visualization Computer vision Machine vision Transfer learning Gesture recognition Assistive technologies

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LASER: LAtent SpacE Rendering for 2D Visual Localization

LASER: LAtent SpacE Rendering for 2D Visual Localization

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Min, Zhixiang Khosravan, Naji Bessinger, Zachary Narayana, Manjunath Kang, Sing Bing Dunn, Enrique Boyadzhiev, Ivaylo Stevens Inst Technol Hoboken NJ 07030 USA Zillow Grp Seattle WA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present LASER, an image-based Monte Carlo Localization (MCL) framework for 2D floor maps. LASER introduces the concept of latent space rendering, where 2D pose hypotheses on the floor map are directly rendered into a geometrically-structured latent space by aggregating viewing ray features. Through a tightly coupled rendering codebook scheme, the viewing ray features are dynamically determined at rendering-time based on their geometries (i.e. length, incident-angle), endowing our representation with view-dependent fine-grain variability. Our codebook scheme effectively disentangles feature encoding from rendering, allowing the latent space rendering to run at speeds above 10KHz. Moreover, through metric learning, our geometrically-structured latent space is common to both pose hypotheses and query images with arbitrary field of views. As a result, LASER achieves state-of-the-art performance on large-scale indoor localization datasets (i.e. ZInD [5] and Structured3D [38]) for both panorama and perspective image queries, while significantly outperforming existing learning-based methods in speed.

关键词： Location awareness Learning systems visualization Monte Carlo methods Lasers Robot vision systems Pose estimation

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