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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2015"

共 4931 条记录，以下是81-90 订阅

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Live Demonstration: Incremental Motion Estimation for Event-based Cameras by Dispersion Minimisation

Live Demonstration: Incremental Motion Estimation for Event-...

引用

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

作者： Nunes, Urbano Miguel Demiris, Yiannis Imperial Coll London Personal Robot Lab London England

ISBN: (纸本)9781665448994

Live demonstration setup. (Left) The setup consists of a DAVIS346B event camera connected to a standard consumer laptop and undergoes some motion. (Right) The motion estimates are plotted in red and, for rotation-like motions, the angular velocities provided by the camera IMU are also plotted in blue. This plot exemplifies an event camera undergoing large rotational motions (up to ~ 1000 deg/s) around the (a) x-axis, (b) y-axis and (c) z-axis. Overall, the incremental motion estimation method follows the IMU measurements. Optionally, the resultant global optical flow can also be shown, as well as the corresponding generated events by accumulating them onto the image plane (bottom left corner).

关键词： Portable computers Motion estimation conferences Cameras Minimization pattern recognition Motion measurement

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Human Action Adverb recognition: ADHA Dataset and A Three-Stream Hybrid Model 31

Human Action Adverb Recognition: ADHA Dataset and A Three-St...

引用

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

作者： Pang, Bo Zha, Kaiwen Lu, Cewu Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We introduce the first benchmark for a new problem - recognizing human action adverbs (HAA): "Adverbs Describing Human Actions" (ADHA). We demonstrate some key features of ADHA: a semantically complete set of adverbs describing human actions, a set of common, describable human actions, and an exhaustive labelling of simultaneously emerging actions in each video. We commit an in-depth analysis on the implementation of current effective models in action recognition and image captioning on adverb recognition, and the results reveal that such methods are unsatisfactory. Furthermore, we propose a novel three-stream hybrid model to tackle the HAA problem, which achieves better performances and receives relatively promising results.

关键词： Image recognition Mood Emotion recognition Sports Benchmark testing Face pattern recognition

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Uncertainty Gated Network for Land Cover Segmentation 31

Uncertainty Gated Network for Land Cover Segmentation

引用

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

作者： Pascual, Guillem Segui, Santi Vioia, Joidi Univ Barcelona Barcelona Spain

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

The production of thematic maps depicting land cover is one of the most common applications of remote sensing. To this end, several semantic segmentation approaches, based on deep learning, have been proposed in the literature, but land cover segmentation is still considered an open problem due to some specific problems related to remote sensing imaging. In this paper we propose a novel approach to deal with the problem of modelling multiscale contexts surrounding pixels of different land cover categories. The approach leverages the computation of a heteroscedastic measure of uncertainty when classifying individual pixels in an image. This classification uncertainty measure is used to define a set of memory gates between layers that allow a principled method to select the optimal decision for each pixel.

关键词： Uncertainty Image segmentation Logic gates Semantics computer vision Measurement uncertainty Image resolution

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A Comparison of Crowd Commotion Measures from Generative Models

A Comparison of Crowd Commotion Measures from Generative Mod...

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ieee conference on computer vision and pattern recognition (CVPR)

作者： Mohammadi, Sadegh Kiani, Hamed Perina, Alessandro Murino, Vittorio Ist Italiano Tecnol Pattern Anal & Comp Vis Dept PAVIS Genoa Italy

ISBN: (纸本)9781467367592

Detecting abnormal events in video sequences is a challenging task that has been broadly investigated over the last decade. The main challenges come from the lack of a clear definition of abnormality and from the scarcity, of ten absence, of abnormal training samples. To address these two shortages, the computer vision community made use of generative models to learn normal behavioral patterns in videos. Then, for each test observation, a (crowd) commotion measure is computed quantifying the deviation from the normal model. In this paper;we evaluated two different families of generative models, namely topic models, representing the standard choice, and the most recent Counting Grids which have never been considered for this task. Moreover;we also extended the 2D Counting Grid, introduced for the analysis of images, to three dimensions, making the model able to capture the spatial-temporal relationships of the videos. In the experimental section, we compared all the approaches on jive challenging sequences showing the superiority of the 3-D counting grid.

关键词： Cameras Computational modeling Context Feature extraction Histograms Surveillance Video sequences

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Proposal-free Lidar Panoptic Segmentation with Pillar-level Affinity

Proposal-free Lidar Panoptic Segmentation with Pillar-level ...

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

作者： Chen, Qi Vora, Sourabh Johns Hopkins Univ Baltimore MD 21218 USA Motional Boston MA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose a simple yet effective proposal-free architecture for lidar panoptic segmentation. We jointly optimize both semantic segmentation and class-agnostic instance classification in a single network using a pilla-rbased bird's-eye view representation. The instance classification head learns pairwise affinity between pillars to determine whether the pillars belong to the same instance or not. We further propose a local clustering algorithm to propagate instance ids by merging semantic segmentation and affinity predictions. Our experiments on nuScenes dataset show that our approach outperforms previous proposal-free methods and is comparable to proposal-based methods which requires extra annotation from object detection.

关键词： computer vision Laser radar conferences Semantics Merging Clustering algorithms Object detection

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Fast Solvers for Minimal Radial Distortion Relative Pose Problems

Fast Solvers for Minimal Radial Distortion Relative Pose Pro...

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

作者： Oskarsson, Magnus Lund Univ Ctr Math Sci Lund Sweden

ISBN: (纸本)9781665448994

In this paper we present a unified formulation for a large class of relative pose problems with radial distortion and varying calibration. For minimal cases, we show that one can eliminate the number of parameters down to one to three. The relative pose can then be expressed using varying calibration constraints on the fundamental matrix, with entries that are polynomial in the parameters. We can then apply standard techniques based on the action matrix and Sturm sequences to construct our solvers. This enables efficient solvers for a large class of relative pose problems with radial distortion, using a common framework. We evaluate a number of these solvers for robust two-view inlier and epipolar geometry estimation, used as minimal solvers in RANSAC.

关键词： Geometry computer vision conferences Estimation Tools Distortion Cameras

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SomethingFinder: Localizing undefined regions using referring expressions

SomethingFinder: Localizing undefined regions using referrin...

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

作者： Eum, Sungmin Han, David Briggs, Gordon US Army Res Lab Adelphi MD 20783 USA Booz Allen Hamilton Mclean VA 22102 USA US Naval Res Lab Washington DC USA

ISBN: (纸本)9781728193601

Previous research on localizing a target region in an image referred to by a natural language expression has occurred within an object-centric paradigm. However, in practice, there may not be any easily named or identifiable objects near a target location. Instead, references may need to rely on basic visual attributes, such as color or geometric clues. An expression like "a red something beside a blue vertical line" could still pinpoint a target location. As such, we begin to explore the open challenge of computational object-agnostic reference by constructing a novel dataset and by devising a new set of algorithms that can identify a target region in an image when given a referring expression containing only basic conceptual features.

关键词： computer vision

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ABAW: Valence-Arousal Estimation, Expression recognition, Action Unit Detection & Multi-Task Learning Challenges

ABAW: Valence-Arousal Estimation, Expression Recognition, Ac...

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

作者： Kollias, Dimitrios Queen Mary Univ London London England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper describes the third Affective Behavior Analysis in-the-wild (ABAW) Competition, held in conjunction with ieee International conference on computer vision and pattern recognition (CVPR), 2022. The 3rd ABAW Competition is a continuation of the Competitions held at ICCV 2021, ieee FG 2020 and ieee CVPR 2017 conferences, and aims at automatically analyzing affect. This year the Competition encompasses four Challenges: i) uni-task Valence-Arousal Estimation, ii) uni-task Expression Classification, iii) uni-task Action Unit Detection, and iv) MultiTask-Learning. All the Challenges are based on a common benchmark database, Aff-Wild2, which is a large scale in-the-wild database and the first one to be annotated in terms of valence-arousal, expressions and action units. In this paper, we present the four Challenges, with the utilized Competition corpora, we outline the evaluation metrics and present both the baseline systems and the top performing teams' per Challenge. Finally we illustrate the obtained results of the baseline systems and of all participating teams.

关键词： Measurement computer vision Databases conferences Estimation Benchmark testing Multitasking

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Where are my clothes? A multi-level approach for evaluating deep instance segmentation architectures on fashion images

Where are my clothes? A multi-level approach for evaluating ...

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

作者： Jouanneau, Warren Bugeau, Aurelie Palyart, Marc Papadakis, Nicolas Vezard, Laurent Univ Bordeaux LaBRI CNRS Bordeaux INP F-33400 Talence France Lectra F-33610 Cestas France Univ Bordeaux IMB CNRS Bordeaux INPUMR 5251 F-33400 Talence France

ISBN: (纸本)9781665448994

In this paper we present an extensive evaluation of instance segmentation in the context of images containing clothes. We propose a multi level evaluation that completes the classical overlapping criteria given by IoU. In particular, we quantify both the contour and color content accuracy of the the predicted segmentation masks. We demonstrate that the proposed evaluation framework is relevant to obtain meaningful insights on models performance through experiments conducted on five state of the art instance segmentation methods.

关键词： Image segmentation computer vision Image color analysis conferences Computational modeling computer architecture pattern recognition

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Does Image Anonymization Impact computer vision Training?

Does Image Anonymization Impact Computer Vision Training?

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

作者： Hukkelas, Hakon Lindseth, Frank Norwegian Univ Sci & Technol Dept Comp Sci Trondheim Norway

ISBN: (纸本)9798350302493

Image anonymization is widely adapted in practice to comply with privacy regulations in many regions. However, anonymization often degrades the quality of the data, reducing its utility for computer vision development. In this paper, we investigate the impact of image anonymization for training computer vision models on key computer vision tasks (detection, instance segmentation, and pose estimation). Specifically, we benchmark the recognition drop on common detection datasets, where we evaluate both traditional and realistic anonymization for faces and full bodies. Our comprehensive experiments reflect that traditional image anonymization substantially impacts final model performance, particularly when anonymizing the full body. Furthermore, we find that realistic anonymization can mitigate this decrease in performance, where our experiments reflect a minimal performance drop for face anonymization. Our study demonstrates that realistic anonymization can enable privacy-preserving computer vision development with minimal performance degradation across a range of important computer vision benchmarks.

关键词： computer vision

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