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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 ...

引用

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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Fuse-PN: A Novel Architecture for Anomaly pattern Segmentation in Aerial Agricultural Images

Fuse-PN: A Novel Architecture for Anomaly Pattern Segmentati...

引用

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

作者： Innani, Shubham Dutande, Prasad Baheti, Bhakti Talbar, Sanjay Baid, Ujjwal SGGS Inst Engn & Technol Ctr Excellence Signal & Image Proc Nanded 431606 India

ISBN: (纸本)9781665448994

Deep learning and pattern recognition in smart farming has seen rapid growth as a building bridge between crop science and computer vision. One of the important application is anomaly segmentation in agriculture like weed, standing water, cloud shadow, etc. Our research work focuses on aerial farmland image dataset known as Agriculture vision. We propose to have data fusion of R, G, B, and NIR modalities that enhances the feature extraction and also propose Efficient Fused Pyramid Network (Fuse-PN) for anomaly pattern segmentation. The proposed encoder module is a bottom-up pathway having a compound scaled network and decoder module is a top-down pyramid network enhancing features at different scales having rich semantic features with lateral connections of low-level features. This proposed approach achieved a mean dice similarity score of 0.8271 for six agricultural anomaly patterns of Agriculture vision dataset and outperforms various approaches in literature.

关键词： Deep learning Training Image segmentation computer vision Semantics Data integration computer architecture

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Reconsidering CO2 emissions from computer vision

Reconsidering CO2 emissions from Computer Vision

引用

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

作者： Fu, Andre Hosseini, Mahdi S. Plataniotis, Konstantinos N. Univ Toronto Toronto ON Canada Univ New Brunswick Fredericton NB Canada

ISBN: (纸本)9781665448994

Climate change is a pressing issue that is currently affecting and will affect every part of our lives. It's becoming incredibly vital we, as a society, address the climate crisis as a universal effort, including those in the computer vision (CV) community. In this work, we analyze the total cost of CO2 emissions by breaking it into (1) the architecture creation cost and (2) the life-time evaluation cost. We show that over time, these costs are non-negligible and are having a direct impact on our future. Importantly, we conduct an ethical analysis of how the CV-community is unintentionally overlooking its own ethical AI principles by emitting this level of CO2. To address these concerns, we propose adding "enforcement" as a pillar of ethical AI and provide some recommendations for how architecture designers and broader CV community can curb the climate crisis.

关键词： computer vision Climate change conferences computer architecture Pressing pattern recognition Artificial intelligence

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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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Can domain adaptation make object recognition work for everyone?

Can domain adaptation make object recognition work for every...

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

作者： Prabhu, Viraj Selvaraju, Ramprasaath R. Hoffman, Judy Naik, Nikhil Georgia Tech Atlanta GA 30332 USA Artera AI Berkeley CA USA Salesforce Res Washington DC USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Despite the rapid progress in deep visual recognition, modern computer vision datasets significantly overrepresent the developed world and models trained on such datasets underperform on images from unseen geographies. We investigate the effectiveness of unsupervised domain adaptation (UDA) of such models across geographies at closing this performance gap. To do so, we first curate two shifts from existing datasets to study the Geographical DA problem, and discover new challenges beyond data distribution shift: context shift, wherein object surroundings may change significantly across geographies, and subpopulation shift, wherein the intra-category distributions may shift. We demonstrate the inefficacy of standard DA methods at Geographical DA, highlighting the need for specialized geographical adaptation solutions to address the challenge of making object recognition work for everyone.

关键词： Geography computer vision Adaptation models Visualization Image recognition Computational modeling conferences

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As Seen on TV: Automatic Basketball Video Production using Gaussian-based Actionness and Game States recognition

As Seen on TV: Automatic Basketball Video Production using G...

引用

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

作者： Quiroga, Julian Carrillo, Henry Maldonado, Edisson Ruiz, John Zapata, Luis M. Genius Sports Comp Vis Medellin Colombia

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Automatic video production of sports aims at producing an aesthetic broadcast of sporting events. We present a new video system able to automatically produce a smooth and pleasant broadcast of Basketball games using a single fixed 4K camera. The system automatically detects and localizes players, ball and referees, to recognize main action coordinates and game states yielding to a professional cameraman-like production of the basketball event. We also release a fully annotated dataset consisting of single 4K camera and twelve-camera videos of basketball games.

关键词： Cameras Games Production Streaming media Robot vision systems Computational modeling

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SCVRL: Shuffled Contrastive Video Representation Learning

SCVRL: Shuffled Contrastive Video Representation Learning

引用

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

作者： Dorkenwald, Michael Xiao, Fanyi Brattoli, Biagio Tighe, Joseph Modolo, Davide Heidelberg Univ Heidelberg Germany AWS AI Labs Palo Alto CA USA AWS Palo Alto CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose SCVRL, a novel contrastive-based framework for self-supervised learning for videos. Differently from previous contrast learning based methods that mostly focus on learning visual semantics (e.g., CVRL), SCVRL is capable of learning both semantic and motion patterns. For that, we reformulate the popular shuffling pretext task within a modern contrastive learning paradigm. We show that our transformer-based network has a natural capacity to learn motion in self-supervised settings and achieves strong performance, outperforming CVRL on four benchmarks.

关键词： Representation learning Visualization Semantics Self-supervised learning Benchmark testing Transformers pattern recognition

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Key Point-Based Driver Activity recognition

Key Point-Based Driver Activity Recognition

引用

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

作者： Vats, Arpita Anastasiu, David C. Santa Clara Univ Santa Clara CA 95053 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We present a key point-based activity recognition framework, built upon pre-trained human pose estimation and facial feature detection models. Our method extracts complex static and movement-based features from key frames in videos, which are used to predict a sequence of key-frame activities. Finally, a merge procedure is employed to identify robust activity segments while ignoring outlier frame activity predictions. We analyze the different components of our framework via a wide array of experiments and draw conclusions with regards to the utility of the model and ways it can be improved. Results show our model is competitive, taking the 11th place out of 27 teams submitting to Track 3 of the 2022 AI City Challenge.

关键词： computer vision conferences Urban areas Pose estimation Activity recognition Predictive models Feature extraction

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Towards Explaining Image-Based Distribution Shifts

Towards Explaining Image-Based Distribution Shifts

引用

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

作者： Kulinski, Sean Inouye, David I. Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Distribution shift can have fundamental consequences such as signaling a change in the operating environment or significantly reducing the accuracy of downstream models. Thus, understanding such distribution shifts is critical for examining and hopefully mitigating the effect of such a shift. Most prior work has focused on either natively handling distribution shift (e.g., Domain Generalization) or merely detecting a shift while assuming any detected shift can be understood and handled appropriately by a human operator. For the latter, we hope to aid in these manual mitigation tasks by explaining the distribution shift to an operator. To this end, we suggest two methods: providing a set of interpretable mappings from the original distribution to the shifted one or providing a set of distributional counterfactual examples. We provide preliminary experiments on these two methods, and discuss important concepts and challenges for moving towards a better understanding of image-based distribution shifts.

关键词： computer vision conferences pattern recognition Task analysis

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Active vision Dataset Benchmark 31

Active Vision Dataset Benchmark

引用

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

作者： Ammirato, Phil Berg, Alexander C. Kosecka, Jana Univ N Carolina Chapel Hill NC 27514 USA George Mason Univ Fairfax VA 22030 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Several recent efforts in computer vision indicate a trend toward studying and understanding problems in larger scale environments, beyond single images, and focus on connections to tasks in navigation, mobile manipulation, and visual question answering. A common goal of these tasks is the capability of moving in the environment, acquiring novel views during perception and while performing a task. This capability comes easily in synthetic environments, however achieving the same effect with real images is much more laborious. We propose using the existing Active vision Dataset to form a benchmark for such problems in a real-world settings with real images. The dataset is well suited for evaluating tasks of multiview active recognition, target driven navigation, and target search, and also can be effective for studying the transfer of strategies learned in simulation to real settings.

关键词： Task analysis Training Visualization Robots Benchmark testing Navigation

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