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检索条件"任意字段=2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2013"

共 4491 条记录，以下是141-150 订阅

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Continual Domain Adaptation through Pruning-aided Domain-specific Weight Modulation

Continual Domain Adaptation through Pruning-aided Domain-spe...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Prasanna, B. Sanyal, Sunandini Babu, R. Venkatesh Indian Institute of Science Vision and Ai Lab Bengaluru India

ISBN: (纸本)9798350302493

In this paper, we propose to develop a method to address unsupervised domain adaptation (UDA) in a practical setting of continual learning (CL). The goal is to update the model on continually changing domains while preserving domain-specific knowledge to prevent catastrophic forgetting of past-seen domains. To this end, we build a framework for preserving domain-specific features utilizing the inherent model capacity via pruning. We also perform effective inference using a novel batch-norm based metric to predict the final model parameters to be used accurately. Our approach achieves not only state-of-the-art performance but also prevents catastrophic forgetting of past domains significantly. Our code is made publicly available. 1. © 2023 ieee.

关键词： Domain Knowledge

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PaintInStyle: One-Shot Discovery of Interpretable Directions by Painting

PaintInStyle: One-Shot Discovery of Interpretable Directions...

引用

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

作者： Doner, Berkay Balcioglu, Elif Sema Barin, Merve Rabia Kocasari, Umut Tiftikci, Mert Yanardag, Pinar Bogazici Univ Istanbul Turkey

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The search for interpretable directions in latent spaces of pre-trained Generative Adversarial Networks (GANs) has become a topic of interest. These directions can be utilized to perform semantic manipulations on the GAN generated images. The discovery of such directions is performed either in a supervised way, which requires manual annotation or pre-trained classifiers, or in an unsupervised way, which requires the user to interpret what these directions represent. In this work, we propose a framework that finds a specific manipulation direction using only a single simple sketch drawn on an image. Our method finds directions consisting of channels in the style space of the StyleGAN2 architecture responsible for the desired edits and performs image manipulations comparable with state-of-the-art methods.

关键词： computer vision Annotations conferences Semantics Manuals computer architecture Generative adversarial networks

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Live Demonstration: PINK: Polarity-based Anti-flicker for Event Cameras

Live Demonstration: PINK: Polarity-based Anti-flicker for Ev...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Im, Gyubeom Park, Keunjoo Kim, Junseok Son, Bongki Shin, Seungchul Lee, Haechang Samsung Electronics 1-1 Samsungjeonja-ro Hwaseong-Si Korea Republic of

ISBN: (纸本)9798350302493

Dynamic vision Sensor (DVS) is an event sensor that asynchronously captures an event whenever there is a brightness change in the scene. However, due to the event sensor's high temporal resolution property, it is particularly vulnerable to flicker. Numerous unwanted events caused by flickering can cause serious loads on computing power and prevent the event sensor from extracting useful information, making it difficult to use the event sensor for machine vision applications in practice. In this demo, we propose the PINK (PolarIty-based aNti-flicKer), which filters flicker based on the event polarity (on/off). The proposed method is lightweight and efficient and can be used in real time. To the best of our knowledge, this is the first anti-flicker algorithm that works in real time and provides a usable opticalflow result for machine vision applications in an extreme flicker environment. A video showing the example of live demo is available online at https://***/5UdU0PLZaf8. © 2023 ieee.

关键词： computer vision

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EKILA: Synthetic Media Provenance and Attribution for Generative Art

EKILA: Synthetic Media Provenance and Attribution for Genera...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Balan, Kar Agarwal, Shruti Jenni, Simon Parsons, Andy Gilbert, Andrew Collomosse, John University of Surrey United Kingdom Adobe Inc.

ISBN: (纸本)9798350302493

We present EKILA;a decentralized framework that enables creatives to receive recognition and reward for their contributions to generative AI (GenAI). EKILA proposes a robust visual attribution technique and combines this with an emerging content provenance standard (C2PA) to address the problem of synthetic image provenance - determining the generative model and training data responsible for an AI-generated image. Furthermore, EKILA extends the non-fungible token (NFT) ecosystem to introduce a tokenized representation for rights, enabling a triangular relationship between the asset's Ownership, Rights, and Attribution (ORA). Leveraging the ORA relationship enables creators to express agency over training consent and, through our attribution model, to receive apportioned credit, including royalty payments for the use of their assets in GenAI. © 2023 ieee.

关键词： computer vision

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Area Under the ROC Curve Maximization for Metric Learning

Area Under the ROC Curve Maximization for Metric Learning

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

作者： Gajic, Bojana Amato, Ariel Baldrich, Ramon van de Weijer, Joost Gatta, Carlo Vintra Inc Barcelona Spain Comp Vis Ctr Barcelona Spain

ISBN: (纸本)9781665487399

Most popular metric learning losses have no direct relation with the evaluation metrics that are subsequently applied to evaluate their performance. We hypothesize that training a metric learning model by maximizing the area under the ROC curve (which is a typical performance measure of recognition systems) can induce an implicit ranking suitable for retrieval problems. This hypothesis is supported by previous work that proved that a curve dominates in ROC space if and only if it dominates in Precision-Recall space. To test this hypothesis, we design and maximize an approximated, derivable relaxation of the area under the ROC curve. The proposed AUC loss achieves state-of-the-art results on two large scale retrieval benchmark datasets (Stanford Online Products and DeepFashion In-Shop). Moreover, the AUC loss achieves comparable performance to more complex, domain specific, state-of-the-art methods for vehicle re-identification.

关键词： Training computer vision conferences Area measurement Benchmark testing pattern recognition

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Proposal-free Lidar Panoptic Segmentation with Pillar-level Affinity

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

引用

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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OutfitGAN: Learning Compatible Items for Generative Fashion Outfits

OutfitGAN: Learning Compatible Items for Generative Fashion ...

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

作者： Moosaei, Maryam Lin, Yusan Akhazhanov, Ablaikhan Chen, Huiyuan Wang, Fei Yang, Hao Visa Res San Francisco CA 94158 USA Univ Calif Los Angeles Los Angeles CA 90024 USA Nazarbayev Univ Astana Kazakhstan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Fashion-on-demand is becoming an important concept for fashion industries. Many attempts have been made to leverage machine learning methods to generate fashion designs tailored to customers' tastes. However, how to assemble items together (e.g., compatibility) is crucial in designing high-quality outfits for synthesis images. Here we propose a fashion generation model, named OutfitGAN, which contains two core modules: a Generative Adversarial Network and a Compatibility Network. The generative module is able to generate new realistic high quality fashion items from a specific category, while the compatibility network ensures reasonable compatibility among all items. The experimental results show the superiority of our OutfitGAN.

关键词： Industries computer vision conferences Machine learning Generative adversarial networks pattern recognition

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

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

引用

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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Anomaly Detection in Autonomous Driving: A Survey

Anomaly Detection in Autonomous Driving: A Survey

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

作者： Bogdoll, Daniel Nitsche, Maximilian Zoellner, J. Marius FZI Res Ctr Informat Technol Karlsruhe Germany KIT Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9781665487399

Nowadays, there are outstanding strides towards a future with autonomous vehicles on our roads. While the perception of autonomous vehicles performs well under closed-set conditions, they still struggle to handle the unexpected. This survey provides an extensive overview of anomaly detection techniques based on camera, lidar, radar, multimodal and abstract object level data. We provide a systematization including detection approach, corner case level, ability for an online application, and further attributes. We outline the state-of-the-art and point out current research gaps.

关键词： computer vision Laser radar Roads conferences Radar detection Benchmark testing Cameras

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M2DAR: Multi-View Multi-Scale Driver Action recognition with vision Transformer

M2DAR: Multi-View Multi-Scale Driver Action Recognition with...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Ma, Yunsheng Yuan, Liangqi Abdelraouf, Amr Han, Kyungtae Gupta, Rohit Li, Zihao Wang, Ziran Purdue University College of Engineering United States Toyota Motor North America InfoTech Labs United States

ISBN: (纸本)9798350302493

Ensuring traffic safety and preventing accidents is a critical goal in daily driving, where the advancement of computer vision technologies can be leveraged to achieve this goal. In this paper, we present a multi-view, multi-scale framework for naturalistic driving action recognition and localization in untrimmed videos, namely M2DAR, with a particular focus on detecting distracted driving behaviors. Our system features a weight-sharing, multi-scale Transformer-based action recognition network that learns robust hierarchical representations. Furthermore, we propose a new election algorithm consisting of aggregation, filtering, merging, and selection processes to refine the preliminary results from the action recognition module across multiple views. Extensive experiments conducted on the 7th AI City Challenge Track 3 dataset demonstrate the effectiveness of our approach, where we achieved an overlap score of 0.5921 on the A2 test set. Our source code is available at https://***/PurdueDigitalTwin/M2DAR. © 2023 ieee.

关键词： Statistical tests

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