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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

共 31021 条记录，以下是4331-4340 订阅

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Expression Transfer Using Flow-based Generative Models

Expression Transfer Using Flow-based Generative Models

引用

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

作者： Valenzuela, Andrea Segura, Carlos Diego, Ferran Gomez, Vicenc Univ Pompeu Fabra Barcelona Catalonia Spain Tel Res Barcelona Catalonia Spain

ISBN: (纸本)9781665448994

Among the different deepfake generation techniques, flow-based methods appear as natural candidates. Due to the property of invertibility, flow-based methods eliminate the necessity of person-specific training and are able to reconstruct any input image almost perfectly to human perception. We present a method for deepfake generation based on facial expression transfer using flow-based generative models. Our approach relies on simple latent vector operations akin to the ones used for attribute manipulation, but for transferring expressions between identity source-target pairs. We show the feasibility of this approach using a pre-trained Glow model and small sets of source and target images, not necessarily considered during prior training. We also provide an evaluation pipeline of the generated images in terms of similarities between identities and Action Units encoding the expression to be transferred. Our results show that an efficient expression transfer is feasible by using the proposed approach setting up a first precedent in deepfake content creation, and its evaluation, independently of the training identities.

关键词： Training computer vision Image coding conferences Computational modeling Pipelines pattern recognition

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DeepLM: Large-scale Nonlinear Least Squares on Deep Learning Frameworks using Stochastic Domain Decomposition

DeepLM: Large-scale Nonlinear Least Squares on Deep Learning...

引用

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

作者： Huang, Jingwei Huang, Shan Sun, Mingwei Huawei Technol Rieman Lab Shenzhen Guangdong Peoples R China Wuhan Univ Wuhan Hubei Peoples R China

ISBN: (纸本)9781665445092

We propose a novel approach for large-scale nonlinear least squares problems based on deep learning frameworks. Nonlinear least squares are commonly solved with the Levenberg-Marquardt (LM) algorithm for fast convergence. We implement a general and efficient LM solver on a deep learning framework by designing a new backward jacobian network to enable automatic sparse jacobian matrix computation. Furthermore, we introduce a stochastic domain decomposition approach that enables batched optimization and preserves convergence for large problems. We evaluate our method by solving bundle adjustment as a fundamental problem. Experiments show that our optimizer significantly outperforms the state-of-the-art solutions and existing deep learning solvers considering quality, efficiency, and memory. Our stochastic domain decomposition enables distributed optimization, consumes little memory and time, and achieves similar quality compared to a global solver. As a result, our solver effectively solves nonlinear least squares on an extremely large scale. Our code will be available based on Pytorch(1) and Mindspore(2).

关键词： Deep learning Jacobian matrices Bundle adjustment computer vision Codes Memory management pattern recognition

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Editing like Humans: A Contextual, Multimodal Framework for Automated Video Editing

Editing like Humans: A Contextual, Multimodal Framework for ...

引用

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

作者： Koorathota, Sharath Adelman, Patrick Cotton, Kelly Sajda, Paul Columbia Univ Dept Biomed Engn New York NY 10027 USA Fovea Inc New York NY 10001 USA CUNY Grad Ctr Dept Psychol New York NY USA

ISBN: (纸本)9781665448994

We propose an automated video editing model, which we term contextual and multimodal video editing (CMVE). The model leverages visual and textual metadata describing videos, integrating essential information from both modalities, and uses a learned editing style from a single example video to coherently combine clips. The editing model is useful for tasks such as generating news clip montages and highlight reels given a text query that describes the video storyline. The model exploits the perceptual similarity between video frames, objects in videos and text descriptions to emulate coherent video editing. Amazon Mechanical Turk participants made judgements comparing CMVE to expert human editing. Experimental results showed no significant difference in the CMVE vs human edited video in terms of matching the text query and the level of interest each generates, suggesting CMVE is able to effectively integrate semantic information across visual and textual modalities and create perceptually coherent quality videos typical of human video editors. We publicly release an online demonstration of our method.

关键词： Training Visualization computer vision conferences Semantics Metadata pattern recognition

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Affordance Perception by a Knowledge-Guided vision-Language Model with Efficient Error Correction 4th

Affordance Perception by a Knowledge-Guided Vision-Languag...

引用

4th International conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Burghouts, Gertjan J. Schaaphok, Marianne van Bekkum, Michael Meijer, Wouter Hillerstrüm, Fieke van Mil, Jell TNO The Hague2597 AK Netherlands

ISBN: (纸本)9789819787043

Mobile robot platforms will increasingly be tasked with activities that involve grasping and manipulating objects in open world environments. Affordance understanding provides a robot with means to realise its goals and execute its tasks, e.g. to achieve autonomous navigation in unknown buildings where it has to find doors and ways to open these. In order to get actionable suggestions, robots need to be able to distinguish subtle differences between objects, as they may result in different action sequences: doorknobs require grasp and twist, while handlebars require grasp and push. In this paper, we improve affordance perception for a robot in an open-world setting. Our contribution is threefold: (1) We provide an affordance representation with precise, actionable affordances;(2) We connect this knowledge base to a foundational vision-language models (VLM) and prompt the VLM for a wider variety of new and unseen objects;(3) We apply a human-in-the-loop for corrections on the output of the VLM. The mix of affordance representation, image detection and a human-in-the-loop is effective for a robot to search for objects to achieve its goals. We have demonstrated this in a scenario of finding various doors and the many different ways to open them. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Visual languages

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Overlap Suppression Clustering for Offline Multi-Camera People Tracking

Overlap Suppression Clustering for Offline Multi-Camera Peop...

引用

IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Ryuto Yoshida Junichi Okubo Junichiro Fujii Masazumi Amakata Takayoshi Yamashita Yachiyo Engineering Co. Ltd. Chubu University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multi-Camera People Tracking is a multifaceted issue that requires the integration of several computer vision tasks, such as Object Detection, Multiple Object Tracking, and Person Re-identification. This study presents a multi-camera people tracking method that comprises four main processes: (1) single camera people tracking based on overlap suppression clustering, (2) representative image extraction using pose estimation for re-identification, (3) re-identification using hierarchical clustering with average linkage, and (4) low-identifiability tracklets *** RIIPS team achieved the highest Higher Order Tracking Accuracy (HOTA) of 71.9446% in the 2024 AI City Challenge Track 1.

关键词： Couplings computer vision Correlation conferences Pose estimation Urban areas Object detection

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SLADE: A Self-Training Framework For Distance Metric Learning

SLADE: A Self-Training Framework For Distance Metric Learnin...

引用

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

作者： Duan, Jiali Lin, Yen-Liang Son Tran Davis, Larry S. Kuo, C-C Jay Univ Southern Calif Los Angeles CA 90089 USA Amazon Seattle WA USA

ISBN: (纸本)9781665445092

Most existing distance metric learning approaches use fully labeled data to learn the sample similarities in an embedding space. We present a self-training framework, SLADE, to improve retrieval performance by leveraging additional unlabeled data. We first train a teacher model on the labeled data and use it to generate pseudo labels for the unlabeled data. We then train a student model on both labels and pseudo labels to generate final feature embeddings. We use self-supervised representation learning to initialize the teacher model. To better deal with noisy pseudo labels generated by the teacher network, we design a new feature basis learning component for the student network, which learns basis functions of feature representations for unlabeled data. The learned basis vectors better measure the pairwise similarity and are used to select high-confident samples for training the student network. We evaluate our method on standard retrieval benchmarks: CUB-200, Cars196 and In-shop. Experimental results demonstrate that with additional unlabeled data, our approach significantly improves the performance over the state-of-the-art methods.

关键词： Training computer vision Art Benchmark testing Extraterrestrial measurements Data models pattern recognition

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Visual Event-Based Egocentric Human Action recognition 10th

Visual Event-Based Egocentric Human Action Recognition

引用

10th Iberian conference on pattern recognition and Image Analysis (IbPRIA)

作者： Moreno-Rodriguez, Francisco J. Javier Traver, V Barranco, Francisco Dimiccoli, Mariella Pla, Filiberto Univ Jaume 1 Castellon de La Plana Spain Univ Jaume 1 Inst New Imaging Technol Castellon de La Plana Spain Univ Granada CITIC Dept Comp Architecture & Technol Granada Spain Inst Robot & Informat Ind CSIC UPC Barcelona Spain

ISBN: (纸本)9783031048814;9783031048807

This paper lies at the intersection of three research areas: human action recognition, egocentric vision, and visual event-based sensors. The main goal is the comparison of egocentric action recognition performance under either of two visual sources: conventional images, or event-based visual data. In this work, the events, as triggered by asynchronous event sensors or their simulation, are spatio-temporally aggregated into event frames (a grid-like representation). This allows to use exactly the same neural model for both visual sources, thus easing a fair comparison. Specifically, a hybrid neural architecture combining a convolutional neural network and a recurrent network is used. It is empirically found that this general architecture works for both, conventional gray-level frames, and event frames. This finding is relevant because it reveals that no modification or adaptation is strictly required to deal with event data for egocentric action classification. Interestingly, action recognition is found to perform better with event frames, suggesting that these data provide discriminative information that aids the neural model to learn good features.

关键词： Egocentric view Action recognition Event vision

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Insights from the Future for Continual Learning

Insights from the Future for Continual Learning

引用

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

作者： Douillard, Arthur Valle, Eduardo Ollion, Charles Robert, Thomas Cord, Matthieu Sorbonne Univ Paris France Heuritech Paris France Univerty Campinas Campinas Brazil CMAP Ecole Polytech Palaiseau France Valeo Ai Paris France

ISBN: (纸本)9781665448994

Continual learning aims to learn tasks sequentially, with (often severe) constraints on the storage of old learning samples, without suffering from catastrophic forgetting. In this work, we propose prescient continual learning, a novel experimental setting, to incorporate existing information about the classes, prior to any training data. Usually, each task in a traditional continual learning setting evaluates the model on present and past classes, the latter with a limited number of training samples. Our setting adds future classes, with no training samples at all. We introduce Ghost Model, a representation-learning-based model for continual learning using ideas from zero-shot learning. A generative model of the representation space in concert with a careful adjustment of the losses allows us to exploit insights from future classes to constraint the spatial arrangement of the past and current classes. Quantitative results on the AwA2 and aP&Y datasets and detailed visualizations showcase the interest of this new setting and the method we propose to address it.(1)

关键词： Training computer vision Computational modeling conferences Training data pattern recognition Task analysis

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Rotation Coordinate Descent for Fast Globally Optimal Rotation Averaging

Rotation Coordinate Descent for Fast Globally Optimal Rotati...

引用

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

作者： Parra, Alvaro Chng, Shin-Fang Chin, Tat-Jun Eriksson, Anders Reid, Ian Univ Adelaide Sch Comp Sci Adelaide SA Australia Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld Australia

ISBN: (纸本)9781665445092

Under mild conditions on the noise level of the measurements, rotation averaging satisfies strong duality, which enables global solutions to be obtained via semidefinite programming (SDP) relaxation. However, generic solvers for SDP are rather slow in practice, even on rotation averaging instances of moderate size, thus developing specialised algorithms is vital. In this paper, we present a fast algorithm that achieves global optimality called rotation coordinate descent (RCD). Unlike block coordinate descent (BCD) which solves SDP by updating the semidefinite matrix in a row-by-row fashion, RCD directly maintains and updates all valid rotations throughout the iterations. This obviates the need to store a large dense semidefinite matrix. We mathematically prove the convergence of our algorithm and empirically show its superior efficiency over state-of-the-art global methods on a variety of problem configurations. Maintaining valid rotations also facilitates incorporating local optimisation routines for further speed-ups. Moreover, our algorithm is simple to implement(1).

关键词： computer vision Coordinate measuring machines Convex functions pattern recognition Noise measurement Velocity measurement Rotation measurement

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Mirror3D: Depth Refinement for Mirror Surfaces

Mirror3D: Depth Refinement for Mirror Surfaces

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

作者： Tan, Jiaqi Lin, Weijie Chang, Angel X. Savva, Manolis Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781665445092

Despite recent progress in depth sensing and 3D reconstruction, mirror surfaces are a significant source of errors. To address this problem, we create the Mirror3D dataset: a 3D mirror plane dataset based on three RGBD datasets (Matterpot3D, NYUv2 and ScanNet) containing 7,011 mirror instance masks and 3D planes. We then develop Mirror3DNet: a module that refines raw sensor depth or estimated depth to correct errors on mirror surfaces. Our key idea is to estimate the 3D mirror plane based on RGB input and surrounding depth context, and use this estimate to directly regress mirror surface depth. Our experiments show that Mirror3DNet significantly mitigates errors from a variety of input depth data, including raw sensor depth and depth estimation or completion methods.

关键词： Surface reconstruction computer vision Three-dimensional displays Estimation computer architecture Prediction methods Sensors

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