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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22906 条记录，以下是4811-4820 订阅

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Improving Unsupervised Image Clustering With Robust Learning

Improving Unsupervised Image Clustering With Robust Learning

引用

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

作者： Park, Sungwon Han, Sungwon Kim, Sundong Kim, Danu Park, Sungkyu Hong, Seunghoon Cha, Meeyoung Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea Inst for Basic Sci Korea Data Sci Grp Daejeon South Korea

ISBN: (纸本)9781665445092

Unsupervised image clustering methods often introduce alternative objectives to indirectly train the model and are subject to faulty predictions and overconfident results. To overcome these challenges, the current research proposes an innovative model RUC that is inspired by robust learning. RUC's novelty is at utilizing pseudo-labels of existing image clustering models as a noisy dataset that may include misclassified samples. Its retraining process can revise misaligned knowledge and alleviate the overconfidence problem in predictions. The model's flexible structure makes it possible to be used as an add-on module to other clustering methods and helps them achieve better performance on multiple datasets. Extensive experiments show that the proposed model can adjust the model confidence with better calibration and gain additional robustness against adversarial noise.

关键词： Training computer vision Clustering methods Computational modeling Predictive models Robustness Calibration

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Face2Exp: Combating Data Biases for Facial Expression recognition

Face2Exp: Combating Data Biases for Facial Expression Recogn...

引用

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

作者： Zeng, Dan Lin, Zhiyuan Yan, Xiao Liu, Yuting Wang, Fei Tang, Bo Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Dept Comp Sci & Engn Shenzhen Peoples R China JDcom Beijing Peoples R China Southern Univ Sci & Technol Sch Microelect Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Facial expression recognition (FER) is challenging due to the class imbalance caused by data collection. Existing studies tackle the data bias problem using only labeled facial expression dataset. Orthogonal to existing FER methods, we propose to utilize large unlabeled face recognition (FR) datasets to enhance FER. However, this raises another data bias problem-the distribution mismatch between FR and FER data. To combat the mismatch, we propose the Meta-Face2Exp framework, which consists of a base network and an adaptation network. The base network learns prior expression knowledge on class-balanced FER data while the adaptation network is trained to fit the pseudo labels of FR data generated by the base model. To combat the mismatch between FR and FER data, Meta-Face2Exp uses a circuit feedback mechanism, which improves the base network with the feedback from the adaptation network. Experiments show that our Meta-Face2Exp achieves comparable accuracy to state-of-the-art FER methods with 10% of the labeled FER data utilized by the baselines. We also demonstrate that the circuit feedback mechanism successfully eliminates data bias(1).

关键词： Knowledge engineering computer vision Adaptation models Face recognition Feedback circuits Data collection Data models

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Transitional Adaptation of Pretrained Models for Visual Storytelling

Transitional Adaptation of Pretrained Models for Visual Stor...

引用

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

作者： Yu, Youngjae Chung, Jiwan Yun, Heeseung Kim, Jongseok Kim, Gunhee Allen Inst AI Seattle WA 98103 USA Seoul Natl Univ Seoul South Korea Violet Seoul South Korea

ISBN: (纸本)9781665445092

Previous models for vision-to-language generation tasks usually pretrain a visual encoder and a language generator in the respective domains and jointly finetune them with the target task. However, this direct transfer practice may suffer from the discord between visual specificity and language fluency since they are often separately trained from large corpora of visual and text data with no common ground. In this work, we claim that a transitional adaptation task is required between pretraining and finetuning to harmonize the visual encoder and the language model for challenging downstream target tasks like visual storytelling. We propose a novel approach named Transitional Adaptation of Pre-trained Model (TAPM) that adapts the multi-modal modules to each other with a simpler alignment task between visual inputs only with no need for text labels. Through extensive experiments, we show that the adaptation step significantly improves the performance of multiple language models for sequential video and image captioning tasks. We achieve new state-of-the-art performance on both language metrics and human evaluation in the multi-sentence description task of LSMDC 2019 [50] and the image storytelling task of VIST [18]. Our experiments reveal that this improvement in caption quality does not depend on the specific choice of language models.

关键词： Measurement Visualization Adaptation models computer vision Computational modeling Generators pattern recognition

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BigGait: Learning Gait Representation You Want by Large vision Models

BigGait: Learning Gait Representation You Want by Large Visi...

引用

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

作者： Ye, Dingqiang Fan, Chao Ma, Jingzhe Liu, Xiaoming Yu, Shiqi Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Michigan State Univ E Lansing MI USA

ISBN: (纸本)9798350353013;9798350353006

Gait recognition stands as one of the most pivotal remote identification technologies and progressively expands across research and industry communities. However, existing gait recognition methods heavily rely on task-specific upstream driven by supervised learning to provide explicit gait representations like silhouette sequences, which inevitably introduce expensive annotation costs and potential error accumulation. Escaping from this trend, this work explores effective gait representations based on the all-purpose knowledge produced by task-agnostic Large vision Models (LVMs) and proposes a simple yet efficient gait framework, termed BigGait. Specifically, the Gait Representation Extractor (GRE) within BigGait draws upon design principles from established gait representations, effectively transforming all-purpose knowledge into implicit gait representations without requiring third-party supervision signals. Experiments on CCPG, CAISA-B* and SUSTech1K indicate that BigGait significantly outperforms the previous methods in both within-domain and cross-domain tasks in most cases, and provides a more practical paradigm for learning the next-generation gait representation. Finally, we delve into prospective challenges and promising directions in LVMs-based gait recognition, aiming to inspire future work in this emerging topic. The source code is available at https://github. com/ShiqiYu/OpenGait.

关键词： Gait recognition Large vision Models Person Re-Identification

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Autolabeling 3D Objects with Differentiable Rendering of SDF Shape Priors

Autolabeling 3D Objects with Differentiable Rendering of SDF...

引用

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

作者： Zakharov, Sergey Kehl, Wadim Bhargava, Arjun Gaidon, Adrien Tech Univ Munich Munich Germany Toyota Res Inst Toyota Japan

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171685

We present an automatic annotation pipeline to recover 9D cuboids and 3D shapes from pre-trained off-the-shelf 2D detectors and sparse LIDAR data. Our autolabeling method solves an ill-posed inverse problem by considering learned shape priors and optimizing geometric and physical parameters. To address this challenging problem, we apply a novel differentiable shape renderer to signed distance fields (SDF), leveraged together with normalized object coordinate spaces (NOCS). Initially trained on synthetic data to predict shape and coordinates, our method uses these predictions for projective and geometric alignment over real samples. Moreover, we also propose a curriculum learning strategy, iteratively retraining on samples of increasing difficulty in subsequent self-improving annotation rounds. Our experiments on the KITTI3D dataset show that we can recover a substantial amount of accurate cuboids, and that these autolabels can be used to train 3D vehicle detectors with state-of-the-art results. The code is available at ***/TRI-ML/sdflabel.

关键词： Shape Three-dimensional displays Rendering (computer graphics) Cascading style sheets Two dimensional displays Pipelines Solid modeling

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Volumetric Bundle Adjustment for Online Photorealistic Scene Capture

Volumetric Bundle Adjustment for Online Photorealistic Scene...

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

作者： Clark, Ronald Imperial Coll London London England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Efficient photorealistic scene capture is a challenging task. Current online reconstruction systems can operate very efficiently, but images generated from the models captured by these systems are often not photorealistic. Recent approaches based on neural volume rendering can render novel views at high fidelity, but they often require a long time to train, making them impractical for applications that require real-time scene capture. In this paper, we propose a system that can reconstruct photorealistic models of complex scenes in an efficient manner. Our system processes images online, i.e. it can obtain a good quality estimate of both the scene geometry and appearance at roughly the same rate the video is captured. To achieve the efficiency, we propose a hierarchical feature volume using VDB grids. This representation is memory efficient and allows for fast querying of the scene information. Secondly, we introduce a novel optimization technique that improves the efficiency of the bundle adjustment which allows our system to converge to the target camera poses and scene geometry much faster. Experiments on real-world scenes show that our method outperforms existing systems in terms of efficiency and capture quality. To the best of our knowledge, this is the first method that can achieve online photorealistic scene capture.

关键词： Bundle adjustment Geometry Streaming media Rendering (computer graphics) Cameras Real-time systems Topology

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Query-Focused Video Summarization: Dataset, Evaluation, and A Memory Network Based Approach 30

Query-Focused Video Summarization: Dataset, Evaluation, and ...

引用

30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Sharghi, Aidean Laurel, Jacob S. Gong, Boqing Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA Univ Alabama Birmingham Dept Comp Sci Birmingham AL 35294 USA

ISBN: (纸本)9781538604571

Recent years have witnessed a resurgence of interest in video summarization. However, one of the main obstacles to the research on video summarization is the user subjectivity - users have various preferences over the summaries. The subjectiveness causes at least two problems. First, no single video summarizer fits all users unless it interacts with and adapts to the individual users. Second, it is very challenging to evaluate the performance of a video summarizer. To tackle the first problem, we explore the recently proposed query-focused video summarization which introduces user preferences in the form of text queries about the video into the summarization process. We propose a memory network parameterized sequential determinantal point process in order to attend the user query onto different video frames and shots. To address the second challenge, we contend that a good evaluation metric for video summarization should focus on the semantic information that humans can perceive rather than the visual features or temporal overlaps. To this end, we collect dense per-video-shot concept annotations, compile a new dataset, and suggest an efficient evaluation method defined upon the concept annotations. We conduct extensive experiments contrasting our video summarizer to existing ones and present detailed analyses about the dataset and the new evaluation method.

关键词： Semantics Measurement Visualization Automobiles Dictionaries computer vision Knowledge discovery Videotapes Dictionaries as Topic data mining Automobiles Semantics images computer vision Visualization evaluation method Dataset Cars semiotics

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Learned Binary Spectral Shape Descriptor for 3D Shape Correspondence 29

Learned Binary Spectral Shape Descriptor for 3D Shape Corres...

引用

2016 ieee conference on computer vision and pattern recognition (CVPR)

作者： Xie, Jin Wang, Meng Fang, Yi NYU Multimedia & Visual Comp Lab Abu Dhabi U Arab Emirates New York Univ Abu Dhabi Dept Elect & Comp Engn Abu Dhabi U Arab Emirates NYU Tandon Sch Engn Dept Elect & Comp Engn Brooklyn NY USA

ISBN: (纸本)9781467388511

Dense 3D shape correspondence is an important problem in computer vision and computer graphics. Recently, the local shape descriptor based 3D shape correspondence approaches have been widely studied, where the local shape descriptor is a real-valued vector to characterize the geometrical structure of the shape. Different from these real-valued local shape descriptors, in this paper, we propose to learn a novel binary spectral shape descriptor with the deep neural network for 3D shape correspondence. The binary spectral shape descriptor can require less storage space and enable fast matching. First, based on the eigen-vectors of the Laplace-Beltrami operator, we construct a neural network to form a nonlinear spectral representation to characterize the shape. Then, for the defined positive and negative points on the shapes, we train the constructed neural network by minimizing the errors between the outputs and their corresponding binary descriptors, minimizing the variations of the outputs of the positive points and maximizing the variations of the outputs of the negative points, simultaneously. Finally, we binarize the output of the neural network to form the binary spectral shape descriptor for shape correspondence. The proposed binary spectral shape descriptor is evaluated on the SCAPE and TOSCA 3D shape datasets for shape correspondence. The experimental results demonstrate the effectiveness of the proposed binary shape descriptor for the shape correspondence task.

关键词： Three dimensional computer graphics

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A real-time pedestrian classification method for event-based dynamic stereo vision

A real-time pedestrian classification method for event-based...

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2010 ieee computer society conference on computer vision and pattern recognition - Workshops, CVPRW 2010

作者： Schraml, S. Belbachir, A.N. Brändle, N. Neuroinformatics Safety and Security Department AIT Austrian Institute of Technology Donau-City Strasse 1/5 A-1220 Vienna Austria Dynamic Transportation Systems Mobility Department AIT Austrian Institute of Technology Giefinggasse 2 A-1210 Vienna Austria

ISBN: (纸本)9781424470297

This paper proposes a real-time implementation of a clustering and classification method using asynchronous events generated upon scene activities by an event-based dynamic stereo vision system. The inherent detection of moving objects offered by the dynamic stereo vision system comprising a pair of dynamic vision sensors allows event-based stereo vision in real-time and a 3D representation of moving objects. The clustering and classification method exploit the sparse spatio-temporal representation of sensor's events for real-time detection and separation between moving objects. The method makes use of density and distance metrics for clustering asynchronous events generated by scene dynamics (changes in the scene). It has been evaluated on clustering the events of moving persons across the sensor field of view. The method has been implemented on the Blackfin BF537 from analog device and tested on real scenarios with more than 100 persons. The results show that the resulting asynchronous events can be successfully clustered in real-time and that the classification rate of pedestrians is successful in more than 92% of the cases. © 2010 ieee.

关键词： Stereo vision

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Mix and match networks: encoder-decoder alignment for zero-pair image translation 31

Mix and match networks: encoder-decoder alignment for zero-p...

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31st ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Yaxing van de Weijer, Joost Herranz, Luis Univ Autonoma Barcelona Comp Vis Ctr Barcelona Spain

ISBN: (纸本)9781538664209

We address the problem of image translation between domains or modalities for which no direct paired data is available (i.e. zero-pair translation). We propose mix and match networks, based on multiple encoders and decoders aligned in such a way that other encoder-decoder pairs can be composed at test time to perform unseen image translation tasks between domains or modalities for which explicit paired samples were not seen during training. We study the impact of autoencoders, side information and losses in improving the alignment and transferability of trained pair-wise translation models to unseen translations. We show our approach is scalable and can perform colorization and style transfer between unseen combinations of domains. We evaluate our system in a challenging cross-modal setting where semantic segmentation is estimated from depth images, without explicit access to any depth-semantic segmentation training pairs. Our model outperforms baselines based on pix2pix and CycleGAN models.

关键词： Decoding Training Image segmentation Semantics Task analysis computer vision Training data

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