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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

共 11897 条记录，以下是1751-1760 订阅

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HARA: A Hierarchical Approach for Robust Rotation Averaging

HARA: A Hierarchical Approach for Robust Rotation Averaging

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Lee, Seong Hun Civera, Javier Univ Zaragoza I3A Zaragoza Spain

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose a novel hierarchical approach for multiple rotation averaging, dubbed HARA. Our method incrementally initializes the rotation graph based on a hierarchy of triplet support. The key idea is to build a spanning tree by prioritizing the edges with many strong triplet supports and gradually adding those with weaker and fewer supports. This reduces the risk of adding outliers in the spanning tree. As a result, we obtain a robust initial solution that enables us to filter outliers prior to nonlinear optimization. With minimal modification, our approach can also integrate the knowledge of the number of valid 2D-2D correspondences. We perform extensive evaluations on both synthetic and real datasets, demonstrating state-of-the-art results.

关键词： computer vision pattern recognition Optimization

来源：评论

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Domain Generalization with Correlated Style Uncertainty

Domain Generalization with Correlated Style Uncertainty

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Zhang, Zheyuan Wang, Bin Jha, Debesh Demir, Ugur Bagci, Ulas Northwestern Univ Machine & Hybrid Intelligence Lab Evanston IL 60208 USA

ISBN: (纸本)9798350318920;9798350318937

Domain generalization (DG) approaches intend to extract domain invariant features that can lead to a more robust deep learning model. In this regard, style augmentation is a strong DG method taking advantage of instance-specific feature statistics containing informative style characteristics to synthetic novel domains. While it is one of the state-of-the-art methods, prior works on style augmentation have either disregarded the interdependence amongst distinct feature channels or have solely constrained style augmentation to linear interpolation. To address these research gaps, in this work, we introduce a novel augmentation approach, named Correlated Style Uncertainty (CSU), surpassing the limitations of linear interpolation in style statistic space and simultaneously preserving vital correlation information. Our method's efficacy is established through extensive experimentation on diverse cross-domain computer vision and medical imaging classification tasks: PACS, Office-Home, and Camelyon17 datasets, and the Duke-Market1501 instance retrieval task. The results showcase a remarkable improvement margin over existing state-of-the-art techniques. The source code is available https://***/freshman97/CSU.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

来源：评论

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MS-EVS: Multispectral event-based vision for deep learning based face detection

MS-EVS: Multispectral event-based vision for deep learning b...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Himmi, Saad Parret, Vincent Chhatkuli, Ajad Van Gool, Luc Swiss Fed Inst Technol Comp Vision Lab Zurich Switzerland Sony Europe BV Stuttgart Lab 1 Sony Semicond Solut Europe Weybridge Surrey England

ISBN: (纸本)9798350318920;9798350318937

Event-based sensing is a relatively new imaging modality that enables low latency, low power, high temporal resolution and high dynamic range acquisition. These properties make it a highly desirable sensor for edge applications and in high dynamic range environments. As of today, most event-based sensors are monochromatic (grayscale), capturing light from a wide spectral range over the visible, in a single channel. In this paper, we introduce multispectral events and study their advantages. In particular, we consider multiple bands in the visible and near-infrared range, and explore their potential compared to monochromatic events and conventional multispectral imaging for the face detection task. We further release the first large scale bimodal face detection datasets, with RGB videos and their simulated color events, N-MobiFace and N-YoutubeFaces, and a smaller dataset with multispectral videos and events, N-SpectralFace. We find that early fusion of multispectral events significantly improves the face detection performance, compared to the early fusion of conventional multispectral images. This result shows that multispectral events carry relatively more useful information about the scene than conventional multispectral images do, with respect to their grayscale equivalent. To the best of our knowledge, our proposed method is the first exploratory research on multispectral events, specifically including near infrared data.

关键词： Algorithms Algorithms Applications Datasets and evaluations Embedded sensing / real-time techniques Image recognition and understanding

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Mix and Localize: Localizing Sound Sources in Mixtures

Mix and Localize: Localizing Sound Sources in Mixtures

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Hu, Xixi Chen, Ziyang Owens, Andrew Univ Michigan Ann Arbor MI 48109 USA Univ Texas Austin Austin TX 78712 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present a method for simultaneously localizing multiple sound sources within a visual scene. This task requires a model to both group a sound mixture into individual sources, and to associate them with a visual signal. Our method jointly solves both tasks at once, using a formulation inspired by the contrastive random walk of Jabri et al. We create a graph in which images and separated sounds correspond to nodes, and train a random walker to transition between nodes from different modalities with high return probability. The transition probabilities for this walk are determined by an audio-visual similarity metric that is learned by our model. We show through experiments with musical instruments and human speech that our model can successfully localize multiple sounds, outperforming other self-supervised methods.

关键词： Measurement Visualization computer vision Computational modeling Instruments Music pattern recognition

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FOSSIL: Free Open-Vocabulary Semantic Segmentation through Synthetic References Retrieval

FOSSIL: Free Open-Vocabulary Semantic Segmentation through S...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Barsellotti, Luca Amoroso, Roberto Baraldi, Lorenzo Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy IIT CNR Pisa Italy

ISBN: (纸本)9798350318920;9798350318937

Unsupervised Open-Vocabulary Semantic Segmentation aims to segment an image into regions referring to an arbitrary set of concepts described by text, without relying on dense annotations that are available only for a subset of the categories. Previous works rely on inducing pixel-level alignment in a multi-modal space through contrastive training over vast corpora of image-caption pairs. However, representing a semantic category solely through its textual embedding is insufficient to encompass the wide-ranging variability in the visual appearances of the images associated with that category. In this paper, we propose FOSSIL, a pipeline that enables a self-supervised backbone to perform open-vocabulary segmentation relying only on the visual modality. In particular, we decouple the task into two components: (1) we leverage text-conditioned diffusion models to generate a large collection of visual embeddings, starting from a set of captions. These can be retrieved at inference time to obtain a support set of references for the set of textual concepts. Further, (2) we exploit self-supervised dense features to partition the image into semantically coherent regions. We demonstrate that our approach provides strong performance on different semantic segmentation datasets, without requiring any additional training.

关键词： Algorithms Image recognition and understanding

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NeurMiPs: Neural Mixture of Planar Experts for View Synthesis

NeurMiPs: Neural Mixture of Planar Experts for View Synthesi...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Lin, Zhi-Hao Ma, Wei-Chiu Hsu, Hao-Yu Wang, Yu-Chiang Frank Wang, Shenlong Univ Illinois Urbana IL 61801 USA Natl Taiwan Univ New Taipei Taiwan MIT Cambridge MA 02139 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present Neural Mixtures of Planar Experts (NeurMiPs), a novel planar-based scene representation for modeling geometry and appearance. NeurMiPs leverages a collection of local planar experts in 3D space as the scene representation. Each planar expert consists of the parameters of the local rectangular shape representing geometry and a neural radiance field modeling the color and opacity. We render novel views by calculating ray-plane intersections and composite output colors and densities at intersected points to the image. NeurMiPs blends the efficiency of explicit mesh rendering and flexibility of the neural radiance field. Experiments demonstrate superior performance and speed of our proposed method, compared to other 3D representations in novel view synthesis.

关键词： Geometry computer vision Three-dimensional displays Shape Image color analysis Pipelines Rendering (computer graphics)

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Grounding Answers for Visual Questions Asked by Visually Impaired People

Grounding Answers for Visual Questions Asked by Visually Imp...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Chen, Chongyan Anjum, Samreen Gurari, Danna Univ Texas Austin Austin TX 78712 USA Univ Colorado Boulder CO 80309 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Visual question answering is the task of answering questions about images. We introduce the VizWiz-VQA-Grounding dataset, the first dataset that visually grounds answers to visual questions asked by people with visual impairments. We analyze our dataset and compare it with five VQA-Grounding datasets to demonstrate what makes it similar and different. We then evaluate the SOTA VQA and VQA-Grounding models and demonstrate that current SOTA algorithms often fail to identify the correct visual evidence where the answer is located. These models regularly struggle when the visual evidence occupies a small fraction of the image, for images that are higher quality, as well as for visual questions that require skills in text recognition. The dataset, evaluation server, and leader-board all can be found at the following link:https://***/tasks-and-datasets/answergrounding-for-vqa/.

关键词： Visualization computer vision Correlation Grounding Text recognition Computational modeling Visual impairment

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Prompt Distribution Learning

Prompt Distribution Learning

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Lu, Yuning Liu, Jianzhuang Zhang, Yonggang Liu, Yajing Tian, Xinmei Univ Sci & Technol China Hefei Peoples R China Huawei Noahs Ark Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present prompt distribution learning for effectively adapting a pre-trained vision-language model to address downstream recognition tasks. Our method not only learns low-bias prompts from a few samples but also captures the distribution of diverse prompts to handle the varying visual representations. In this way, we provide high-quality task-related content for facilitating recognition. This prompt distribution learning is realized by an efficient approach that learns the output embeddings of prompts instead of the input embeddings. Thus, we can employ a Gaussian distribution to model them effectively and derive a surrogate loss for efficient training. Extensive experiments on 12 datasets demonstrate that our method consistently and significantly outperforms existing methods. For example, with 1 sample per category, it relatively improves the average result by 9.1% compared to human-crafted prompts.

关键词： Training Visualization computer vision Adaptation models Gaussian distribution pattern recognition Task analysis

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STEP - Towards Structured Scene-Text Spotting

STEP - Towards Structured Scene-Text Spotting

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Garcia-Bordils, Sergi Karatzas, Dimosthenis Rusinol, Marcal UAB Comp Vision Ctr Birmingham AL 35294 USA AllRead MLT Barcelona Spain

ISBN: (纸本)9798350318920;9798350318937

We introduce the structured scene-text spotting task, which requires a scene-text OCR system to spot text in the wild according to a query regular expression. Contrary to generic scene-text OCR, structured scene-text spotting seeks to dynamically condition both detection and recognition on user-provided regular expressions. To tackle this task, we propose the Structured TExt sPotter (STEP), a model that exploits the provided text structure to guide the OCR process. STEP is able to deal with regular expressions that contain spaces and it is not bound to detection at word-level granularity. Our approach enables accurate zero-shot structured text spotting in a wide variety of real-world reading scenarios and is solely trained on publicly available data. To demonstrate the effectiveness of our approach, we introduce a new challenging test dataset that contains several types of out-of-vocabulary structured text, reflecting important reading applications such as weight information, serial numbers, license plates etc. We demonstrate that STEP can provide specialized OCR performance on demand in all tested scenarios. The code and test dataset are released at https://***/CVC-DAG/STEP.

关键词： Algorithms Algorithms Algorithms and algorithms Datasets and evaluations formulations Image recognition and understanding Machine learning architectures

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Efficient MAE towards Large-Scale vision Transformers

Efficient MAE towards Large-Scale Vision Transformers

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Han, Qiu Zhang, Gongjie Huang, Jiaxing Gao, Peng Wei, Zhang Lu, Shijian Nanyang Technol Univ S Lab Singapore Singapore Black Sesame Technol Singapore Singapore Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350318920;9798350318937

Masked Autoencoder (MAE) has demonstrated superb pre-training efficiency for vision Transformer, thanks to its partial input paradigm and high mask ratio (0.75). However, MAE often suffers from severe performance drop under higher mask ratios, which hinders its potential toward larger-scale vision Transformers. In this work, we identify that the performance drop is largely attributed to the over-dominance of difficult reconstruction targets, as higher mask ratios lead to more sparse visible patches and fewer visual clues for reconstruction. To mitigate this issue, we design Efficient MAE that introduces a novel Difficulty-Flatten Loss and a decoder masking strategy, enabling a higher mask ratio for more efficient pre-training. The Difficulty-Flatten Loss provides balanced supervision on reconstruction targets of different difficulties, mitigating the performance drop under higher mask ratios effectively. Additionally, the decoder masking strategy discards the most difficult reconstruction targets, which further alleviates the optimization difficulty and accelerates the pre-training clearly. Our proposed Efficient MAE introduces 27% and 30% pre-training runtime accelerations for the ViT-Large and ViT-Huge models, provides valuable insights into MAE's optimization, and paves the way for larger-scale vision Transformer pre-training. Code and pre-trained models will be released.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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