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

共 52943 条记录，以下是4841-4850 订阅

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PSD: Principled Synthetic-to-Real Dehazing Guided by Physical Priors

PSD: Principled Synthetic-to-Real Dehazing Guided by Physica...

引用

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

作者： Chen, Zeyuan Wang, Yangchao Yang, Yang Liu, Dong Univ Sci & Technol China Hefei Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9781665445092

Deep learning-based methods have achieved remarkable performance for image dehazing. However, previous studies are mostly focused on training models with synthetic hazy images, which incurs performance drop when the models are used for real-world hazy images. We propose a Principled Synthetic-to-real Dehazing (PSD) framework to improve the generalization performance of dehazing. Starting from a dehazing model backbone that is pre-trained on synthetic data, PSD exploits real hazy images to fine-tune the model in an unsupervised fashion. For the fine-tuning, we leverage several well-grounded physical priors and combine them into a prior loss committee. PSD allows for most of the existing dehazing models as its backbone, and the combination of multiple physical priors boosts dehazing significantly. Through extensive experiments, we demonstrate that our PSD framework establishes the new state-of-the-art performance for real-world dehazing, in terms of visual quality assessed by no-reference quality metrics as well as subjective evaluation and downstream task performance indicator.

关键词： Training Measurement Learning systems Visualization computer vision Data models pattern recognition

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Audio Provenance Analysis in Heterogeneous Media Sets

Audio Provenance Analysis in Heterogeneous Media Sets

引用

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

作者： Milica Gerhardt Luca Cuccovillo Patrick Aichroth Fraunhofer Institute for Digital Media Technology IDMT Ilemanu Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper introduces a framework for Audio Provenance Analysis, addressing the complex challenge of ana-lyzing heterogeneous sets of audio items without requiring any prior knowledge of their content. Our framework applies a novel approach that combines partial audio matching and phylogeny techniques. It constructs directed acyclic graphs to capture the origins and the evolution of content within near-duplicate audio clusters, identifying the least altered versions and tracing the reuse of content within these clusters. The approach is evaluated for two selected application scenarios, demonstrating that it can accurately determine the direction of content reuse and identify parent-child relationships, while also offering a dedicated dataset for benchmarking future research in this area.

关键词： Directed acyclic graph computer vision conferences Media Benchmark testing Phylogeny pattern recognition

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Troubleshooting Blind Image Quality Models in the Wild

Troubleshooting Blind Image Quality Models in the Wild

引用

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

作者： Wang, Zhihua Wang, Haotao Chen, Tianlong Wang, Zhangyang Ma, Kede City Univ Hong Kong Hong Kong Peoples R China Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781665445092

Recently, the group maximum differentiation competition (gMAD) has been used to improve blind image quality assessment (BIQA) models, with the help of full-reference metrics. When applying this type of approach to troubleshoot "best-performing" BIQA models in the wild, we are faced with a practical challenge: it is highly nontrivial to obtain stronger competing models for efficient failure-spotting. Inspired by recent findings that difficult samples of deep models may be exposed through network pruning, we construct a set of "self-competitors," as random ensembles of pruned versions of the target model to be improved. Diverse failures can then be efficiently identified via self-gMAD competition. Next, we fine-tune both the target and its pruned variants on the human-rated gMAD set. This allows all models to learn from their respective failures, preparing themselves for the next round of self-gMAD competition. Experimental results demonstrate that our method efficiently troubleshoots BIQA models in the wild with improved generalizability.

关键词： Image quality Measurement computer vision Computational modeling Mathematical models pattern recognition Computational efficiency

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Neural Auto-Exposure for High-Dynamic Range Object Detection

Neural Auto-Exposure for High-Dynamic Range Object Detection

引用

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

作者： Onzon, Emmanuel Mannan, Fahim Heide, Felix Algolux Montreal PQ Canada Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781665445092

Real-world scenes have a dynamic range of up to 280 dB that todays imaging sensors cannot directly capture. Existing live vision pipelines tackle this fundamental challenge by relying on high dynamic range (HDR) sensors that try to recover HDR images from multiple captures with different exposures. While HDR sensors substantially increase the dynamic range, they are not without disadvantages, including severe artifacts for dynamic scenes, reduced fill factor, lower resolution, and high sensor cost. At the same time, traditional auto-exposure methods for low-dynamic range sensors have advanced as proprietary methods relying on image statistics separated from downstream vision algorithms. In this work, we revisit auto-exposure control as an alternative to HDR sensors. We propose a neural network for exposure selection that is trained jointly, end-to-end with an object detector and an image signal processing (ISP) pipeline. To this end, we use an HDR dataset for automotive object detection and an HDR training procedure. We validate that the proposed neural auto-exposure control, which is tailored to object detection, outperforms conventional auto-exposure methods by more than 6 points in mean average precision (mAP).

关键词： Image sensors Training computer vision Pipelines Object detection computer architecture Dynamic range

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OpenCV and Python for Emotion Analysis of Face Expressions 3

OpenCV and Python for Emotion Analysis of Face Expressions

引用

3rd International conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Islam Zim, Md Khadimul Lovely Professional University Department of Computer Science and Engineering Phagwara India

ISBN: (纸本)9798350336238

If someone showed you a picture of themselves and asked you to describe how they feel, you'd probably have a good idea. Think about how useful it would be if your computer could do that! But what if you could enhance the things you have? It seems like a completely absurd idea. In the past, it was easy to infer a person's emotional state simply by observing their face. However, it is much more challenging for a computer to perform this task. Emotion recognition in photographs is now feasible with the help of machine learning and computer vision. Facial expression recognition is a growing subset of the field of facial recognition. Despite the fact that there are methods that use machine learning and artificial intelligence to accomplish the same goals, this work attempts to use the OpenCV approach to recognise expressions and classify the expressions based on the photos. © 2023 ieee.

关键词： Python

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Mirror3D: Depth Refinement for Mirror Surfaces

Mirror3D: Depth Refinement for Mirror Surfaces

引用

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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Omnimatte: Associating Objects and Their Effects in Video

Omnimatte: Associating Objects and Their Effects in Video

引用

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

作者： Lu, Erika Cole, Forrester Dekel, Tali Zisserman, Andrew Freeman, William T. Rubinstein, Michael Google Res Mountain View CA 94043 USA Univ Oxford Oxford England Weizmann Inst Sci Rehovot Israel

ISBN: (纸本)9781665445092

computer vision is increasingly effective at segmenting objects in images and videos;however, scene effects related to the objects-shadows, reflections, generated smoke, etc.-are typically overlooked. Identifying such scene effects and associating them with the objects producing them is important for improving our fundamental understanding of visual scenes, and can also assist a variety of applications such as removing, duplicating, or enhancing objects in video. In this work, we take a step towards solving this novel problem of automatically associating objects with their effects in video. Given an ordinary video and a rough segmentation mask over time of one or more subjects of interest, we estimate an omnimatte for each subject-an alpha matte and color image that includes the subject along with all its related time-varying scene elements. Our model is trained only on the input video in a self-supervised manner, without any manual labels, and is generic-it produces omnimattes automatically for arbitrary objects and a variety of effects. We show results on real-world videos containing interactions between different types of subjects (cars, animals, people) and complex effects, ranging from semitransparent elements such as smoke and reflections, to fully opaque effects such as objects attached to the subject.

关键词： Training Image segmentation computer vision Color Visual effects Reflection pattern recognition

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Cross-Modal Center Loss for 3D Cross-Modal Retrieval

Cross-Modal Center Loss for 3D Cross-Modal Retrieval

引用

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

作者： Jing, Longlong Vahdani, Elahe Tan, Jiaxing Tian, Yingli CUNY New York NY 10021 USA

ISBN: (纸本)9781665445092

Cross-modal retrieval aims to learn discriminative and modal-invariant features for data from different modalities. Unlike the existing methods which usually learn from the features extracted by offline networks, in this paper, we propose an approach to jointly train the components of cross-modal retrieval framework with metadata, and enable the network to find optimal features. The proposed end-to-end framework is updated with three loss functions: 1) a novel cross-modal center loss to eliminate cross-modal discrepancy, 2) cross-entropy loss to maximize inter-class variations, and 3) mean-square-error loss to reduce modality variations. In particular, our proposed cross-modal center loss minimizes the distances of features from objects belonging to the same class across all modalities. Extensive experiments have been conducted on the retrieval tasks across multi-modalities including 2D image, 3D point cloud and mesh data. The proposed framework significantly outperforms the state-of-the-art methods for both cross-modal and in-domain retrieval for 3D objects on the ModelNet10 and ModelNet40 datasets.

关键词： Solid modeling computer vision Three-dimensional displays Computational modeling Metadata Feature extraction pattern recognition

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Jigsaw Clustering for Unsupervised Visual Representation Learning

Jigsaw Clustering for Unsupervised Visual Representation Lea...

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

作者： Chen, Pengguang Liu, Shu Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China SmartMore Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Unsupervised representation learning with contrastive learning achieved great success. This line of methods duplicate each training batch to construct contrastive pairs, making each training batch and its augmented version forwarded simultaneously and leading to additional computation. We propose a new jigsaw clustering pretext task in this paper, which only needs to forward each training batch itself, and reduces the training cost. Our method makes use of information from both intra- and inter-images, and outperforms previous single-batch based ones by a large margin. It is even comparable to the contrastive learning methods when only half of training batches are used. Our method indicates that multiple batches during training are not necessary, and opens the door for future research of single-batch unsupervised methods. Our models trained on ImageNet datasets achieve state-of-the-art results with linear classification, outperforming previous single-batch methods by 2.6%. Models transferred to COCO datasets outperforms MoCo v2 by 0.4% with only half of the training batches. Our pretrained models outperform supervised ImageNet pretrained models on CIFAR-10 and CIFAR-100 datasets by 0.9% and 4.1% respectively.

关键词： Training Learning systems Visualization computer vision Costs pattern recognition Task analysis

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Virtual Fully-Connected Layer: Training a Large-Scale Face recognition Dataset with Limited Computational Resources

Virtual Fully-Connected Layer: Training a Large-Scale Face R...

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

作者： Li, Pengyu Wang, Biao Zhang, Lei Alibaba Grp Artificial Intelligence Ctr DAMO Acad Hangzhou Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Recently, deep face recognition has achieved significant progress because of Convolutional Neural Networks (CNNs) and large-scale datasets. However, training CNNs on a large-scale face recognition dataset with limited computational resources is still a challenge. This is because the classification paradigm needs to train a fully-connected layer as the category classifier, and its parameters will be in the hundreds of millions if the training dataset contains millions of identities. This requires many computational resources, such as GPU memory. The metric learning paradigm is an economical computation method, but its performance is greatly inferior to that of the classification paradigm. To address this challenge, we propose a simple but effective CNN layer called the Virtual fully-connected (Virtual FC) layer to reduce the computational consumption of the classification paradigm. Without bells and whistles, the proposed Virtual FC reduces the parameters by more than 100 times with respect to the fully-connected layer and achieves competitive performance on mainstream face recognition evaluation datasets. Moreover, the performance of our Virtual FC layer on the evaluation datasets is superior to that of the metric learning paradigm by a significant margin. Our code will be released in hopes of disseminating our idea to other domains1.

关键词： Training Measurement computer vision Codes Face recognition Memory management Graphics processing units

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