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

共 22908 条记录，以下是4341-4350 订阅

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TearingNet: Point Cloud Autoencoder to Learn Topology-Friendly Representations

TearingNet: Point Cloud Autoencoder to Learn Topology-Friend...

引用

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

作者： Pang, Jiahao Li, Duanshun Tian, Dong InterDigital Princeton NJ 08540 USA

ISBN: (纸本)9781665445092

Topology matters. Despite the recent success of point cloud processing with geometric deep learning, it remains arduous to capture the complex topologies of point cloud data with a learning model. Given a point cloud dataset containing objects with various genera, or scenes with multiple objects, we propose an autoencoder, TearingNet, which tackles the challenging task of representing the point clouds using a fixed-length descriptor. Unlike existing works directly deforming predefined primitives of genus zero (e.g., a 2D square patch) to an object-level point cloud, our TearingNet is characterized by a proposed Tearing network module and a Folding network module interacting with each other iteratively. Particularly, the Tearing network module learns the point cloud topology explicitly. By breaking the edges of a primitive graph, it tears the graph into patches or with holes to emulate the topology of a target point cloud, leading to faithful reconstructions. Experimentation shows the superiority of our proposal in terms of reconstructing point clouds as well as generating more topology friendly representations than benchmarks.

关键词： Deep learning Surface reconstruction Network topology Shape Topology pattern recognition Proposals

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VirFace: Enhancing Face recognition via Unlabeled Shallow Data

VirFace: Enhancing Face Recognition via Unlabeled Shallow Da...

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

作者： Li, Wenyu Guo, Tianchu Li, Pengyu Chen, Binghui Wang, Biao Zuo, Wangmeng Zhang, Lei Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Recently, how to exploit unlabeled data for training face recognition models has been attracting increasing attention. However, few works consider the unlabeled shallow data(1) in real-world scenarios. The existing semi-supervised face recognition methods that focus on generating pseudo labels or minimizing softmax classification probabilities of the unlabeled data do not work very well on the unlabeled shallow data. It is still a challenge on how to effectively utilize the unlabeled shallow face data to improve the performance of face recognition. In this paper, we propose a novel face recognition method, named VirFace, to effectively exploit the unlabeled shallow data for face recognition. VirFace consists of VirClass and VirInstance. Specifically, VirClass enlarges the inter-class distance by injecting the unlabeled data as new identities, while VirInstance produces virtual instances sampled from the learned distribution of each identity to further enlarge the inter-class distance. To the best of our knowledge, we are the first to tackle the problem of unlabeled shallow face data. Extensive experiments have been conducted on both the small- and large-scale datasets, e.g. LFW and IJB-C, etc, demonstrating the superiority of the proposed method.

关键词： Training computer vision Face recognition Benchmark testing Data models

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In the light of feature distributions: moment matching for Neural Style Transfer

In the light of feature distributions: moment matching for N...

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

作者： Kalischek, Nikolai Wegner, Jan D. Schindler, Konrad Swiss Fed Inst Technol Photogrammetry & Remote Sensing EcoVis Lab Zurich Switzerland

ISBN: (纸本)9781665445092

Style transfer aims to render the content of a given image in the graphical/artistic style of another image. The fundamental concept underlying Neural Style Transfer (NST) is to interpret style as a distribution in the feature space of a Convolutional Neural Network, such that a desired style can be achieved by matching its feature distribution. We show that most current implementations of that concept have important theoretical and practical limitations, as they only partially align the feature distributions. We propose a novel approach that matches the distributions more precisely, thus reproducing the desired style more faithfully, while still being computationally efficient. Specifically, we adapt the dual form of Central Moment Discrepancy (CMD), as recently proposed for domain adaptation, to minimize the difference between the target style and the feature distribution of the output image. The dual interpretation of this metric explicitly matches all higher-order centralized moments and is therefore a natural extension of existing NST methods that only take into account the first and second moments. Our experiments confirm that the strong theoretical properties also translate to visually better style transfer, and better disentangle style from semantic image content.

关键词： Measurement computer vision Semantics Computational efficiency Convolutional neural networks pattern matching

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Towards Evaluating Explanations of vision Transformers for Medical Imaging

Towards Evaluating Explanations of Vision Transformers for M...

引用

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

作者： Piotr Komorowski Hubert Baniecki Przemysław Biecek University of Warsaw Warsaw University of Technology

As deep learning models increasingly find applications in critical domains such as medical imaging, the need for transparent and trustworthy decision-making becomes paramount. Many explainability methods provide insights into how these models make predictions by attributing importance to input features. As vision Transformer (ViT) becomes a promising alternative to convolutional neural networks for image classification, its interpretability remains an open research question. This paper investigates the performance of various interpretation methods on a ViT applied to classify chest X-ray images. We introduce the notion of evaluating faithfulness, sensitivity, and complexity of ViT explanations. The obtained results indicate that Layerwise relevance propagation for transformers outperforms Local interpretable model-agnostic explanations and Attention visualization, providing a more accurate and reliable representation of what a ViT has actually learned. Our findings provide insights into the applicability of ViT explanations in medical imaging and highlight the importance of using appropriate evaluation criteria for comparing them.

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Prompt Learning with One-Shot Setting based Feature Space Analysis in vision-and-Language Models

Prompt Learning with One-Shot Setting based Feature Space An...

引用

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

作者： Yuki Hirohashi Tsubasa Hirakawa Takayoshi Yamashita Hironobu Fujiyoshi OMRON Corp. Chubu University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

By using few-shot data and labels, prompt learning obtains optimal prompts that are capable of achieving high performance on downstream tasks. Existing prompt learning methods generate high-quality prompts that are suitable for downstream tasks but tend to perform poorly in scenarios where only very limited data (e.g., one-shot) is available. We address on this challenging one-shot scenario and propose a novel architecture for prompt learning, called Image-Text Feature Alignment Branch (ITFAB). ITFAB aligns text features closer to the centroids of image features and separates text features with different classes to resolve misalignment in the feature space, thereby facilitating the acquisition of high-quality prompts with very limited data. In one-shot setting, our method outperforms the existing CoOp and CoCoOp methods and in some cases even surpasses CoCoOp’s 16-shot performance. Testing on different datasets and domain, show that ITFAB almost matches CoCoOp’s effectiveness. It also works with current prompt learning methods like MapLe and PromptSRC, improving their performance in one-shot setting.

关键词： Learning systems computer vision Analytical models Adaptation models Image resolution Accuracy conferences

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Variational Prototype Learning for Deep Face recognition

Variational Prototype Learning for Deep Face Recognition

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

作者： Deng, Jiankang Guo, Jia Yang, Jing Lattas, Alexandros Zafeiriou, Stefanos Huawei Shenzhen Peoples R China Imperial Coll London England InsightFace London England Univ Nottingham Nottingham England

ISBN: (纸本)9781665445092

Deep face recognition has achieved remarkable improvements due to the introduction of margin-based soft-max loss, in which the prototype stored in the last linear layer represents the center of each class. In these methods, training samples are enforced to be close to positive prototypes and far apart from negative prototypes by a clear margin. However, we argue that prototype learning only employs sample-to-prototype comparisons without considering sample-to-sample comparisons during training and the low loss value gives us an illusion of perfect feature embedding, impeding the further exploration of SGD. To this end, we propose Variational Prototype Learning (VPL), which represents every class as a distribution instead of a point in the latent space. By identifying the slow feature drift phenomenon, we directly inject memorized features into prototypes to approximate variational prototype sampling. The proposed VPL can simulate sample-to-sample comparisons within the classification framework, encouraging the SGD solver to be more exploratory, while boosting performance. Moreover, VPL is conceptually simple, easy to implement, computationally efficient and memory saving. We present extensive experimental results on popular benchmarks, which demonstrate the superiority of the proposed VPL method over the state-of-the-art competitors.

关键词： Training Learning systems computer vision Face recognition Memory management Prototypes Benchmark testing

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Tracking and Counting Apples in Orchards Under Intermittent Occlusions and Low Frame Rates

Tracking and Counting Apples in Orchards Under Intermittent ...

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

作者： Gonçalo P. Matos Carlos Santiago João P. Costeira Ricardo L. Saldanha Ernesto M. Morgado SISCOG - Sistemas Congitivos SA Lisbon Portugal Institute for Systems and Robotics (ISR/IST) LARSyS Instituto Superior Técnico Univ. of Lisbon Portugal

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Estimating what will be the fruit yield in an orchard helps farmers to better plan the resources needed for harvesting, storing, and commercialising the crop, and also to take some agricultural decisions (like pruning) that may increase the quality of the yield and increase profits. Therefore, over the last years, several methods based on computer vision were proposed to automate this task, by directly counting the fruits on trees using a video camera. However, existing works and methods usually assume ideal conditions, and may fail under more challenging scenarios with unconstrained camera motion and intermittent occlusions of fruits. Here we show that combining Structure-from-Motion (SfM) with a bipartite graph matching has the potential to address those challenges. We found that our approach applied to real-world datasets, with unconstrained camera motion and low frame rates, outperforms existing methods by a large margin. Our results demonstrate that the proposed method is robust to multiple intermittent occlusions under challenging conditions, and thus suitable to be used in diverse real-world scenarios in orchards, either with a camera operated by hand or mounted on an agricultural vehicle. Although not shown here, we believe that the proposed method can also be applied to other object tracking problems besides counting fruits, under similar settings — i.e. static objects and a freely moving camera.

关键词： computer vision Visualization conferences Crops Cameras Trajectory pattern recognition

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Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation

Bipartite Graph Network with Adaptive Message Passing for Un...

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

作者： Li, Rongjie Zhang, Songyang Wan, Bo He, Xuming ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Katholieke Univ Leuven Dept Elect Engn ESAT Leuven Belgium

ISBN: (纸本)9781665445092

Scene graph generation is an important visual understanding task with a broad range of vision applications. Despite recent tremendous progress, it remains challenging due to the intrinsic long-tailed class distribution and large intra-class variation. To address these issues, we introduce a novel confidence-aware bipartite graph neural network with adaptive message propagation mechanism for unbiased scene graph generation. In addition, we propose an efficient bi-level data resampling strategy to alleviate the imbalanced data distribution problem in training our graph network. Our approach achieves superior or competitive performance over previous methods on several challenging datasets, including Visual Genome, Open Images V4/V6, demonstrating its effectiveness and generality.

关键词： Training Visualization computer vision Adaptive systems Message passing Neural networks Genomics

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A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classification

A Realistic Evaluation of Semi-Supervised Learning for Fine-...

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

作者： Su, Jong-Chyi Cheng, Zezhou Maji, Subhransu Univ Massachusetts Amherst Amherst MA 01003 USA

ISBN: (纸本)9781665445092

We evaluate the effectiveness of semi-supervised learning (SSL) on a realistic benchmark where data exhibits considerable class imbalance and contains images from novel classes. Our benchmark consists of two fine-grained classification datasets obtained by sampling classes from the Aves and Fungi taxonomy. We find that recently proposed SSL methods provide significant benefits, and can effectively use out-of-class data to improve performance when deep networks are trained from scratch. Yet their performance pales in comparison to a transfer learning baseline, an alternative approach for learning from a few examples. Furthermore, in the transfer setting, while existing SSL methods provide improvements, the presence of out-of-class is often detrimental. In this setting, standard fine-tuning followed by distillation-based self-training is the most robust. Our work suggests that semi-supervised learning with experts on realistic datasets may require different strategies than those currently prevalent in the literature.

关键词： Fungi Technological innovation Transfer learning Taxonomy Benchmark testing Semisupervised learning pattern recognition

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Deep Gradient Projection Networks for Pan-sharpening

Deep Gradient Projection Networks for Pan-sharpening

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

作者： Xu, Shuang Zhang, Jiangshe Zhao, Zixiang Sun, Kai Liu, Junmin Zhang, Chunxia Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China

ISBN: (纸本)9781665445092

Pan-sharpening is an important technique for remote sensing imaging systems to obtain high resolution multispectral images. Recently, deep learning has become the most popular tool for pan-sharpening. This paper develops a model-based deep pan-sharpening approach. Specifically, two optimization problems regularized by the deep prior are formulated, and they are separately responsible for the generative models for panchromatic images and low resolution multispectral images. Then, the two problems are solved by a gradient projection algorithm, and the iterative steps are generalized into two network blocks. By alternatively stacking the two blocks, a novel network, called gradient projection based pan-sharpening neural network, is constructed. The experimental results on different kinds of satellite datasets demonstrate that the new network outperforms state-of-the-art methods both visually and quantitatively.

关键词： Image resolution Satellites Stacking Neural networks Tools Iterative algorithms pattern recognition

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