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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

共 3320 条记录，以下是181-190 订阅

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Yoga-82: A New Dataset for Fine-grained Classification of Human Poses

Yoga-82: A New Dataset for Fine-grained Classification of Hu...

引用

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

作者： Verma, Manisha Kumawat, Sudhakar Nakashima, Yuta Raman, Shanmuganathan Osaka Univ Osaka Japan Indian Inst Technol Gandhinagar Gandhinagar India

ISBN: (纸本)9781728193601

Human pose estimation is a well-known problem in computer vision to locate joint positions. Existing datasets for learning of poses are observed to be not challenging enough in terms of pose diversity, object occlusion and view points. This makes the pose annotation process relatively simple and restricts the application of the models that have been trained on them. To handle more variety in human poses, we propose the concept of fine-grained hierarchical pose classification, in which we formulate the pose estimation as a classification task, and propose a dataset, Yoga-82, for large-scale yoga pose recognition with 82 classes. Yoga-82 consists of complex poses where fine annotations may not be possible. To resolve this, we provide hierarchical labels for yoga poses based on the body configuration of the pose. The dataset contains a three-level hierarchy including body positions, variations in body positions, and the actual pose names. We present the classification accuracy of the state-of-the-art convolutional neural network architectures on Yoga-82. We also present several hierarchical variants of DenseNet in order to utilize the hierarchical labels.

关键词： computer vision

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Towards CNN map representation and compression for camera relocalisation 31

Towards CNN map representation and compression for camera re...

引用

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

作者： Contreras, Luis Mayol-Cuevas, Walterio Univ Bristol Dept Comp Sci Bristol Avon England

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

This paper presents a study on the use of Convolutional Neural Networks for camera relocalisation and its application to map compression. We follow state of the art visual relocalisation results and evaluate the response to different data inputs. We use a CNN map representation and introduce the notion of map compression under this paradigm by using smaller CNN architectures without sacrificing relocalisation performance. We evaluate this approach in a series of publicly available datasets over a number of CNN architectures with different sizes, both in complexity and number of layers. This formulation allows us to improve relocalisation accuracy by increasing the number of training trajectories while maintaining a constant-size CNN.

关键词： Training Task analysis Cameras computer architecture Trajectory Biological neural networks

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3D Room Layout Recovery Generalizing across Manhattan and Non-Manhattan Worlds

3D Room Layout Recovery Generalizing across Manhattan and No...

引用

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

作者： Jia, Haijing Yi, Hong Fujiki, Hirochika Zhang, Hengzhi Wang, Wei Odamaki, Makoto Ricoh Software Res Ctr Beijing Co Ltd Beijing Peoples R China Ricoh Co Ltd Tokyo Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent 3D room layout recovery approaches mostly concentrate on Manhattan layouts, where the vertical walls are orthogonal with respect to each other, even though there are many rooms with non-Manhattan layouts in the real world. This paper presents a room layout recovery method generalizing across Manhattan and non-Manhattan worlds. Without introducing additional supervision, we extend current Manhattan layout recovery methods by predicting an extra surface normal feature, which is further used for an adaptive post-processing to reconstruct layouts of arbitrary shapes. Experimental results show that our method has a great improvement on non-Manhattan layouts while being capable of generalizing across Manhattan and non-Manhattan layouts.

关键词： Surface reconstruction computer vision Three-dimensional displays Shape conferences Layout Transformers

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CorrGAN: Input Transformation Technique Against Natural Corruptions

CorrGAN: Input Transformation Technique Against Natural Corr...

引用

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

作者： Haque, Mirazul Budnik, Christof J. Yang, Wei UT Dallas Richardson TX 75080 USA Siemens Corp Technol Princeton NJ USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Because of the increasing accuracy of Deep Neural Networks (DNNs) on different tasks, a lot of real times systems are utilizing DNNs. These DNNs are vulnerable to adversarial perturbations and corruptions. Specifically, natural corruptions like fog, blur, contrast etc can affect the prediction of DNN in an autonomous vehicle. In real time, these corruptions are needed to be detected and also the corrupted inputs are needed to be de-noised to be predicted correctly. In this work, we propose CorrGAN approach, which can generate benign input when a corrupted input is provided. In this framework, we train Generative Adversarial Network (GAN) with novel intermediate output-based loss function. The GAN can denoise the corrupted input and generate benign input. Through experimentation, we show that up to 75.2% of the corrupted misclassified inputs can be classified correctly by DNN using CorrGAN.

关键词： Deep learning computer vision Perturbation methods conferences Neural networks computer architecture Generative adversarial networks

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Revisiting the Receptive Field of Conv-GRU in DROID-SLAM

Revisiting the Receptive Field of Conv-GRU in DROID-SLAM

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

作者： Bangunharcana, Antyanta Kim, Soohyun Kim, Kyung-Soo Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This work focuses on improving the Conv-GRU-based optical flow update within a DROID-SLAM framework. Prior optical flow models typically follow a UNet or coarse-to-fine architecture in order to extract long-range cross-correlation and context cues. This helps flow estimation in the presence of large motion and challenging image regions, e.g., textureless regions. We propose modifications to the Conv-GRU module which follows the rationale of these prior models by integrating (Atrous) Spatial Pyramid Pooling and global self-attention into the Conv-GRU block. By enlarging the receptive field through the aforementioned modifications, the model is able to integrate information from a larger context window, thus improving the robustness even when given inputs that comprise challenging image regions. We show empirically through extensive experiments the gain in accuracy through these modifications.

关键词： computer vision Simultaneous localization and mapping conferences Computational modeling Estimation computer architecture Robustness

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Improved Noise2Noise Denoising with Limited Data

Improved Noise2Noise Denoising with Limited Data

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

作者： Calvarons, Adria Font Tech Univ Munich Munich Germany

ISBN: (纸本)9781665448994

Deep learning methods have proven to be very effective for the task of image denoising even when clean reference images are not available. In particular, Noise2Noise, which requires pairs of noisy images during the training phase, has been shown to yield results as good as approaches using pairs of noisy and clean images (Noise2Clean). However, the performance of Noise2Noise drops when the amount of training data is reduced, limiting its capability in practical scenarios. In this work, an analysis of the Noise2Noise learning strategy is done using real noise and synthetic datasets. This paper demonstrates, using diverse network architectures and loss functions, that the duplicity of information in the noisy pairs can be exploited to reach increased denoising performance of Noise2Noise. Additionally, the issue of overfitting in Noise2Noise is analyzed, given its relevance when training with limited data, and an interpretable early termination criterion is proposed.

关键词： Training Limiting Image color analysis Noise reduction Training data Network architecture pattern recognition

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Lacunarity Pooling Layers for Plant Image Classification using Texture Analysis

Lacunarity Pooling Layers for Plant Image Classification usi...

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

作者： Mohan, Akshatha Peeples, Joshua Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77840 USA

ISBN: (纸本)9798350365474

Pooling layers (e.g., max and average) may overlook important information encoded in the spatial arrangement of pixel intensity and/or feature values. We propose a novel lacunarity pooling layer that aims to capture the spatial heterogeneity of the feature maps by evaluating the variability within local windows. The layer operates at multiple scales, allowing the network to adaptively learn hierarchical features. The lacunarity pooling layer can be seamlessly integrated into any artificial neural network architecture. Experimental results demonstrate the layer's effectiveness in capturing intricate spatial patterns, leading to improved feature extraction capabilities. The proposed approach holds promise in various domains, especially in agricultural image analysis tasks. This work contributes to the evolving landscape of artificial neural network architectures by introducing a novel pooling layer that enriches the representation of spatial features. Our code is publicly available. (1)

关键词： computer vision Image Classification Machine Learning Texture Analysis

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An Adversarial Approach for Explaining the Predictions of Deep Neural Networks

An Adversarial Approach for Explaining the Predictions of De...

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

作者： Rahnama, Arash Tseng, Andrew Modzy Vienna VA 22182 USA

ISBN: (纸本)9781665448994

Machine learning models have been successfully applied to a wide range of applications including computer vision, natural language processing, and speech recognition. A successful implementation of these models however, usually relies on deep neural networks (DNNs) which are treated as opaque black-box systems due to their incomprehensible complexity and intricate internal mechanism. In this work, we present a novel algorithm for explaining the predictions of a DNN using adversarial machine learning. Our approach identifies the relative importance of input features in relation to the predictions based on the behavior of an adversarial attack on the DNN. Our algorithm has the advantage of being fast, consistent, and easy to implement and interpret. We present our detailed analysis that demonstrates how the behavior of an adversarial attack, given a DNN and a task, stays consistent for any input test data point proving the generality of our approach. Our analysis enables us to produce consistent and efficient explanations. We illustrate the effectiveness of our approach by conducting experiments using a variety of DNNs, tasks, and datasets. Finally, we compare our work with other well-known techniques in the current literature.

关键词： Deep learning computer vision Machine learning algorithms Computational modeling conferences Speech recognition Prediction algorithms

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Dealing with Missing Modalities in the Visual Question Answer-Difference Prediction Task through Knowledge Distillation

Dealing with Missing Modalities in the Visual Question Answe...

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

作者： Cho, Jae Won Kim, Dong-Jin Choi, Jinsoo Jung, Yunjae Kweon, In So Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665448994

In this work, we address the issues of the missing modalities that have arisen from the Visual Question Answer-Difference prediction task and find a novel method to solve the task at hand. We address the missing modality-the ground truth answers-that are not present at test time and use a privileged knowledge distillation scheme to deal with the issue of the missing modality. In order to efficiently do so, we first introduce a model, the "Big" Teacher, that takes the image/question/answer triplet as its input and outperforms the baseline, then use a combination of models to distill knowledge to a target network (student) that only takes the image/question pair as its inputs. We experiment our models on the VizWiz and VQA-V2 Answer Difference datasets and show through extensive experimentation and ablation the performance of our method and a diverse possibility for future research.

关键词： Visualization computer vision conferences Knowledge discovery pattern recognition Task analysis Bars

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X-MAN: Explaining multiple sources of anomalies in video

X-MAN: Explaining multiple sources of anomalies in video

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

作者： Szymanowicz, Stanislaw Charles, James Cipolla, Roberto Univ Cambridge Cambridge England

ISBN: (纸本)9781665448994

Our objective is to detect anomalies in video while also automatically explaining the reason behind the detector's response. In a practical sense, explainability is crucial for this task as the required response to an anomaly depends on its nature and severity. However, most leading methods (based on deep neural networks) are not interpretable and hide the decision making process in uninterpretable feature representations. In an effort to tackle this problem we make the following contributions: (1) we show how to build interpretable feature representations suitable for detecting anomalies with state of the art performance, (2) we propose an interpretable probabilistic anomaly detector which can describe the reason behind it's response using high level concepts, (3) we are the first to directly consider object interactions for anomaly detection and (4) we propose a new task of explaining anomalies and release a large dataset for evaluating methods on this task. Our method competes well with the state of the art on public datasets while also providing anomaly explanation based on objects and their interactions.

关键词： Training Deep learning Decision making Detectors Probabilistic logic Feature extraction pattern recognition

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