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检索条件"任意字段=2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022"

共 3917 条记录，以下是3581-3590 订阅

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Deep Low-Rank Subspace Clustering

Deep Low-Rank Subspace Clustering

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Mohsen Kheirandishfard Fariba Zohrizadeh Farhad Kamangar Department of Computer Science and Engineering University of Texas Arlington USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

This paper is concerned with developing a novel approach to tackle the problem of subspace clustering. The approach introduces a convolutional autoencoder-based architecture to generate low-rank representations (LRR) of input data which are proven to be very suitable for subspace clustering. We propose to insert a fully-connected linear layer and its transpose between the encoder and decoder to implicitly impose a rank constraint on the learned representations. We train this architecture by minimizing a standard deep subspace clustering loss function and then recover underlying subspaces by applying a variant of spectral clustering technique. Extensive experiments on benchmark datasets demonstrate that the proposed model can not only achieve very competitive clustering results using a relatively small network architecture but also can maintain its high level of performance across a wide range of LRRs. This implies that the model can be appropriately combined with the state-of-the-art subspace clustering architectures to produce more accurate results.

关键词： Decoding Clustering algorithms computer architecture Data models Kernel computer vision Partitioning algorithms

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Photosequencing of Motion Blur using Short and Long Exposures

Photosequencing of Motion Blur using Short and Long Exposure...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Vijay Rengarajan Shuo Zhao Ruiwen Zhen John Glotzbach Hamid Sheikh Aswin C. Sankaranarayanan Carnegie Mellon University Samsung Research America

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Photosequencing aims to transform a motion blurred image to a sequence of sharp images. This problem is challenging due to the inherent ambiguities in temporal ordering as well as the recovery of lost spatial textures due to blur. Adopting a computational photography approach, we propose to capture two short exposure images, along with the original blurred long exposure image to aid in the aforementioned challenges. Post-capture, we recover the sharp photosequence using a novel blur decomposition strategy that recursively splits the long exposure image into smaller exposure intervals. We validate the approach by capturing a variety of scenes with interesting motions using machine vision cameras programmed to capture short and long exposure sequences. Our experimental results show that the proposed method resolves both fast and fine motions better than prior works.

关键词： Cameras Noise measurement Training Neural networks Sequential analysis Image reconstruction Machine vision

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Match or No Match: Keypoint Filtering based on Matching Probability

Match or No Match: Keypoint Filtering based on Matching Prob...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Alexandra I. Papadaki Ronny Hänsch Computer Vision & Remote Sensing Department Technical University Berlin Department SAR Technology German Aerospace Center (DLR)

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Keypoints that do not meet the needs of a given application are a very common accuracy and efficiency bottleneck in many computer vision tasks, including keypoint matching and 3D reconstruction. Many computer vision and machine learning methods have dealt with this issue, trying to improve keypoint detection or the matching process. We introduce an algorithm that filters detected keypoints before the matching is even attempted, by predicting the probability of each point to be successfully matched. This is realised using a flexible and time efficient Random Forest classifier. Experiments on stereo and multi-view datasets of building facades show that the proposed method decreases the computational cost of a subsequent keypoint matching and 3D reconstruction, by correctly filtering 50% of the points that wouldn't be matched while preserving 73% of the matchable keypoints. This enables a subsequent processing with minimal mismatches, provides reliable matches, and point clouds. The presented filtering leads to an improved 3D reconstruction of the scene, even in the hard case of repetitive patterns and vegetation.

关键词： Radio frequency Detectors Three-dimensional displays Vegetation mapping Task analysis computer vision Reliability

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Noise-based Selection of Robust Inherited Model for Accurate Continual Learning

Noise-based Selection of Robust Inherited Model for Accurate...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Xiaocong Du Zheng Li Jae-sun Seo Frank Liu Yu Cao Arizona State University Oak Ridge National Lab

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

There is a growing demand for an intelligent system to continually learn knowledge from a data stream. Continual learning requires both the preservation of previous knowledge (i.e., avoiding catastrophic forgetting) and the acquisition of new knowledge. Different from previous works that focus only on model adaptation (e.g., regularization, network expansion, memory rehearsal, etc.), we propose a novel training scheme named acquisitive learning (AL), which emphasizes both the knowledge inheritance and knowledge acquisition. AL starts from an elaborately selected model with pre-trained knowledge (the inherited model) and then adapts it to new data using segmented training. The selection is achieved by injecting random noise to various inherited models for better model robustness, which promises higher accuracy in further knowledge acquisition. The approach is validated by the visualization of the loss landscape and quantitative roughness measurement. The combination of the selective inherited model and knowledge acquisition reduces catastrophic forgetting by 10X on the CIFAR-100 dataset.

关键词： conferences computer vision pattern recognition

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Self-supervised Object Motion and Depth Estimation from Video

Self-supervised Object Motion and Depth Estimation from Vide...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Qi Dai Vaishakh Patii Simon Hecker Dengxin Dai Luc Van Gool Konrad Schindler Computer Vision Lab ETH Zurich

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

We present a self-supervised learning framework to estimate the individual object motion and monocular depth from video. We model the object motion as a 6 degree-of- freedom rigid-body transformation. The instance segmentation mask is leveraged to introduce the information of object. Compared with methods which predict dense optical flow map to model the motion, our approach significantly reduces the number of values to be estimated. Our system eliminates the scale ambiguity of motion prediction through imposing a novel geometric constraint loss term. Experiments on KITTI driving dataset demonstrate our system is capable to capture the object motion without external annotation. Our system outperforms previous self-supervised approaches in terms of 3D scene flow prediction, and contribute to the disparity prediction in dynamic area.

关键词： Cameras Estimation Dynamics Three-dimensional displays Motion segmentation Optical imaging Transforms

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Decomposing Image Generation into Layout Prediction and Conditional Synthesis

Decomposing Image Generation into Layout Prediction and Cond...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Anna Volokitin Ender Konukoglu Luc Van Gool Computer Vision Laboratory ETH Zürich

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Learning the distribution of multi-object scenes with Generative Adversarial Networks (GAN) is challenging. Guiding the learning using semantic intermediate representations, which are less complex than images, can be a solution. In this article, we investigate splitting the optimisation of generative adversarial networks into two parts, by first generating a semantic segmentation mask from noise and then translating that segmentation mask into an image. We performed experiments using images from the CityScapes dataset and compared our approach to Progressive Growing of GANs (PGGAN), which uses multiscale growing of networks to guide the learning. Using the lens of a segmentation algorithm to examine the structure of generated images, we find that our method achieves higher structural consistency in latent space interpolations and yields generations with better differentiation between distinct objects, while achieving the same image quality as PGGAN as judged by a user study and a standard GAN evaluation metric.

关键词： Image segmentation Gallium nitride Generators Image generation Generative adversarial networks Semantics Training

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Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation

Multi-view Self-Constructing Graph Convolutional Networks wi...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Qinghui Liu Michael Kampffmeyer Robert Jenssen Arnt-Børre Salberg Norwegian Computing Center Oslo Norway UiT Machine Learning Group UiT the Arctic University of Norway Tromsø Norway

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

We propose a novel architecture called the Multi-view Self-Constructing Graph Convolutional Networks (MSCG- Net) for semantic segmentation. Building on the recently proposed Self-Constructing Graph (SCG) module, which makes use of learnable latent variables to self-construct the underlying graphs directly from the input features without relying on manually built prior knowledge graphs, we leverage multiple views in order to explicitly exploit the rotational invariance in airborne images. We further develop an adaptive class weighting loss to address the class imbalance. We demonstrate the effectiveness and flexibility of the proposed method on the Agriculture-vision challenge dataset 1 and our model achieves very competitive results (0.547 mIoU) 2 with much fewer parameters and at a lower computational cost compared to related pure-CNN based work.

关键词： Semantics Training computer architecture Task analysis Two dimensional displays Atmospheric modeling Adaptation models

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Fine grained pointing recognition for natural drone guidance

Fine grained pointing recognition for natural drone guidance

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： O. L. Barbed P. Azagra L. Teixeira M. Chli J. Civera A. C. Murillo DIIS-i3A University of Zaragoza Spain Vision for Robotics Lab ETH Zurich Switzerland

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Human action recognition systems are typically focused on identifying different actions, rather than fine grained variations of the same action. This work explores strategies to identify different pointing directions in order to build a natural interaction system to guide autonomous systems such as drones. Commanding a drone with hand-held panels or tablets is common practice but intuitive user-drone interfaces might have significant benefits. The system proposed in this work just requires the user to provide occasional high-level navigation commands by pointing the drone towards the desired motion direction. Due to the lack of data on these settings, we present a new benchmarking video dataset to validate our framework and facilitate future research on the area. Our results show good accuracy for pointing direction recognition, while running at interactive rates and exhibiting robustness to variability in user appearance, viewpoint, camera distance and scenery.

关键词： Drones Robots Image segmentation Skeleton Cameras Elbow Wrist

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FoNet: A Memory-efficient Fourier-based Orthogonal Network for Object recognition

FoNet: A Memory-efficient Fourier-based Orthogonal Network f...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Feng Wei Uyen Trang Nguyen Hui Jiang Department of Electrical Engineering and Computer Science York University Toronto Canada

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

The memory consumption of most Convolutional Neural Network (CNN) architectures grows rapidly with the increasing depth of the network, which is a major constraint for efficient network training and inference on modern GPUs with limited memory. Several studies show that the feature maps (as generated after the convolutional layers) are the big bottleneck in this memory problem. Often, these feature maps mimic natural photographs in the sense that their energy is concentrated in the spectral domain. In this paper, we propose a Fourier-based Orthogonal Network (FoNet) that incorporates orthogonal representations and performs both the convolution and the activation operations in the spectral domain to achieve memory reduction. The performance of our FoNet is evaluated on four standard object recognition benchmarks (i.e., MNIST, CIFAR10, SVHN, and ImageNet), and compared with four state-of-the-art implementations (i.e., LeNet, AlexNet, VGG, and DenseNet). Encouragingly, FoNet is able to reduce memory consumption by about 60% without significant loss of performance for all tested network architectures.

关键词： Spectral analysis Memory management Kernel Training Convolution Computational modeling

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DeepFake Detection by Analyzing Convolutional Traces

DeepFake Detection by Analyzing Convolutional Traces

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Luca Guarnera Oliver Giudice Sebastiano Battiato iCTLab University of Catania Catania Italy

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

The Deepfake phenomenon has become very popular nowadays thanks to the possibility to create incredibly realistic images using deep learning tools, based mainly on ad-hoc Generative Adversarial Networks (GAN). In this work we focus on the analysis of Deepfakes of human faces with the objective of creating a new detection method able to detect a forensics trace hidden in images: a sort of fingerprint left in the image generation process. The proposed technique, by means of an Expectation Maximization (EM) algorithm, extracts a set of local features specifically addressed to model the underlying convolutional generative process. Ad-hoc validation has been employed through experimental tests with naive classifiers on five different architectures (GDWCT, STARGAN, ATTGAN, STYLEGAN, STYLEGAN2) against the CELEBA dataset as ground-truth for non-fakes. Results demonstrated the effectiveness of the technique in distinguishing the different architectures and the corresponding generation process.

关键词： Gallium nitride Generative adversarial networks Videos computer architecture Data models Convolution Feature extraction

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