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检索条件"任意字段=2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005"

共 6545 条记录，以下是221-230 订阅

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The Topology and Language of Relationships in the Visual Genome Dataset

The Topology and Language of Relationships in the Visual Gen...

引用

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

作者： Abou Chacra, David Zelek, John Univ Waterloo Waterloo ON Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The Visual Genome Dataset is the de facto standard dataset used in Scene Graph generation. It contains a large collection of images with corresponding object and relationship labels. We explore the lingual aspect of the relationship predicates and find that very few symmetric/inverse relationships are represented in the dataset(for example, 'above' and 'under'). We believe this is linked to human spatial cognition, and posit that labelling bias stemming from human representations of relationships creates asymmetric relationship labels that span the whole dataset. We also perform a 2D topological analysis of the bounding boxes linked by different relationship predicates. This analysis sheds light on certain classes and their ambiguity wherein more frequent classes are semantically overloaded and therefore quite confusing. Finally we show that when reduced to more lingually and topologically well defined spatial relationships scene graph generation algorithm performance improves tremendously, but scene graph generators are still far from perfect.

关键词： Visualization conferences Genomics Generators Topology pattern recognition Labeling

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Cyclical Pruning for Sparse Neural Networks

Cyclical Pruning for Sparse Neural Networks

引用

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

作者： Srinivas, Suraj Kuzmin, Andrey Nagel, Markus van Baalen, Mart Skliar, Andrii Blankevoort, Tijmen Idiap Res Inst Martigny Switzerland Ecole Polytech Fed Lausanne Lausanne Switzerland Qualcomm AI Res Amsterdam Netherlands

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Current methods for pruning neural network weights iteratively apply magnitude-based pruning on the model weights and re-train the resulting model to recover lost accuracy. In this work, we show that such strategies do not allow for the recovery of erroneously pruned weights. To enable weight recovery, we propose a simple strategy called cyclical pruning which requires the pruning schedule to be periodic and allows for weights pruned erroneously in one cycle to recover in subsequent ones. Experimental results on both linear models and large-scale deep neural networks show that cyclical pruning outperforms existing pruning algorithms, especially at high sparsity ratios. Our approach is easy to tune and can be readily incorporated into existing pruning pipelines to boost performance.

关键词： Deep learning Schedules computer vision conferences Computational modeling Neural networks Pipelines

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Why Object Detectors Fail: Investigating the Influence of the Dataset

Why Object Detectors Fail: Investigating the Influence of th...

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

作者： Miller, Dimity Goode, Georgia Bennie, Callum Moghadam, Peyman Jurdak, Raja Queensland Univ Technol Brisbane Qld Australia CSIRO Data61 Robot & Autonomous Syst Melbourne Vic Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

A false negative in object detection describes an object that was not correctly localised and classified by a detector. In prior work, we introduced five 'false negative mechanisms' that identify the specific component inside the detector architecture that failed to detect the object. Using these mechanisms, we explore how different computer vision datasets and their inherent characteristics can influence object detector failures. Specifically, we investigate the false negative mechanisms of Faster R-CNN and RetinaNet across five computer vision datasets, namely Microsoft COCO, Pascal VOC, ExDark, ObjectNet, and COD10K. Our results show that object size and class influence the false negative mechanisms of object detectors. We also show that comparing the false negative mechanisms of a single object class across different datasets can highlight potentially unknown biases in datasets.

关键词： computer vision conferences Detectors Object detection computer architecture Market research pattern recognition

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Zero-shot Learning Using Multimodal Descriptions

Zero-shot Learning Using Multimodal Descriptions

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

作者： Mall, Utkarsh Hariharan, Bharath Bala, Kavita Cornell Univ Ithaca NY 14853 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Zero-shot learning (ZSL) tackles the problem of recognizing unseen classes using only semantic descriptions, e.g., attributes. Current zero-shot learning techniques all assume that a single vector of attributes suffices to describe each category. We show that this assumption is incorrect. Many classes in real world problems have multiple modes of appearance: male and female birds vary in appearance, for instance. Domain experts know this and can provide attribute descriptions of the chief modes of appearance for each class. Motivated by this, we propose the task of multi-modal zero-shot learning, where the learner must learn from these multimodal attribute descriptions. We present a technique for addressing this problem of multimodal ZSL that outperforms the unimodal counterpart significantly. We posit that multimodal ZSL is more practical for real-world problems where complex intra-class variation is common.

关键词： computer vision conferences Computational modeling Semantics Manuals Benchmark testing Birds

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Conjugate Adder Net (CAddNet) - a Space-Efficient Approximate CNN

Conjugate Adder Net (CAddNet) - a Space-Efficient Approximat...

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

作者： Shen, Lulan Ziaeefard, Maryam Meyer, Brett Gross, Warren Clark, James J. McGill Univ Montreal PQ Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The AdderNet was recently developed as a way to implement deep neural networks without needing multiplication operations to combine weights and inputs. Instead, absolute values of the difference between weights and inputs are used, greatly reducing the gate-level implementation complexity. Training of AdderNets is challenging, however, and the loss curves during training tend to fluctuate significantly. In this paper we propose the Conjugate Adder Network, or CAddNet, which uses the difference between the absolute values of conjugate pairs of inputs and the weights. We show that this can be implemented simply via a single minimum operation, resulting in a roughly 50% reduction in logic gate complexity as compared with AdderNets. The CAddNet method also stabilizes training as compared with AdderNets, yielding training curves similar to standard CNNs.

关键词： Training Deep learning conferences Neural networks Logic gates Complexity theory pattern recognition

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HMIway-env: A Framework for Simulating Behaviors and Preferences to Support Human-AI Teaming in Driving

HMIway-env: A Framework for Simulating Behaviors and Prefere...

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

作者： Gopinath, Deepak DeCastro, Jonathan Rosman, Guy Sumner, Emily Morgan, Allison Hakimi, Shabnam Stent, Simon Toyota Res Inst Los Altos CA 94022 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We introduce a lightweight simulation and modeling framework, HMIway-env, for studying human-machine teaming in the context of driving. The goal of the framework is to accelerate the development of adaptive AI systems which can respond to individual driver states, traits, and preferences, by serving as a data-generation engine and training environment for learning personalized human-AI teaming policies. We extend highway-env, an OpenAI Gym-based simulator environment, to enable specification of human driver behavior, and design of vehicle-driver interactions and outcomes. We describe one instance of our framework incorporating models for distracted and cautious driving, which we validate through crowd-sourced feedback, and show early experimental results toward the training of better intervention policies.

关键词： Training Adaptation models Fatigue Behavioral sciences pattern recognition Artificial intelligence Task analysis

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Fast-n-Squeeze: towards real-time spectral reconstruction from RGB images

Fast-n-Squeeze: towards real-time spectral reconstruction fr...

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

作者： Agarla, Mirko Bianco, Simone Buzzelli, Marco Celona, Luigi Schettini, Raimondo Univ Milano Bicocca Dept Informat Syst & Commun Viale Sarca 336Bldg U14 Milan Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We present an efficient method for the reconstruction of multispectral information from RGB images, as part of the NTIRE 2022 Spectral Reconstruction Challenge. Given an input image, our method determines a global RGB-to-spectral linear transformation matrix, based on a search through optimal matrices from training images that share low-level features with the input. The resulting spectral signatures are then adjusted by a global scaling factor, determined through a lightweight SqueezeNet-inspired neural network. By combining the efficiency of linear transformation matrices with the data-driven effectiveness of convolutional neural networks, we are able to achieve superior performance than winners of the previous editions of the challenge.

关键词： Training Measurement Image resolution Neural networks Real-time systems pattern recognition Convolutional neural networks

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Comparison of CoModGANs, LaMa and GLIDE for Art Inpainting Completing M.C Escher's Print Gallery

Comparison of CoModGANs, LaMa and GLIDE for Art Inpainting C...

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

作者： Cipolina-Kun, Lucia Caenazzo, Simone Mazzei, Gaston ML Collect San Francisco CA 94117 USA Riskcare Ltd London England Univ Paris Saclay CNRS LISN VENISE Team Orsay France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Digital art restoration has benefited from inpainting models to correct the degradation or missing sections of a painting. This work compares three current state-of-the art models for inpainting of large missing regions. We provide qualitative and quantitative comparison of the performance by CoModGANs, LaMa and GLIDE in inpainting of blurry and missing sections of images. We use Escher's incomplete painting Print Gallery as our test study since it presents several of the challenges commonly present in restorative inpainting.

关键词： Degradation Image resolution Computational modeling conferences Digital art Image restoration pattern recognition

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Parallel Generative Adversarial Network for Third-person to First-person Image Generation

Parallel Generative Adversarial Network for Third-person to ...

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

作者： Liu, Gaowen Latapie, Hugo Kilic, Ozkan Lawrence, Adam Cisco Res Emerging Technol & Incubat San Jose CA 95134 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Cross-view image generation has been recently proposed to generate images of one view from another dramatically different view. In this paper, we investigate third-person (exocentric) view to first-person (egocentric) view image generation. This is a challenging task since egocentric view sometimes is remarkably different from exocentric view. Thus, transforming the appearances across the two views is a non-trivial task. To this end, we propose a novel Parallel Generative Adversarial Network (P-GAN) with a novel cross-cycle loss to learn the shared information for generating egocentric images from exocentric view. We also incorporate a novel contextual feature loss in the learning procedure to capture the contextual information in images. Extensive experiments on the Exo-Ego datasets [5] show that our model outperforms the state-of-the-art approaches.

关键词： computer vision Image synthesis conferences Computational modeling Generative adversarial networks pattern recognition Task analysis

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Adaptive Bitrate Quantization Scheme Without Codebook for Learned Image Compression

Adaptive Bitrate Quantization Scheme Without Codebook for Le...

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

作者： Loehdefink, Jonas Sitzmann, Jonas Baer, Andreas Fingscheidt, Tim Tech Univ Carolo Wilhelmina Braunschweig Braunschweig Germany

ISBN: (纸本)9781665487399

We propose a generic approach to quantization without codebook in learned image compression called onehot max (OHM, O) quantization. It reorganizes the feature space resulting in an additional dimension, along which vector quantization yields one-hot vectors by comparing activations. Furthermore, we show how to integrate Omega quantization into a compression system with bitrate adaptation, i.e., full control over bitrate during inference. We perform experiments on both MNIST and Kodak and report on rate-distortion trade-offs comparing with the integer rounding reference. For low bitrates (< 0.4 bpp), our proposed quantizer yields better performance while exhibiting also other advantageous training and inference properties. Code is available at https://***/ifnspaml/OHMQ.

关键词： Training computer vision Image coding Codes Vector quantization conferences Bit rate

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