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共 11423 条记录，以下是271-280 订阅

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Automatic Correction of Internal Units in Generative Neural Networks

Automatic Correction of Internal Units in Generative Neural ...

引用

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

作者： Tousi, Ali Jeong, Haedong Han, Jiyeon Choi, Hwanil Choi, Jaesik Korea Adv Inst Sci & Technol KAIST Daejeon South Korea Ulsan Natl Inst Sci & Technol UNIST Ulsan South Korea INEEJI Seoul South Korea

ISBN: (纸本)9781665445092

Generative Adversarial Networks (GANs) have shown satisfactory performance in synthetic image generation by devising complex network structure and adversarial training scheme. Even though GANs are able to synthesize realistic images, there exists a number of generated images with defective visual patterns which are known as artifacts. While most of the recent work tries to fix artifact generations by perturbing latent code, few investigate internal units of a generator to fix them. In this work, we devise a method that automatically identifies the internal units generating various types of artifact images. We further propose the sequential correction algorithm which adjusts the generation flow by modifying the detected artifact units to improve the quality of generation while preserving the original outline. Our method outperforms the baseline method in terms of FID-score and shows satisfactory results with human evaluation.

关键词： Training Visualization computer vision Codes Image synthesis Neural networks Complex networks

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GLAVNet: Global-Local Audio-Visual Cues for Fine-Grained Material recognition

GLAVNet: Global-Local Audio-Visual Cues for Fine-Grained Mat...

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

作者： Shi, Fengmin Guo, Jie Zhang, Haonan Yang, Shan Wang, Xiying Guo, Yanwen Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China IQIYI Intelligence Chongqing Heilongjiang Peoples R China

ISBN: (纸本)9781665445092

In this paper, we aim to recognize materials with combined use of auditory and visual perception. To this end, we construct a new dataset named GLAudio that consists of both the geometry of the object being struck and the sound captured from either modal sound synthesis (for virtual objects) or real measurements (for real objects). Besides global geometries, our dataset also takes local geometries around different hitpoints into consideration. This local information is less explored in existing datasets. We demonstrate that local geometry has a greater impact on the sound than the global geometry and offers more cues in material recognition. To extract features from different modalities and perform proper fusion, we propose a new deep neural network GLAVNet that comprises multiple branches and a well-designed fusion module. Once trained on GLAudio, our GLAVNet provides state-of-the-art performance on material identification and supports fine-grained material categorization.

关键词： Geometry Deep learning Visualization computer vision Feature extraction pattern recognition Task analysis

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Robust Audio-Visual Instance Discrimination

Robust Audio-Visual Instance Discrimination

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

作者： Morgado, Pedro Misra, Ishan Vasconcelos, Nuno Univ Calif San Diego San Diego CA 92093 USA Facebook AI Res Menlo Pk CA USA

ISBN: (纸本)9781665445092

We present a self-supervised learning method to learn audio and video representations. Prior work uses the natural correspondence between audio and video to define a standard cross-modal instance discrimination task, where a model is trained to match representations from the two modalities. However, the standard approach introduces two sources of training noise. First, audio-visual correspondences often produce faulty positives since the audio and video signals can be uninformative of each other. To limit the detrimental impact of faulty positives, we optimize a weighted contrastive learning loss, which down-weighs their contribution to the overall loss. Second, since selfsupervised contrastive learning relies on random sampling of negative instances, instances that are semantically similar to the base instance can be used as faulty negatives. To alleviate the impact of faulty negatives, we propose to optimize an instance discrimination loss with a soft target distribution that estimates relationships between instances. We validate our contributions through extensive experiments on action recognition tasks and show that they address the problems of audio-visual instance discrimination and improve transfer learning performance.

关键词： Training Learning systems computer vision Transfer learning pattern recognition Task analysis Standards

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StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation

StyleSpace Analysis: Disentangled Controls for StyleGAN Imag...

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

作者： Wu, Zongze Lischinski, Dani Shechtman, Eli Hebrew Univ Jerusalem Jerusalem Israel Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

We explore and analyze the latent style space of StyleGAN2, a state-of-the-art architecture for image generation, using models pretrained on several different datasets. We first show that StyleSpace, the space of channel-wise style parameters, is significantly more disentangled than the other intermediate latent spaces explored by previous works. Next, we describe a method for discovering a large collection of style channels, each of which is shown to control a distinct visual attribute in a highly localized and disentangled manner. Third, we propose a simple method for identifying style channels that control a specific attribute, using a pretrained classifier or a small number of example images. Manipulation of visual attributes via these StyleSpace controls is shown to be better disentangled than via those proposed in previous works. To show this, we make use of a newly proposed Attribute Dependency metric. Finally, we demonstrate the applicability of StyleSpace controls to the manipulation of real images. Our findings pave the way to semantically meaningful and well-disentangled image manipulations via simple and intuitive interfaces.

关键词： Measurement Visualization computer vision Analytical models Image synthesis computer architecture Aerospace electronics

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A Conservative Approach for Unbiased Learning on Unknown Biases

A Conservative Approach for Unbiased Learning on Unknown Bia...

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

作者： Jeon, Myeongho Kim, Daekyung Lee, Woochul Kang, Myungjoo Lee, Joonseok Seoul Natl Univ Seoul South Korea Monitor Corp Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Although convolutional neural networks (CNNs) achieve state-of-the-art in image classification, recent works address their unreliable predictions due to their excessive dependence on biased training data. Existing unbiased modeling postulates that the bias in the dataset is obvious to know, but it is actually unsuited for image datasets including countless sensory attributes. To mitigate this issue, we present a new scenario that does not necessitate a predefined bias. Under the observation that CNNs do have multi-variant and unbiased representations in the model, we propose a conservative framework that employs this internal information for unbiased learning. Specifically, this mechanism is implemented via hierarchical features captured along the multiple layers and orthogonal regularization. Extensive evaluations on public benchmarks demonstrate our method is effective for unbiased learning.

关键词： Representation learning Deep learning Machine vision Training data Distributed databases Data models Robustness

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Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation

Differentiable SLAM-net: Learning Particle SLAM for Visual N...

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

作者： Karkus, Peter Cai, Shaojun Hsu, David Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781665445092

Simultaneous localization and mapping (SLAM) remains challenging for a number of downstream applications, such as visual robot navigation, because of rapid turns, featureless walls, and poor camera quality. We introduce the Differentiable SLAM Network (SLAM-net) along with a navigation architecture to enable planar robot navigation in previously unseen indoor environments. SLAM-net encodes a particle filter based SLAM algorithm in a differentiable computation graph, and learns task-oriented neural network components by backpropagating through the SLAM algorithm. Because it can optimize all model components jointly for the end-objective, SLAM-net learns to be robust in challenging conditions. We run experiments in the Habitat platform with different real-world RGB and RGB-D datasets. SLAM-net significantly outperforms the widely adapted ORB-SLAM in noisy conditions. Our navigation architecture with SLAMnet improves the state-of-the-art for the Habitat Challenge 2020 PointNav task by a large margin (37% to 64% success).

关键词： Visualization Simultaneous localization and mapping Navigation Robot vision systems Neural networks computer architecture Particle filters

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Reciprocal Landmark Detection and Tracking with Extremely Few Annotations

Reciprocal Landmark Detection and Tracking with Extremely Fe...

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

作者： Lin, Jianzhe Sahebzamani, Ghazal Luong, Christina Dezaki, Fatemeh Taheri Jafari, Mohammad Abolmaesumi, Purang Tsang, Teresa Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada Vancouver Gen Hosp Vancouver BC Canada Univ British Columbia Dept Med Vancouver BC Canada Univ British Columbia Div Cardiol Vancouver BC Canada

ISBN: (纸本)9781665445092

Localization of anatomical landmarks to perform two-dimensional measurements in echocardiography is part of routine clinical workflow in cardiac disease diagnosis. Automatic localization of those landmarks is highly desirable to improve workflow and reduce interobserver variability. Training a machine learning framework to perform such localization is hindered given the sparse nature of gold standard labels;only few percent of cardiac cine series frames are normally manually labeled for clinical use. In this paper, we propose a new end-to-end reciprocal detection and tracking model that is specifically designed to handle the sparse nature of echocardiography labels. The model is trained using few annotated frames across the entire cardiac cine sequence to generate consistent detection and tracking of landmarks, and an adversarial training for the model is proposed to take advantage of these annotated frames. The superiority of the proposed reciprocal model is demonstrated using a series of experiments.

关键词： Training Location awareness computer vision Echocardiography Annotations Computational modeling Machine learning

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Regularizing Generative Adversarial Networks under Limited Data

Regularizing Generative Adversarial Networks under Limited D...

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

作者： Tseng, Hung-Yu Jiang, Lu Liu, Ce Yang, Ming-Hsuan Yang, Weilong Google Res Mountain View CA USA Univ Calif Merced Merced CA 95343 USA Waymo Mountain View CA USA Yonsei Univ Seoul South Korea

ISBN: (纸本)9781665445092

Recent years have witnessed the rapid progress of generative adversarial networks (GANs). However, the success of the GAN models hinges on a large amount of training data. This work proposes a regularization approach for training robust GAN models on limited data. We theoretically show a connection between the regularized loss and an f-divergence called LeCam-divergence, which we find is more robust under limited training data. Extensive experiments on several benchmark datasets demonstrate that the proposed regularization scheme 1) improves the generalization performance and stabilizes the learning dynamics of GAN models under limited training data, and 2) complements the recent data augmentation methods. These properties facilitate training GAN models to achieve state-of-the-art performance when only limited training data of the ImageNet benchmark is available. The source code is available at https://***/google/lecam-gan.

关键词： Training computer vision Codes Computational modeling Training data Benchmark testing Fasteners

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Linguistic Structures as Weak Supervision for Visual Scene Graph Generation

Linguistic Structures as Weak Supervision for Visual Scene G...

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

作者： Ye, Keren Kovashka, Adriana Univ Pittsburgh Dept Comp Sci Pittsburgh PA 15260 USA

ISBN: (纸本)9781665445092

Prior work in scene graph generation requires categorical supervision at the level of triplets-subjects and objects, and predicates that relate them, either with or without bounding box information. However, scene graph generation is a holistic task: thus holistic, contextual supervision should intuitively improve performance. In this work, we explore how linguistic structures in captions can benefit scene graph generation. Our method captures the information provided in captions about relations between individual triplets, and context for subjects and objects (e.g. visual properties are mentioned). Captions are a weaker type of supervision than triplets since the alignment between the exhaustive list of human-annotated subjects and objects in triplets, and the nouns in captions, is weak. However, given the large and diverse sources of multimodal data on the web (e.g. blog posts with images and captions), linguistic supervision is more scalable than crowdsourced triplets. We show extensive experimental comparisons against prior methods which leverage instance- and image-level supervision, and ablate our method to show the impact of leveraging phrasal and sequential context, and techniques to improve localization of subjects and objects.

关键词： Location awareness Visualization computer vision Blogs Linguistics pattern recognition Noise measurement

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Closed-Form Factorization of Latent Semantics in GANs

Closed-Form Factorization of Latent Semantics in GANs

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

作者： Shen, Yujun Zhou, Bolei Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665445092

A rich set of interpretable dimensions has been shown to emerge in the latent space of the Generative Adversarial Networks (GANs) trained for synthesizing images. In order to identify such latent dimensions for image editing, previous methods typically annotate a collection of synthesized samples and train linear classifiers in the latent space. However, they require a clear definition of the target attribute as well as the corresponding manual annotations, limiting their applications in practice. In this work, we examine the internal representation learned by GANs to reveal the underlying variation factors in an unsupervised manner. In particular, we take a closer look into the generation mechanism of GANs and further propose a closed-form factorization algorithm for latent semantic discovery by directly decomposing the pre-trained weights. With a lightning-fast implementation, our approach is capable of not only finding semantically meaningful dimensions comparably to the state-of-the-art supervised methods, but also resulting in far more versatile concepts across multiple GAN models trained on a wide range of datasets.(1)

关键词： computer vision Limiting Closed-form solutions Annotations Computational modeling Semantics Manuals

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