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The Majority Can Help the Minority: Context-rich Minority Oversampling for Long-tailed Classification

The Majority Can Help the Minority: Context-rich Minority Ov...

引用

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

作者： Park, Seulki Hong, Youngkyu Heo, Byeongho Yun, Sangdoo Choi, Jin Young Seoul Natl Univ ECE ASRI Seoul South Korea NAVER AI Lab Bundangdong Kyonggi Do South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The problem of class imbalanced data is that the generalization performance of the classifier deteriorates due to the lack of data from minority classes. In this paper, we pro-pose a novel minority over-sampling method to augment di-versified minority samples by leveraging the rich context of the majority classes as background images. To diversify the minority samples, our key idea is to paste an image from a minority class onto rich-context images from a majority class, using them as background images. Our method is simple and can be easily combined with the existing long-tailed recognition methods. We empirically prove the effectiveness of the proposed oversampling method through extensive experiments and ablation studies. Without any architectural changes or complex algorithms, our method achieves state-of-the-art performance on various long-tailed classification benchmarks. Our code is made available at https://***/naver-ai/cmo.

关键词： computer vision Codes Benchmark testing pattern recognition Classification algorithms

来源：评论

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AdaFace: Quality Adaptive Margin for Face recognition

AdaFace: Quality Adaptive Margin for Face Recognition

引用

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

作者： Kim, Minchul Jain, Anil K. Liu, Xiaoming Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

recognition in low quality face datasets is challenging because facial attributes are obscured and degraded. Advances in margin -based loss functions have resulted in enhanced discriminability of faces in the embedding space. Further, previous studies have studied the effect of adaptive losses to assign more importance to misclassified (hard) examples. In this work, we introduce another aspect of adaptiveness in the loss function, namely the image quality. We argue that the strategy to emphasize misclassified samples should be adjusted according to their image quality. Specifically, the relative importance of easy or hard samples should be based on the sample's image quality. We propose a new loss function that emphasizes samples of different difficulties based on their image quality. Our method achieves this in the form of an adaptive margin function by approximating the image quality with feature norms. Extensive experiments show that our method, AdaFace, improves the face recognition performance over the state-ofthe-art (SoTA) on four datasets (IJB-B, IJB-C, IJB-S and TinyFace). Code and models are released in Stipp.

关键词： Image quality Training computer vision Adaptation models Codes Face recognition Training data

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BokehMe: When Neural Rendering Meets Classical Rendering

BokehMe: When Neural Rendering Meets Classical Rendering

引用

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

作者： Peng, Juewen Cao, Zhiguo Luo, Xianrui Lu, Hao Xian, Ke Zhang, Jianming Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China Adobe Res San Jose CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose BokehMe, a hybrid bokeh rendering framework that marries a neural renderer with a classical physically motivated renderer. Given a single image and a potentially imperfect disparity map, BokehMe generates high-resolution photo-realistic bokeh effects with adjustable blur size, focal plane, and aperture shape. To this end, we analyze the errors from the classical scattering-based method and derive a formulation to calculate an error map. Based on this formulation, we implement the classical renderer by a scattering-based method and propose a two-stage neural renderer to fix the erroneous areas from the classical renderer. The neural renderer employs a dynamic multi-scale scheme to efficiently handle arbitrary blur sizes, and it is trained to handle imperfect disparity input. Experiments show that our method compares favorably against previous methods on both synthetic image data and real image data with predicted disparity. A user study is further conducted to validate the advantage of our method.

关键词： Photography computer vision Shape Apertures Rendering (computer graphics) pattern recognition

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FWD: Real-time Novel View Synthesis with Forward Warping and Depth

FWD: Real-time Novel View Synthesis with Forward Warping and...

引用

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

作者： Cao, Ang Rockwell, Chris Johnson, Justin Univ Michigan Ann Arbor MI 48109 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Novel view synthesis (NVS) is a challenging task requiring systems to generate photorealistic images of scenes from new viewpoints, where both quality and speed are important for applications. Previous image-based rendering (IBR) methods are fast, but have poor quality when input views are sparse. Recent Neural Radiance Fields (NeRF) and generalizable variants give impressive results but are not real-time. In our paper, we propose a generalizable NVS method with sparse inputs, called FWD, which gives high-quality synthesis in real-time. With explicit depth and differentiable rendering, it achieves competitive results to the SOTA methods with 130-1000x speedup and better perceptual quality. If available, we can seamlessly integrate sensor depth during either training or inference to improve image quality while retaining real-time speed. With the growing prevalence of depths sensors, we hope that methods making use of depth will become increasingly useful.

关键词： Training Image quality Visualization computer vision Three-dimensional displays Rendering (computer graphics) Real-time systems

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A Simple Data Mixing Prior for Improving Self-Supervised Learning

A Simple Data Mixing Prior for Improving Self-Supervised Lea...

引用

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

作者： Ren, Sucheng Wang, Huiyu Gao, Zhengqi He, Shengfeng Yuille, Alan Zhou, Yuyin Xie, Cihang South China Univ Technol Guangzhou Peoples R China Johns Hopkins Univ Baltimore MD 21218 USA MIT Cambridge MA 02139 USA UC Santa Cruz Santa Cruz CA 95064 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Data mixing (e.g., Mixup, Cutmix, ResizeMix) is an essential component for advancing recognition models. In this paper, we focus on studying its effectiveness in the self-supervised setting. By noticing the mixed images that share the same source images are intrinsically related to each other, we hereby propose SDMP, short for Simple Data Mixing Prior, to capture this straightforward yet essential prior, and position such mixed images as additional positive pairs to facilitate self-supervised representation learning. Our experiments verify that the proposed SDMP enables data mixing to help a set of self-supervised learning frameworks (e.g., MoCo) achieve better accuracy and out-of-distribution robustness. More notably, our SDMP is the first method that successfully leverages data mixing to improve (rather than hurt) the performance of vision Transformers in the self-supervised setting. Code is publicly available at https://***/OliverRensu/SDMP.

关键词： Training computer vision Codes Self-supervised learning computer architecture Transformers Robustness

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Stereo Magnification with Multi-Layer Images

Stereo Magnification with Multi-Layer Images

引用

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

作者： Khakhulin, T. Korzhenkov, D. Solovev, P. Sterkin, G. Ardelean, A-T Lempitsky, V Samsung AI Ctr Moscow Russia Skolkovo Inst Sci & Technol Moscow Russia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Representing scenes with multiple semitransparent colored layers has been a popular and successful choice for real-time novel view synthesis. Existing approaches infer colors and transparency values over regularly spaced layers of planar or spherical shape. In this work, we introduce a new view synthesis approach based on multiple semitransparent layers with scene-adapted geometry. Our approach infers such representations from stereo pairs in two stages. The first stage produces the geometry of a small number of data-adaptive layers from a given pair of views. The second stage infers the color and transparency values for these layers, producing the final representation for novel view synthesis. Importantly, both stages are connected through a differentiable renderer and are trained end-to-end. In the experiments, we demonstrate the advantage of the proposed approach over the use of regularly spaced layers without adaptation to scene geometry. Despite being orders of magnitude faster during rendering, our approach also outperforms the recently proposed IBRNet system based on implicit geometry representation.

关键词： Geometry computer vision Image color analysis Shape Rendering (computer graphics) Real-time systems pattern recognition

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SNR-Aware Low-light Image Enhancement

SNR-Aware Low-light Image Enhancement

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

作者： Xu, Xiaogang Wang, Ruixing Fu, Chi-Wing Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China SmartMore Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper presents a new solution for low-light image enhancement by collectively exploiting Signal-to-Noise-Ratio-aware transformers and convolutional models to dynamically enhance pixels with spatial-varying operations. They are long-range operations for image regions of extremely low Signal-to-Noise-Ratio (SNR) and short-range operations for other regions. We propose to take an SNR prior to guide the feature fusion and formulate the SNR-aware transformer with a new self-attention model to avoid tokens from noisy image regions of very low SNR. Extensive experiments show that our framework consistently achieves better performance than SOTA approaches on seven representative benchmarks with the same structure. Also, we conducted a large-scale user study with 100 participants to verify the superior perceptual quality of our results. The code is available at https://***/dvlab-research/SNR-Aware-Low-Light-Enhance.

关键词： Photography Convolutional codes Convolution Semantics Transformers pattern recognition Noise measurement

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Integrative Few-Shot Learning for Classification and Segmentation

Integrative Few-Shot Learning for Classification and Segment...

引用

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

作者： Kang, Dahyun Cho, Minsu Pohang Univ Sci & Technol POSTECH Pohang South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We introduce the integrative task of few-shot classification and segmentation (FS-CS) that aims to both classify and segment target objects in a query image when the target classes are given with a few examples. This task combines two conventional few-shot learning problems, fewshot classification and segmentation. FS-CS generalizes them to more realistic episodes with arbitrary image pairs, where each target class may or may not be present in the query. To address the task, we propose the integrative fewshot learning (iFSL) framework for FS-CS, which trains a learner to construct class-wise foreground maps for multilabel classification and pixel-wise segmentation. We also develop an effective iFSL model, attentive squeeze network (ASNet), that leverages deep semantic correlation and global self-attention to produce reliable foreground maps. In experiments, the proposed method shows promising performance on the FS-CS task and also achieves the state of the art on standard few-shot segmentation benchmarks.

关键词： Image segmentation computer vision Correlation computer network reliability Semantics Benchmark testing pattern recognition

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Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes

Eigenlanes: Data-Driven Lane Descriptors for Structurally Di...

引用

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

作者： Jin, Dongkwon Park, Wonhui Jeong, Seong-Gyun Kwon, Heeyeon Kim, Chang-Su Korea Univ Seoul South Korea 42dotai Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

A novel algorithm to detect road lanes in the eigenlane space is proposed in this paper: First, we introduce the notion of eigenlanes, which are data-driven descriptors for structurally diverse lanes, including curved, as well as straight, lanes. To obtain eigenlanes, we perform the best rank-M approximation of a lane matrix containing all lanes in a training set. Second, we generate a set of lane candidates by clustering the training lanes in the eigenlane space. Third, using the lane candidates, we determine an optimal set of lanes by developing an anchor-based detection network, called SIIC-Net. Experimental results demonstrate that the proposed algorithm provides excellent detection performance for structurally diverse lanes. Our codes are available at https://***/dongkwonjin/Eigenlanes.

关键词： Training computer vision Image analysis Codes Navigation Roads Machine vision

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GradViT: Gradient Inversion of vision Transformers

GradViT: Gradient Inversion of Vision Transformers

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

作者： Hatamizadeh, Ali Yin, Hongxu Roth, Holger Li, Wenqi Kautz, Jan Xu, Daguang Molchanov, Pavlo NVIDIA Santa Clara CA 95051 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this work we demonstrate the vulnerability of vision transformers (ViTs) to gradient-based inversion attacks. During this attack, the original data batch is reconstructed given model weights and the corresponding gradients. We introduce a method, named GradViT, that optimizes random noise into naturally looking images via an iterative process. The optimization objective consists of (i) a loss on matching the gradients, (ii) image prior in the form of distance to batch-normalization statistics of a pretrained CNN model, and (iii) a total variation regularization on patches to guide correct recovery locations. We propose a unique loss scheduling function to overcome local minima during optimization. We evaluate GadViT on ImageNet1K and MS-Celeb-1M datasets, and observe unprecedentedly high fidelity and closeness to the original (hidden) data. During the analysis we find that vision transformers are significantly more vulnerable than previously studied CNNs due to the presence of the attention mechanism. Our method demonstrates new state-of-the-art results for gradient inversion in both qualitative and quantitative metrics. Project page at https.//***/.

关键词： Measurement Differential privacy Neural networks Transformers pattern recognition Security Iterative methods

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