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StyleMix: Separating Content and Style for Enhanced Data Augmentation

StyleMix: Separating Content and Style for Enhanced Data Aug...

引用

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

作者： Hong, Minui Choi, Jinwoo Kim, Gunhee Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9781665445092

In spite of the great success of deep neural networks for many challenging classification tasks, the learned networks are vulnerable to overfitting and adversarial attacks. Recently, mixup based augmentation methods have been actively studied as one practical remedy for these drawbacks. However, these approaches do not distinguish between the content and style features of the image, but mix or cut-andpaste the images. We propose StyleMix and StyleCutMix as the first mixup method that separately manipulates the content and style information of input image pairs. By carefully mixing up the content and style of images, we can create more abundant and robust samples, which eventually enhance the generalization of model training. We also develop an automatic scheme to decide the degree of style mixing according to the pair's class distance, to prevent messy mixed images from too differently styled pairs. Our experiments on CIFAR-10, CIFAR-100 and ImageNet datasets show that StyleMix achieves better or comparable performance to state of the art mixup methods and learns more robust classifiers to adversarial attacks.

关键词： Training Deep learning computer vision Head Image color analysis Skin Robustness

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DualAST: Dual Style-Learning Networks for Artistic Style Transfer

DualAST: Dual Style-Learning Networks for Artistic Style Tra...

引用

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

作者： Chen, Haibo Zhao, Lei Wang, Zhizhong Zhang, Huiming Zuo, Zhiwen Li, Ailin Xing, Wei Lu, Dongming Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China

ISBN: (纸本)9781665445092

Artistic style transfer is an image editing task that aims at repainting everyday photographs with learned artistic styles. Existing methods learn styles from either a single style example or a collection of artworks. Accordingly, the stylization results are either inferior in visual quality or limited in style controllability. To tackle this problem, we propose a novel Dual Style-Learning Artistic Style Transfer (DualAST) framework to learn simultaneously both the holistic artist-style (from a collection of artworks) and the specific artwork-style (from a single style image): the artist-style sets the tone (i.e., the overall feeling) for the stylized image, while the artwork-style determines the details of the stylized image, such as color and texture. Moreover, we introduce a Style-Control Block (SCB) to adjust the styles of generated images with a set of learnable style-control factors. We conduct extensive experiments to evaluate the performance of the proposed framework, the results of which confirm the superiority of our method.

关键词： Visualization computer vision Image color analysis Controllability pattern recognition Task analysis

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AutoDO: Robust AutoAugment for Biased Data with Label Noise via Scalable Probabilistic Implicit Differentiation

AutoDO: Robust AutoAugment for Biased Data with Label Noise ...

引用

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

作者： Gudovskiy, Denis Rigazio, Luca Ishizaka, Shun Kozuka, Kazuki Tsukizawa, Sotaro Panasonic AI Lab Newark NJ 07102 USA AIoli Labs Phoenix AZ USA Panasonic Technol Div Osaka Japan

ISBN: (纸本)9781665445092

AutoAugment [4] has sparked an interest in automated augmentation methods for deep learning models. These methods estimate image transformation policies for train data that improve generalization to test data. While recent papers evolved in the direction of decreasing policy search complexity, we show that those methods are not robust when applied to biased and noisy data. To overcome these limitations, we reformulate AutoAugment as a generalized automated dataset optimization (AutoDO) task that minimizes the distribution shift between test data and distorted train dataset. In our AutoDO model, we explicitly estimate a set of per-point hyperparameters to flexibly change distribution of train data. In particular, we include hyperparameters for augmentation, loss weights, and softlabels that are jointly estimated using implicit differentiation. We develop a theoretical probabilistic interpretation of this framework using Fisher information and show that its complexity scales linearly with the dataset size. Our experiments on SVHN, CIFAR-10/100, and ImageNet classification show up to 9.3% improvement for biased datasets with label noise compared to prior methods and, importantly, up to 36.6% gain for underrepresented SVHN classes(1).

关键词： Deep learning computer vision Probabilistic logic Distortion Data models Complexity theory pattern recognition

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Jo-SRC: A Contrastive Approach for Combating Noisy Labels

Jo-SRC: A Contrastive Approach for Combating Noisy Labels

引用

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

作者： Yao, Yazhou Sun, Zeren Zhang, Chuanyi Shen, Fumin Wu, Qi Zhang, Jian Tang, Zhenmin Nanjing Univ Sci & Technol Nanjing Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China Univ Adelaide Adelaide SA Australia Univ Technol Sydney Sydney NSW Australia

ISBN: (纸本)9781665445092

Due to the memorization effect in Deep Neural Networks (DNNs), training with noisy labels usually results in inferior model performance. Existing state-of-the-art methods primarily adopt a sample selection strategy, which selects small-loss samples for subsequent training. However, prior literature tends to perform sample selection within each mini-batch, neglecting the imbalance of noise ratios in different mini-batches. Moreover, valuable knowledge within high-loss samples is wasted. To this end, we propose a noise-robust approach named Jo-SRC (Joint Sample Selection and Model Regularization based on Consistency). Specifically, we train the network in a contrastive learning manner. Predictions from two different views of each sample are used to estimate its "likelihood" of being clean or out-of-distribution. Furthermore, we propose a joint loss to advance the model generalization performance by introducing consistency regularization. Extensive experiments have validated the superiority of our approach over existing stateof-the-art methods.

关键词： Training Degradation Deep learning computer vision Codes Robustness pattern recognition

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ArtCoder: An End-to-end Method for Generating Scanning-robust Stylized QR Codes

ArtCoder: An End-to-end Method for Generating Scanning-robus...

引用

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

作者： Su, Hao Niu, Jianwei Liu, Xuefeng Li, Qingfeng Wan, Ji Xu, Mingliang Ren, Tao Beihang Univ Sch Comp Sci & Engn State Key Lab VR Technol & Syst Beijing Peoples R China Zhengzhou Univ Sch Informat Engn Ind Technol Res Inst Zhengzhou Peoples R China Beihang Univ Hangzhou Innovat Inst Beijing Peoples R China

ISBN: (纸本)9781665445092

Quick Response (QR) code is one of the most worldwide used two-dimensional codes. Traditional QR codes appear as random collections of black-and-white modules that lack visual semantics and aesthetic elements, which inspires the recent works to beautify the appearances of QR codes. However, these works adopt fixed generation algorithms and therefore can only generate QR codes with a pre-defined style. In this paper, combining the Neural Style Transfer technique, we propose a novel end-to-end method, named ArtCoder, to generate the stylized QR codes that are personalized, diverse, attractive, and scanning-robust. To guarantee that the generated stylized QR codes are still scanning-robust, we propose a Sampling-Simulation layer, a module-based code loss, and a competition mechanism. The experimental results show that our stylized QR codes have high-quality in both the visual effect and the scanning-robustness, and they are able to support the real-world application.

关键词： computer vision Codes Semantics Diversity reception Visual effects pattern recognition

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Stochastic Whitening Batch Normalization

Stochastic Whitening Batch Normalization

引用

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

作者： Zhang, Shengdong Nezhadarya, Ehsan Fashandi, Homa Liu, Jiayi Graham, Darin Shah, Mohak LG Elect Canada Toronto AI Lab Toronto ON Canada LG Elect USA Amer R&D Lab Santa Clara CA USA

ISBN: (纸本)9781665445092

Batch Normalization (BN) is a popular technique for training Deep Neural Networks (DNNs). BN uses scaling and shifting to normalize activations of mini-batches to accelerate convergence and improve generalization. The recently proposed Iterative Normalization (IterNorm) method improves these properties by whitening the activations iteratively using Newton's method. However, since Newton's method initializes the whitening matrix independently at each training step, no information is shared between consecutive steps. In this work, instead of exact computation of whitening matrix at each time step, we estimate it gradually during training in an online fashion, using our proposed Stochastic Whitening Batch Normalization (SWBN) algorithm. We show that while SWBN improves the convergence rate and generalization of DNNs, its computational overhead is less than that of IterNorm. Due to the high efficiency of the proposed method, it can be easily employed in most DNN architectures with a large number of layers. We provide comprehensive experiments and comparisons between BN, IterNorm, and SWBN layers to demonstrate the effectiveness of the proposed technique in conventional (many-shot) image classification and few-shot classification tasks.

关键词： Training Deep learning computer vision Stochastic processes pattern recognition Iterative methods Convolutional neural networks

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Multi-Scale Feature Fusion using Channel Transformers for Guided Thermal Image Super Resolution

Multi-Scale Feature Fusion using Channel Transformers for Gu...

引用

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

作者： Raghunath Sai Puttagunta Birendra Kathariya Zhu Li George York University of Missouri-Kansas City US Air Force Academy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Thermal imaging, leveraging the infrared spectrum, offers a compelling alternative to visible spectrum (VIS) imagery in challenging environmental conditions like low- light, occlusions, and adverse weather. However, its widespread adoption in computer vision tasks is hampered by lower spatial resolution. We address this challenge by proposing a novel framework titled Multi-Scale Feature Fusion using Channel Transformers (MSFFCT) for Guided Thermal Image Super-Resolution (GTISR).GTISR tackles the resolution limitations of thermal imagery. It leverages high-resolution RGB information as a guide to reconstruct high-resolution thermal imagery from low-resolution thermal inputs. At the core of MSFFCT lies a novel deep learning architecture that combines the strengths of two powerful approaches: channel-based transformers and multi-scale *** overcomes inherent limitations of Convolutional Neural Networks (CNNs) typically used in super-resolution tasks. CNNs often suffer from restricted receptive fields, limiting their ability to capture long-range dependencies within the image. Additionally, computational cost grows significantly with larger inputs. MSFFCT addresses these shortcomings by enabling efficient processing of global information and offering superior scalability. MSFFCT achieved state-of-the-art results on the ×8 and × 16 GTISR tasks of the 2024 Perception Beyond Visual Spectrum (PBVS) challenge, winning 2 nd place in both tasks and demonstrating its effectiveness in real-world scenarios.

关键词： computer vision Visualization Limiting Superresolution Imaging Transformer cores Transformers

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Guided Integrated Gradients: an Adaptive Path Method for Removing Noise

Guided Integrated Gradients: an Adaptive Path Method for Rem...

引用

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

作者： Kapishnikov, Andrei Venugopalan, Subhashini Avci, Besim Wedin, Ben Terry, Michael Bolukbasi, Tolga Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Integrated Gradients (IG) [29] is a commonly used feature attribution method for deep neural networks. While IG has many desirable properties, the method often produces spurious/noisy pixel attributions in regions that are not related to the predicted class when applied to visual models. While this has been previously noted [27], most existing solutions [25, 17] are aimed at addressing the symptoms by explicitly reducing the noise in the resulting attributions. In this work, we show that one of the causes of the problem is the accumulation of noise along the IG path. To minimize the effect of this source of noise, we propose adapting the attribution path itself - conditioning the path not just on the image but also on the model being explained. We introduce Adaptive Path Methods (APMs) as a generalization of path methods, and Guided IG as a specific instance of an APM. Empirically, Guided IG creates saliency maps better aligned with the model's prediction and the input image that is being explained. We show through qualitative and quantitative experiments that Guided IG outperforms other, related methods in nearly every experiment.

关键词： Measurement Deep learning Adaptation models Visualization computer vision Computational modeling Predictive models

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ChallenCap: Monocular 3D Capture of Challenging Human Performances using Multi-Modal References

ChallenCap: Monocular 3D Capture of Challenging Human Perfor...

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

作者： He, Yannan Pang, Anqi Chen, Xin Liang, Han Wu, Minye Ma, Yuexin Xu, Lan ShanghaiTech Univ Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China

ISBN: (纸本)9781665445092

Capturing challenging human motions is critical for numerous applications, but it suffers from complex motion patterns and severe self-occlusion under the monocular setting. In this paper, we propose ChallenCap - a template-based approach to capture challenging 3D human motions using a single RGB camera in a novel learning-and-optimization framework, with the aid of multi-modal references. We propose a hybrid motion inference stage with a generation network, which utilizes a temporal encoder-decoder to extract the motion details from the pair-wise sparse-view reference, as well as a motion discriminator to utilize the unpaired marker-based references to extract specific challenging motion characteristics in a data-driven manner. We further adopt a robust motion optimization stage to increase the tracking accuracy, by jointly utilizing the learned motion details from the supervised multi-modal references as well as the reliable motion hints from the input image reference. Extensive experiments on our new challenging motion dataset demonstrate the effectiveness and robustness of our approach to capture challenging human motions.

关键词： computer vision Three-dimensional displays Tracking Cameras Robustness Hybrid power systems pattern recognition

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Towards Part-Based Understanding of RGB-D Scans

Towards Part-Based Understanding of RGB-D Scans

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

作者： Bokhovkin, Alexey Ishimtsev, Vladislav Bogomolov, Emil Zorin, Denis Artemov, Alexey Burnaev, Evgeny Dai, Angela Tech Univ Munich Munich Germany Skolkovo Inst Sci & Technol Moscow Russia NYU New York NY 10003 USA

ISBN: (纸本)9781665445092

Recent advances in 3D semantic scene understanding have shown impressive progress in 3D instance segmentation, enabling object-level reasoning about 3D scenes;however, a finer-grained understanding is required to enable interactions with objects and their functional understanding. Thus, we propose the task of part-based scene understanding of real-world 3D environments: from an RGB-D scan of a scene, we detect objects, and for each object predict its decomposition into geometric part masks, which composed together form the complete geometry of the observed object. We leverage an intermediary part graph representation to enable robust completion as well as building of part priors, which we use to construct the final part mask predictions. Our experiments demonstrate that guiding part understanding through part graph to part prior-based predictions significantly outperforms alternative approaches to the task of semantic part completion.

关键词： Geometry computer vision Three-dimensional displays Semantics Buildings Cognition Autonomous agents

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