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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

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Unpaired Faces to Cartoons: Improving XGAN

Unpaired Faces to Cartoons: Improving XGAN

引用

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

作者： Stev H. Ramos Joel Cabrera Daniel Ibá ñ ez Alejandro B. Jimé nez-Panta Cé sar Beltrá n-Castañ ó n Edwin Villanueva Pontifical Catholic University of Peru Lima Peru

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Domain Adaptation is a task that aims to translate an image from a source domain to a desired target domain. Current methods in domain adaptation use adversarial training based on Generative Adversarial Networks (GAN). In the present work, we focus on the task of domain adaptation from real faces to cartoon face images. We start from a baseline architecture called XGAN and introduce some improvements to it. Our proposed model is called W-XDGAN, which uses a form of GAN called Wasserstein-GAN, learns to approximate the Wasserstein Distance, and adds a denoiser to smooth the output cartoons. Whereas the original XGAN paper only presented a qualitative analysis, the advantages of this solution are demonstrated both quantitatively and qualitatively by comparing the results with models such as UNIT and original XGAN. Our code and models are publicly available at https://***/IAmigos/avatar-image-generator.

关键词： Training computer vision Codes Computational modeling conferences computer architecture Generative adversarial networks

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Learning Event Guided High Dynamic Range Video Reconstruction

Learning Event Guided High Dynamic Range Video Reconstructio...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yixin Yang Jin Han Jinxiu Liang Imari Sato Boxin Shi National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University Graduate School of Information Science and Technology The University of Tokyo National Institute of Informatics

Limited by the trade-off between frame rate and exposure time when capturing moving scenes with conventional cameras, frame based HDR video reconstruction suffers from scene-dependent exposure ratio balancing and ghosting artifacts. Event cameras provide an alternative visual representation with a much higher dynamic range and temporal resolution free from the above issues, which could be an effective guidance for HDR imaging from LDR videos. In this paper, we propose a multimodal learning framework for event guided HDR video reconstruction. In order to better leverage the knowledge of the same scene from the two modalities of visual signals, a multimodal representation alignment strategy to learn a shared latent space and a fusion module tailored to complementing two types of signals for different dynamic ranges in different regions are proposed. Temporal correlations are utilized recurrently to suppress the flickering effects in the reconstructed HDR video. The proposed HDRev-Net demonstrates state-of-the-art performance quantitatively and qualitatively for both synthetic and real-world data.

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Revisiting the Stack-Based Inverse Tone Mapping

Revisiting the Stack-Based Inverse Tone Mapping

引用

conference on computer vision and pattern recognition (CVPR)

作者： Ning Zhang Yuyao Ye Yang Zhao Ronggang Wang School of Electronics and Computer Engineering Peking University Peng Cheng Laboratory School of Computer and Information Hefei University of Technology

Current stack-based inverse tone mapping (ITM) methods can recover high dynamic range (HDR) radiance by predicting a set of multi-exposure images from a single low dynamic range image. However, there are still some limitations. On the one hand, these methods estimate a fixed number of images (e.g., three exposure-up and three exposure-down), which may introduce unnecessary computational cost or reconstruct incorrect results. On the other hand, they neglect the connections between the up-exposure and down-exposure models and thus fail to fully excavate effective features. In this paper, we revisit the stack-based ITM approaches and propose a novel method to reconstruct HDR radiance from a single image, which only needs to estimate two exposure images. At first, we design the exposure adaptive block that can adaptively adjust the exposure based on the luminance distribution of the input image. Secondly, we devise the cross-model attention block to connect the exposure adjustment models. Thirdly, we propose an end-to-end ITM pipeline by incorporating the multi-exposure fusion model. Furthermore, we propose and open a multi-exposure dataset that indicates the optimal exposure-up/down levels. Experimental results show that the proposed method outperforms some state-of-the-art methods.

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Feature Representation Learning with Adaptive Displacement Generation and Transformer Fusion for Micro-Expression recognition

Feature Representation Learning with Adaptive Displacement G...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Zhijun Zhai Jianhui Zhao Chengjiang Long Wenju Xu Shuangjiang He Huijuan Zhao School of Computer Science Wuhan University Wuhan Hubei China Meta Reality Labs Burlingame CA USA OPPO US Research Center InnoPeak Technology Inc Palo Alto CA USA FiberHome Telecommunication Technologies Co. Ltd Wuhan Hubei China

Micro-expressions are spontaneous, rapid and subtle facial movements that can neither be forged nor suppressed. They are very important nonverbal communication clues, but are transient and of low intensity thus difficult to recognize. Recently deep learning based methods have been developed for micro-expression (ME) recognition using feature extraction and fusion techniques, however, targeted feature learning and efficient feature fusion still lack further study according to the ME characteristics. To address these issues, we propose a novel framework Feature Representation Learning with adaptive Displacement Generation and Transformer fusion (FRL-DGT), in which a convolutional Displacement Generation Module (DGM) with self-supervised learning is used to extract dynamic features from onset/apex frames targeted to the subsequent ME recognition task, and a well-designed Transformer Fusion mechanism composed of three Transformer-based fusion modules (local, global fusions based on AU regions and full-face fusion) is applied to extract the multi-level informative features after DGM for the final ME prediction. The extensive experiments with solid leave-one-subject-out (LOSO) evaluation results have demonstrated the superiority of our proposed FRL-DGT to state-of-the-art methods.

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CHMATCH: Contrastive Hierarchical Matching and Robust Adaptive Threshold Boosted Semi-Supervised Learning

CHMATCH: Contrastive Hierarchical Matching and Robust Adapti...

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conference on computer vision and pattern recognition (CVPR)

作者： Jianlong Wu Haozhe Yang Tian Gan Ning Ding Feijun Jiang Liqiang Nie School of Computer Science and Technology Harbin Institute of Technology Shenzhen School of Computer Science and Technology Shandong University Alibaba Group

The recently proposed FixMatch and FlexMatch have achieved remarkable results in the field of semi-supervised learning. But these two methods go to two extremes as FixMatch and FlexMatch use a pre-defined constant threshold for all classes and an adaptive threshold for each category, respectively. By only investigating consistency regularization, they also suffer from unstable results and indiscriminative feature representation, especially under the situation of few labeled samples. In this paper, we propose a novel CHMatch method, which can learn robust adaptive thresholds for instance-level prediction matching as well as discriminative features by contrastive hierarchical matching. We first present a memory-bank based robust threshold learning strategy to select highly-confident samples. In the meantime, we make full use of the structured information in the hierarchical labels to learn an accurate affinity graph for contrastive learning. CHMatch achieves very stable and superior results on several commonly-used benchmarks. For example, CHMatch achieves 8.44% and 9.02% error rate reduction over FlexMatch on CIFAR-100 under WRN-28-2 with only 4 and 25 labeled samples per class, respectively 1 1 Project address: https://***/sailist/CHMatch.

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Learning Imbalanced Data with vision Transformers

Learning Imbalanced Data with Vision Transformers

引用

conference on computer vision and pattern recognition (CVPR)

作者： Zhengzhuo Xu Ruikang Liu Shuo Yang Zenghao Chai Chun Yuan Shenzhen International Graduate School Tsinghua University China

The real-world data tends to be heavily imbalanced and severely skew the data-driven deep neural networks, which makes Long-Tailed recognition (LTR) a massive challenging task. Existing LTR methods seldom train vision Transformers (ViTs) with Long-Tailed (LT) data, while the off-the-shelf pretrain weight of ViTs always leads to unfair comparisons. In this paper, we systematically investigate the ViTs' performance in LTR and propose LiVT to train ViTs from scratch only with LT data. With the observation that ViTs suffer more severe LTR problems, we conduct Masked Generative Pretraining (MGP) to learn generalized features. With ample and solid evidence, we show that MGP is more robust than supervised manners. Although Binary Cross Entropy (BCE) loss performs well with ViTs, it struggles on the LTR tasks. We further propose the balanced BCE to ameliorate it with strong theoretical groundings. Specially, we derive the unbiased extension of Sigmoid and compensate extra logit margins for deploying it. Our Bal-BCE contributes to the quick convergence of ViTs in just a few epochs. Extensive experiments demonstrate that with MGP and Bal-BCE, LiVT successfully trains ViTs well without any additional data and outperforms comparable state-of-the-art methods significantly, e.g., our ViT-B achieves 81.0% Top-1 accuracy in iNaturalist 2018 without bells and whistles. Code is available at https://***/XuZhengzhuo/LiVT.

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Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-Shot Learning with Hyperspherical Embeddings

Hubs and Hyperspheres: Reducing Hubness and Improving Transd...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Daniel J. Trosten Rwiddhi Chakraborty Sigurd Løksc Kristoffer Knutsen Wickstrøm Robert Jenssen Michael C. Kampffmeyer Department of Physics and Technology UiT The Arctic University of Norway UiT Machine Learning group (machine-learning.uit. no) and Visual Intelligence Centre (visual-intelligence.no) Norwegian Computing Center Department of Computer Science University of Copenhagen Pioneer Centre for AI (aicentre.dk)

Distance-based classification is frequently used in transductive few-shot learning (FSL). However, due to the high-dimensionality of image representations, FSL classifiers are prone to suffer from the hubness problem, where a few points (hubs) occur frequently in multiple nearest neighbour lists of other points. Hubness negatively impacts distance-based classification when hubs from one class appear often among the nearest neighbors of points from another class, degrading the classifier's performance. To address the hubness problem in FSL, we first prove that hubness can be eliminated by distributing representations uniformly on the hypersphere. We then propose two new approaches to embed representations on the hypersphere, which we prove optimize a tradeoff between uniformity and local similarity preservation - reducing hubness while retaining class structure. Our experiments show that the proposed methods reduce hubness, and significantly improves transductive FSL accuracy for a wide range of classifiers 1 1 Code available at https://***/uitml/noHub..

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DART: Diversify-Aggregate-Repeat Training Improves Generalization of Neural Networks

DART: Diversify-Aggregate-Repeat Training Improves Generaliz...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Samyak Jain Sravanti Addepalli Pawan Kumar Sahu Priyam Dey R. Venkatesh Babu Vision and AI Lab Indian Institute of Science Bangalore Indian Institute of Technology Varanasi Indian Institute of Technology Dhanbad

Generalization of Neural Networks is crucial for deploying them safely in the real world. Common training strategies to improve generalization involve the use of data augmentations, ensembling and model averaging. In this work, we first establish a surprisingly simple but strong benchmark for generalization which utilizes diverse augmentations within a training minibatch, and show that this can learn a more balanced distribution of features. Further, we propose Diversify-Aggregate-Repeat Training (DART) strategy that first trains diverse models using different augmentations (or domains) to explore the loss basin, and further Aggregates their weights to combine their expertise and obtain improved generalization. We find that Repeating the step of Aggregation throughout training improves the over-all optimization trajectory and also ensures that the individual models have sufficiently low loss barrier to obtain improved generalization on combining them. We theoretically justify the proposed approach and show that it indeed generalizes better. In addition to improvements in In-Domain generalization, we demonstrate SOTA performance on the Domain Generalization benchmarks in the popular DomainBed framework as well. Our method is generic and can easily be integrated with several base training algorithms to achieve performance gains. Our code is available here: https://***/val-iisc/DART.

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Decoupled Multimodal Distilling for Emotion recognition

Decoupled Multimodal Distilling for Emotion Recognition

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conference on computer vision and pattern recognition (CVPR)

作者： Yong Li Yuanzhi Wang Zhen Cui PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Human multimodal emotion recognition (MER) aims to perceive human emotions via language, visual and acoustic modalities. Despite the impressive performance of previous MER approaches, the inherent multimodal heterogeneities still haunt and the contribution of different modalities varies significantly. In this work, we mitigate this issue by proposing a decoupled multimodal distillation (DMD) approach that facilitates flexible and adaptive crossmodal knowledge distillation, aiming to enhance the discriminative features of each modality. Specially, the representation of each modality is decoupled into two parts, i.e., modality-irrelevant/-exclusive spaces, in a self-regression manner. DMD utilizes a graph distillation unit (GD-Unit) for each decoupled part so that each GD can be performed in a more specialized and effective manner. A GD-Unit consists of a dynamic graph where each vertice represents a modality and each edge indicates a dynamic knowledge distillation. Such GD paradigm provides a flexible knowledge transfer manner where the distillation weights can be automatically learned, thus enabling diverse crossmodal knowledge transfer patterns. Experimental results show DMD consistently obtains superior performance than state-of-the-art MER methods. Visualization results show the graph edges in DMD exhibit meaningful distributional patterns w.r.t. the modality-irrelevant/-exclusive feature spaces. Codes are re leased at https://***/mdswyz/DMD.

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Masked and Adaptive Transformer for Exemplar Based Image Translation

Masked and Adaptive Transformer for Exemplar Based Image Tra...

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conference on computer vision and pattern recognition (CVPR)

作者： Chang Jiang Fei Gao Biao Ma Yuhao Lin Nannan Wang Gang Xu School of Computer Science and Technology Hangzhou Dianzi University Hangzhou Institute of Technology Xidian University ISN State Key Laboratory Xidian University

We present a novel framework for exemplar based image translation. Recent advanced methods for this task mainly focus on establishing cross-domain semantic correspondence, which sequentially dominates image generation in the manner of local style control. Unfortunately, cross-domain semantic matching is challenging; and matching errors ultimately degrade the quality of generated images. To overcome this challenge, we improve the accuracy of matching on the one hand, and diminish the role of matching in image generation on the other hand. To achieve the former, we propose a masked and adaptive transformer (MAT) for learning accurate cross-domain correspondence, and executing context-aware feature augmentation. To achieve the latter, we use source features of the input and global style codes of the exemplar, as sup-plementary information, for decoding an image. Besides, we devise a novel contrastive style learning method, for acquire quality-discriminative style representations, which in turn benefit high-quality image generation. Experimen-tal results show that our method, dubbed MATEBIT, performs considerably better than state-of-the-art methods, in diverse image translation tasks. The codes are available at https://***/AiArt-HDU/MATEBIT.

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