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Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring

Efficient Frequency Domain-based Transformers for High-Quali...

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

作者： Kong, Lingshun Dong, Jiangxin Ge, Jianjun Li, Mingqiang Pan, Jinshan Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China China Elect Technol Grp Corp Informat Sci Acad Beijing Peoples R China

ISBN: (纸本)9798350301298

We present an effective and efficient method that explores the properties of Transformers in the frequency domain for high-quality image deblurring. Our method is motivated by the convolution theorem that the correlation or convolution of two signals in the spatial domain is equivalent to an element-wise product of them in the frequency domain. This inspires us to develop an efficient frequency domain-based self-attention solver (FSAS) to estimate the scaled dot-product attention by an element-wise product operation instead of the matrix multiplication in the spatial domain. In addition, we note that simply using the naive feed-forward network (FFN) in Transformers does not generate good deblurred results. To overcome this problem, we propose a simple yet effective discriminative frequency domain-based FFN (DFFN), where we introduce a gated mechanism in the FFN based on the Joint Photographic Experts Group (JPEG) compression algorithm to discriminatively determine which low- and high-frequency information of the features should be preserved for latent clear image restoration. We formulate the proposed FSAS and DFFN into an asymmetrical network based on an encoder and decoder architecture, where the FSAS is only used in the decoder module for better image deblurring. Experimental results show that the proposed method performs favorably against the state-of-the-art approaches.

关键词： Low-level vision

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Improving Landmark Localization with Semi-Supervised Learning 31

Improving Landmark Localization with Semi-Supervised Learnin...

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31st ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Honari, Sina Molchanov, Pavlo Tyree, Stephen Vincent, Pascal Pal, Christopher Kautz, Jan Univ Montreal MILA Montreal PQ Canada NVIDIA Santa Clara CA USA Ecole Polytech Montreal Montreal PQ Canada CIFAR Toronto ON Canada Facebook AI Res Menlo Pk CA USA NVIDIA Res Santa Clara CA USA

ISBN: (纸本)9781538664209

We present two techniques to improve landmark localization in images from partially annotated datasets. Our primary goal is to leverage the common situation where precise landmark locations are only provided for a small data subset, but where class labels for classification or regression tasks related to the landmarks are more abundantly available. First, we propose the framework of sequential multitasking and explore it here through an architecture for landmark localization where training with class labels acts as an auxiliary signal to guide the landmark localization on unlabeled data. A key aspect of our approach is that errors can be backpropagated through a complete landmark localization model. Second, we propose and explore an unsupervised learning technique for landmark localization based on having a model predict equivariant landmarks with respect to transformations applied to the image. We show that these techniques, improve landmark prediction considerably and can learn effective detectors even when only a small fraction of the dataset has landmark labels. We present results on two toy datasets and four real datasets, with hands and faces, and report new state-of-the-art on two datasets in the wild, e.g. with only 5% of labeled images we outperform previous state-of-the-art trained on the AFLW dataset.

关键词： Task analysis Training Predictive models Computational modeling computer architecture Neural networks Estimation

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Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration 31

Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature ...

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31st ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Sheng, Lu Lin, Ziyi Shao, Jing Wang, Xiaogang Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9781538664209

Zero-shot artistic style transfer is an important image synthesis problem aiming at transferring arbitrary style into content images. However;the trade-off between the generalization and efficiency in existing methods impedes a high quality zero-shot style transfer in real-time. In this pa pet;we resolve this dilemma and propose an efficient yet effective Avatar-Net that enables visually plausible multi scale transfer for arbitrary style. The key ingredient of our method is a style decorator that makes up the content features by semantically aligned style features from an arbitrary style image, which does not only holistically match their feature distributions but also preserve detailed style patterns in the decorated features. By embedding this module into an image reconstruction network that fuses multi scale style abstractions, the Avatar-Net renders multi-scale stylization for any style image in one feed-forward pass. We demonstrate the state-of-the-art effectiveness and efficiency of the proposed method in generating high-quality stylized images, with a series of successive applications include multiple style integration, video stylization and etc.

关键词： Feature extraction Image reconstruction Decoding pattern matching Visualization Optimization Semantics

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vision-Language Navigation with Self-Supervised Auxiliary Reasoning Tasks

Vision-Language Navigation with Self-Supervised Auxiliary Re...

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

作者： Zhu, Fengda Zhu, Yi Chang, Xiaojun Liang, Xiaodan Monash Univ Melbourne Vic Australia Univ Chinese Acad Sci Beijing Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Dark Matter AI Inc Guangzhou Peoples R China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171685

vision-Language Navigation (VLN) is a task where agents learn to navigate following natural language instructions. The key to this task is to perceive both the visual scene and natural language sequentially. Conventional approaches exploit the vision and language features in crossmodal grounding. However, the VLN task remains challenging, since previous works have neglected the rich semantic information contained in the environment (such as implicit navigation graphs or sub-trajectory semantics). In this paper, we introduce Auxiliary Reasoning Navigation (AuxRN), a framework with four self-supervised auxiliary reasoning tasks to take advantage of the additional training signals derived from the semantic information. The auxiliary tasks have four reasoning objectives: explaining the previous actions, estimating the navigation progress, predicting the next orientation, and evaluating the trajectory consistency. As a result, these additional training signals help the agent to acquire knowledge of semantic representations in order to reason about its activity and build a thorough perception of the environment. Our experiments indicate that auxiliary reasoning tasks improve both the performance of the main task and the model generalizability by a large margin. Empirically, we demonstrate that an agent trained with selfsupervised auxiliary reasoning tasks substantially outperforms the previous state-of-the-art method, being the best existing approach on the standard benchmark1.

关键词： Task analysis Navigation Cognition Trajectory Semantics Training Natural languages

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Toward Stable, Interpretable, and Lightweight Hyperspectral Super-resolution

Toward Stable, Interpretable, and Lightweight Hyperspectral ...

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

作者： Guo, Wen-Jin Xie, Weiying Jiang, Kai Li, Yunsong Lei, Jie Fang, Leyuan Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha Peoples R China

ISBN: (纸本)9798350301298

For real applications, existing HSI-SR methods are not only limited to unstable performance under unknown scenarios but also suffer from high computation consumption. In this paper, we develop a new coordination optimization framework for stable, interpretable, and lightweight HSI-SR. Specifically, we create a positive cycle between fusion and degradation estimation under a new probabilistic framework. The estimated degradation is applied to fusion as guidance for a degradation-aware HSI-SR. Under the framework, we establish an explicit degradation estimation method to tackle the indeterminacy and unstable performance caused by the black-box simulation in previous methods. Considering the interpretability in fusion, we integrate spectral mixing prior into the fusion process, which can be easily realized by a tiny autoencoder, leading to a dramatic release of the computation burden. Based on the spectral mixing prior, we then develop a partial fine-tune strategy to reduce the computation cost further. Comprehensive experiments demonstrate the superiority of our method against the state-of-the-arts under synthetic and real datasets. For instance, we achieve a 2.3 dB promotion on PSNR with 120x model size reduction and 4300x FLOPs reduction under the CAVE dataset. Code is available in https://***/WenjinGuo/DAEM.

关键词： Low-level vision

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Geometric empirical Bayesian model for classification of functional data under diverse sampling regimes

Geometric empirical Bayesian model for classification of fun...

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

作者： Matuk, James Bharath, Karthik Chkrebtii, Oksana Kurtek, Sebastian Ohio State Univ Columbus OH 43210 USA Univ Nottingham Nottingham England

ISBN: (纸本)9781665448994

Functional data analysis (FDA) is focused on various statistical tasks, including inference, for observations that vary over a continuum, which are not effectively addressed by multivariate methods. A feature of these functional observations is the presence of two distinct forms of variability: amplitude that describes differences in magnitudes of features, e.g., extrema, and phase that describes differences in timings of amplitude features. One area of focus in FDA is the classification of new observations based on previously observed training data that has been split into pre-defined classes. Existing methods fail to directly account for both phase and amplitude variability, and work under the restrictive assumption that functional observations are measured on a common, fine grid over the input domain. In this work, we address these issues directly by formulating a Bayesian hierarchical model for irregular, fragmented or sparsely sampled functional observations, where training data from different classes are available. Our approach builds on a recently developed inferential framework for incomplete functional observations and the elastic FDA framework for characterizing amplitude and phase variability. The approach operates by inferring individual parameters that separately track amplitude and phase, which can be combined to infer complete functions underlying each observation, and a class parameter, which can be used to discern the class membership of an observation based on the training data. We validate the proposed framework using simulation studies and real data applications, and showcase the advantages of this perspective when both amplitude and phase variability are present in the data.

关键词： computer vision Phase measurement Computational modeling conferences Training data Data models Bayes methods

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AutoLoss-Zero: Searching Loss Functions from Scratch for Generic Tasks

AutoLoss-Zero: Searching Loss Functions from Scratch for Gen...

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

作者： Li, Hao Fu, Tianwen Dai, Jifeng Li, Hongsheng Huang, Gao Zhu, Xizhou Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Tsinghua Univ Beijing Peoples R China Shanghai Jiao Tong Univ Qing Yuan Res Inst Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Significant progress has been achieved in automating the design of various components in deep networks. However, the automatic design of loss functions for generic tasks with various evaluation metrics remains under-investigated. Previous works on handcrafting loss functions heavily rely on human expertise, which limits their extensibility. Meanwhile, searching for loss functions is nontrivial due to the vast search space. Existing efforts mainly tackle the issue by employing task-specific heuristics on specific tasks and particular metrics. Such work cannot be extended to other tasks without arduous human effort. In this paper, we propose AutoLoss-Zero, which is a general framework for searching loss functions from scratch for generic tasks. Specifically, we design an elementary search space composed only of primitive mathematical operators to accommodate the heterogeneous tasks and evaluation metrics. A variant of the evolutionary algorithm is employed to discover loss functions in the elementary search space. A loss-rejection protocol and a gradient-equivalence-check strategy are developed so as to improve the search efficiency, which are applicable to generic tasks. Extensive experiments on various computer vision tasks demonstrate that our searched loss functions are on par with or superior to existing loss functions, which generalize well to different datasets and networks. Code shall be released.

关键词： computer vision Protocols Codes Evolutionary computation Extraterrestrial measurements pattern recognition Task analysis

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Attribute-aware Face Aging with Wavelet-based Generative Adversarial Networks 32

Attribute-aware Face Aging with Wavelet-based Generative Adv...

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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Liu, Yunfan Li, Qi Sun, Zhenan CASIA Ctr Res Intelligent Percept & Comp Beijing Peoples R China CASIA Natl Lab Pattern Recognit Beijing Peoples R China Chinese Acad Sci Artificial Intelligence Res Qingdao Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing Peoples R China

ISBN: (纸本)9781728132938

Since it is difficult to collect face images of the same subject over a long range of age span, most existing face aging methods resort to unpaired datasets to learn age mappings. However, the matching ambiguity between young and aged face images inherent to unpaired training data may lead to unnatural changes official attributes during the aging process, which could not be solved by only enforcing identity consistency like most existing studies do. In this paper we propose an attribute-aware face aging model with wavelet-based Generative Adversarial Networks (GANs) to address the above issues. To be specific, we embed facial attribute vectors into both the generator and discriminator of the model to encourage each synthesized elderly face image to be faithful to the attribute of its corresponding input. In addition, a wavelet packet transform (WPT) module is incorporated to improve the visual fidelity of generated images by capturing age-related texture details at multiple scales in the frequency space. Qualitative results demonstrate the ability of our model in,synthesizing visually plausible face images, and extensive quantitative evaluation results show that the proposed method achieves state-of:the-art performance on existing datasets.

关键词： And Body Pose Biometrics Face Gesture

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Nighttime Image Dehazing Based on Variational Decomposition Model

Nighttime Image Dehazing Based on Variational Decomposition ...

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

作者： Liu, Yun Yan, Zhongsheng Wu, Aimin Ye, Tian Li, Yuche Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China Chongqing Coll Int Business & Econ Coll Big Data & Intelligent Engn Chongqing 401520 Peoples R China Jimei Univ Sch Ocean Informat Engn Xiamen 361021 Peoples R China China Univ Petr Coll Geosci Beijing 102249 Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Most of existing dehazing algorithms are unable to deal with nighttime hazy scenarios well due to complex degraded factors such as non-uniform illumination, low light and glows. To obtain high-quality image under nighttime haze imaging conditions, we present an effective single nighttime image dehazing framework based on a variational decomposition model to simultaneously address these undesirable issues. First, a variational decomposition model consisting of three regularization terms is proposed to simultaneously decompose a nighttime hazy image into a structure layer, a detail layer and a noise layer. Concretely, we employ l(1) norm to constrain the structure component, adopt l(0) sparsity term to enforce the piece-wise continuous of the detail layer, and use l(2) norm to separate the noise layer. Next, the structure layer is recovered by means of inversing the physical model and the detail layers are revealed in a multi-scale gradient enhancement manner. Finally, the dehazed structure layer and the enhanced detail layers are integrated into a haze-free image. Experimental results show that the proposed framework achieves superior performance on nighttime haze removal and noise suppression compared with several state-of-the-art dehazing techniques.

关键词： Degradation computer vision conferences Computational modeling Noise reduction Lighting Focusing

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InternVL: Scaling up vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks

InternVL: Scaling up Vision Foundation Models and Aligning f...

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

作者： Chen, Zhe Wu, Jiannan Wang, Wenhai Su, Weijie Chen, Guo Xing, Sen Zhong, Muyan Zhang, Qinglong Zhu, Xizhou Lu, Lewei Li, Bin Luo, Ping Lu, Tong Qiao, Yu Dai, Jifeng Shanghai AI Lab OpenGVLab Shanghai Peoples R China Nanjing Univ Nanjing Peoples R China Univ Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Tsinghua Univ Beijing Peoples R China Univ Sci & Technol China Hefei Peoples R China SenseTime Res Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350353006

The exponential growth of large language models (LLMs) has opened up numerous possibilities for multi-modal AGI systems. However, the progress in vision and vision-language foundation models, which are also critical elements of multi-modal AGI, has not kept pace with LLMs. In this work, we design a large-scale vision-language foundation model (InternVL), which scales up the vision foundation model to 6 billion parameters and progressively aligns it with the LLM, using web-scale image-text data from various sources. This model can be broadly applied to and achieve state-of-the-art performance on 32 generic visual-linguistic benchmarks including visual perception tasks such as image-level or pixel-level recognition, vision-language tasks such as zero-shot image/video classification, zero-shot image/video-text retrieval, and link with LLMs to create multi-modal dialogue systems. It has powerful visual capabilities and can be a good alternative to the ViT-22B. We hope that our research could contribute to the development of multi-modal large models.

关键词： multi-modal vision foundation model vision-language model

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