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On Generalizing Beyond Domains in Cross-Domain Continual Learning

On Generalizing Beyond Domains in Cross-Domain Continual Lea...

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

作者： Simon, Christian Faraki, Masoud Tsai, Yi-Hsuan Yu, Xiang Schulter, Samuel Suh, Yumin Harandi, Mehrtash Chandraker, Manmohan Australian Natl Univ Canberra ACT Australia NEC Labs Amer Princeton NJ USA Monash Univ Melbourne Vic Australia Univ Calif San Diego San Diego CA USA Data61 Sydney NSW Australia Phiar Technol Redwood City CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Humans have the ability to accumulate knowledge of new tasks in varying conditions, but deep neural networks often suffer from catastrophic forgetting of previously learned knowledge after learning a new task. Many recent methods focus on preventing catastrophic forgetting under the assumption of train and test data following similar distributions. In this work, we consider a more realistic scenario of continual learning under domain shifts where the model must generalize its inference to an unseen domain. To this end, we encourage learning semantically meaningful features by equipping the classifier with class similarity metrics as learning parameters which are obtained through Mahalanobis similarity computations. Learning of the backbone representation along with these extra parameters is done seamlessly in an end-to-end manner. In addition, we propose an approach based on the exponential moving average of the parameters for better knowledge distillation. We demonstrate that, to a great extent, existing continual learning algorithms fail to handle the forgetting issue under multiple distributions, while our proposed approach learns new tasks under domain shift with accuracy boosts up to 10% on challenging datasets such as DomainNet and OfficeHome.

关键词： Knowledge engineering Measurement Deep learning computer vision Computational modeling Neural networks pattern recognition

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PromptSync: Bridging Domain Gaps in vision-Language Models through Class-Aware Prototype Alignment and Discrimination

PromptSync: Bridging Domain Gaps in Vision-Language Models t...

引用

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

作者： Khandelwal, Anant Glance AI Bangalore Karnataka India

ISBN: (纸本)9798350365474

The potential for zero-shot generalization in vision-language (V-L) models such as CLIP has spurred their widespread adoption in addressing numerous downstream tasks. Previous methods have employed test-time prompt tuning to adapt the model to unseen domains, but they overlooked the issue of imbalanced class distributions. In this study, we explicitly address this problem by employing class-aware prototype alignment weighted by mean class probabilities obtained for the test sample and filtered augmented views. Additionally, we ensure that the class probabilities are as accurate as possible by performing prototype discrimination using contrastive learning. The combination of alignment and discriminative loss serves as a geometric regularizer, preventing the prompt representation from collapsing onto a single class and effectively bridging the distribution gap between the source and test domains. Our method, named PromptSync, synchronizes the prompts for each test sample on both the text and vision branches of the V-L model. In empirical evaluations on the domain generalization benchmark, our method outperforms previous best methods by 2.33% in overall performance, by 1% in base-to-novel generalization, and by 2.84% in cross-dataset transfer tasks.

关键词： Visual languages

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You Do Not Need Additional Priors or Regularizers in Retinex-based Low-light Image Enhancement

You Do Not Need Additional Priors or Regularizers in Retinex...

引用

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

作者： Fu, Huiyuan Zheng, Wenkai Meng, Xiangyu Wang, Xin Wang, Chuanming Ma, Huadong Beijing Univ Posts & Telecommun Beijing Peoples R China SUNY Stony Brook Stony Brook NY USA

ISBN: (纸本)9798350301298

Images captured in low-light conditions often suffer from significant quality degradation. Recent works have built a large variety of deep Retinex-based networks to enhance low-light images. The Retinex-based methods require decomposing the image into reflectance and illumination components, which is a highly ill-posed problem and there is no available ground truth. Previous works addressed this problem by imposing some additional priors or regularizers. However, finding an effective prior or regularizer that can be applied in various scenes is challenging, and the performance of the model suffers from too many additional constraints. We propose a contrastive learning method and a self-knowledge distillation method for Retinex decomposition that allow training our Retinex-based model without elaborate hand-crafted regularization functions. Rather than estimating reflectance and illuminance images and representing the final images as their element-wise products as in previous works, our regularizer-free Retinex decomposition and synthesis network (RFR) extracts reflectance and illuminance features and synthesizes them end-to-end. In addition, we propose a loss function for contrastive learning and a progressive learning strategy for self-knowledge distillation. Extensive experimental results demonstrate that our proposed methods can achieve superior performance compared with state-of-the-art approaches.

关键词： Low-level vision

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Perceptual in-Loop Filter for Image and Video Compression

Perceptual in-Loop Filter for Image and Video Compression

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

作者： Wang, Huairui Ren, Guangjie Ouyang, Tong Zhang, Junxi Han, Wenwei Liu, Zizheng Chen, Zhenzhong Wuhan Univ Wuhan Peoples R China Tencent Media Lab Shenzhen Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we introduce our hybrid image and video compression scheme enhanced by CNN-optimized in-loop filter. Specifically, a Structure Preserving in-Loop Filter (SPiLF) is incorporated in the hybrid video codec Enhanced Compression Model (ECM), where two branches, i.e., gradient branch and pixel branch, are developed based on the dense residual unit (DRU). To provide pleasant visual quality, the Generative adversarial networks (GAN) loss and LPIPS loss are further considered. Therefore, the proposal is mainly focusing on perceptual-friendly image compression for human vision, whilst video compression could be further investigated. The experiments show that the proposed method achieves advanced visual quality when compared to the traditional methods.

关键词： Visualization computer vision Image coding conferences Focusing Video compression Generative adversarial networks

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Progressive Transformation Learning for Leveraging Virtual Images in Training

Progressive Transformation Learning for Leveraging Virtual I...

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

作者： Shen, Yi-Ting Lee, Hyungtae Kwon, Heesung Bhattacharyya, Shuvra S. Univ Maryland College Pk MD USA DEVCOM Army Res Lab Adelphi MD USA

ISBN: (纸本)9798350301298

To effectively interrogate UAV-based images for detecting objects of interest, such as humans, it is essential to acquire large-scale UAV-based datasets that include human instances with various poses captured from widely varying viewing angles. As a viable alternative to laborious and costly data curation, we introduce Progressive Transformation Learning (PTL), which gradually augments a training dataset by adding transformed virtual images with enhanced realism. Generally, a virtual2real transformation generator in the conditional GAN framework suffers from quality degradation when a large domain gap exists between real and virtual images. To deal with the domain gap, PTL takes a novel approach that progressively iterates the following three steps: 1) select a subset from a pool of virtual images according to the domain gap, 2) transform the selected virtual images to enhance realism, and 3) add the transformed virtual images to the training set while removing them from the pool. In PTL, accurately quantifying the domain gap is critical. To do that, we theoretically demonstrate that the feature representation space of a given object detector can be modeled as a multivariate Gaussian distribution from which the Mahalanobis distance between a virtual object and the Gaussian distribution of each object category in the representation space can be readily computed. Experiments show that PTL results in a substantial performance increase over the baseline, especially in the small data and the cross-domain regime.

关键词： detection recognition: Categorization retrieval

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CoordGAN: Self-Supervised Dense Correspondences Emerge from GANs

CoordGAN: Self-Supervised Dense Correspondences Emerge from ...

引用

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

作者： Mu, Jiteng De Mello, Shalini Yu, Zhiding Vasconcelos, Nuno Wang, Xiaolong Kautz, Jan Liu, Sifei Univ Calif San Diego La Jolla CA 92093 USA Nvidia Santa Clara CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent advances show that Generative Adversarial Networks (GANs) can synthesize images with smooth variations along semantically meaningful latent directions, such as pose, expression, layout, etc. While this indicates that GANs implicitly learn pixel-level correspondences across images, few studies explored how to extract them explicitly. In this work, we introduce Coordinate GAN (CoordGAN), a structure-texture disentangled GAN that learns a dense correspondence map for each generated image. We represent the correspondence maps of different images as warped coordinate frames transformed from a canonical coordinate frame, i.e., the correspondence map, which describes the structure (e.g., the shape of a face), is controlled via a transformation. Hence, finding correspondences boils down to locating the same coordinate in different correspondence maps. In CoordGAN, we sample a transformation to represent the structure of a synthesized instance, while an independent texture branch is responsible for rendering appearance details orthogonal to the structure. Our approach can also extract dense correspondence maps for real images by adding an encoder on top of the generator. We quantitatively demonstrate the quality of the learned dense correspondences through segmentation mask transfer on multiple datasets. We also show that the proposed generator achieves better structure and texture disentanglement compared to existing approaches. Project page: https:/***/CoordGAN/

关键词： Image segmentation computer vision Shape Face recognition Computational modeling Layout Benchmark testing

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RES-PCA: A Scalable Approach to Recovering Low-rank Matrices 32

RES-PCA: A Scalable Approach to Recovering Low-rank Matrices

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

作者： Peng, Chong Chen, Chenglizhao Kang, Zhao Li, Jianbo Cheng, Qiang Qingdao Univ Coll Comp Sci & Technol Qingdao Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Hefei Peoples R China Univ Kentucky Dept Comp Sci Lexington KY 40506 USA

ISBN: (纸本)9781728132938

Robust principal component analysis (RPCA) has drawn significant attentions due to its powerful capability in recovering low-rank matrices as well as successful appplications in various real world problems. The current state-of-the-art algorithms usually need to solve singular-value decomposition of large matrices, which generally has at least a quadratic or even cubic complexity. This drawback has limited the application of RPCA in solving real world problems. To combat this drawback, in this paper we propose a new type of RPCA method, RES-PCA, which is linearly efficient and scalable in both data size and dimension. For comparison purpose,AltProj,an existing scalable approach to RPCA requires the precise knowlwdge of the true rank;otherwise, it may fail to recover low-rank matrices. By contrast, our method works with or without knowing the true rank;even when both methods work, our method is faster Extensive experiments have been performed and testified to the effectiveness of proposed method quantitatively and in visual quality, which suggests that our method is suitable to be employed as a light-weight, scalable component for RPCA in any application pipelines.

关键词： Categorization recognition: Detection Retrieval Statistical Learning

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Privacy-preserving Online AutoML for Domain-Specific Face Detection

Privacy-preserving Online AutoML for Domain-Specific Face De...

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

作者： Yan, Chenqian Zhang, Yuge Zhang, Quanlu Yang, Yaming Jiang, Xinyang Yang, Yuqing Wang, Baoyuan Microsoft Res Beijing Peoples R China Xiaobing Ai Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Despite the impressive progress of general face detection, the tuning of hyper-parameters and architectures is still critical for the performance of a domain-specific face detector. Though existing AutoML works can speedup such process, they either require tuning from scratch for a new scenario or do not consider data privacy. To scale up, we derive a new AutoML setting from a platform perspective. In such setting, new datasets sequentially arrive at the platform, where an architecture and hyper-parameter configuration is recommended to train the optimal face detector for each dataset. This, however, brings two major challenges: (1) how to predict the best configuration for any given dataset without touching their raw images due to the privacy concern? and (2) how to continuously improve the AutoML algorithm from previous tasks and offer a better warm-up for future ones? We introduce "HyperFD", a new privacy-preserving online AutoML framework for face detection. At its core part, a novel meta feature representation of a dataset as well as its learning paradigm is proposed. Thanks to HyperFD, each local task (client) is able to effectively leverage the learning "experience" of previous tasks without uploading raw images to the platform;meanwhile, the meta feature extractor is continuously learned to better trade off the bias and variance. Extensive experiments demonstrate the effectiveness and efficiency of our design.

关键词： Deep learning Data privacy Face recognition computer architecture Detectors Prediction algorithms Face detection

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Modeling sRGB Camera Noise with Normalizing Flows

Modeling sRGB Camera Noise with Normalizing Flows

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

作者： Kousha, Shayan Maleky, Ali Brown, Michael S. Brubaker, Marcus A. York Univ N York ON Canada Vector Inst Toronto ON Canada Samsung AI Ctr Toronto Toronto ON Canada

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Noise modeling and reduction are fundamental tasks in low-level computer vision. They are particularly important for smartphone cameras relying on small sensors that exhibit visually noticeable noise. There has recently been renewed interest in using data-driven approaches to improve camera noise models via neural networks. These data-driven approaches target noise present in the raw-sensor image before it has been processed by the camera's image signal processor (ISP). Modeling noise in the RAW-rgb domain is useful for improving and testing the in-camera denoising algorithm;however, there are situations where the camera's ISP does not apply denoising or additional denoising is desired when the RAW-rgb domain image is no longer available. In such cases, the sensor noise propagates through the ISP to the final rendered image encoded in standard RGB (sRGB). The nonlinear steps on the ISP culminate in a significantly more complex noise distribution in the sRGB domain and existing raw-domain noise models are unable to capture the sRGB noise distribution. We propose a new sRGB-domain noise model based on normalizing flows that is capable of learning the complex noise distribution found in sRGB images under various ISO levels. Our normalizing flows-based approach outperforms other models by a large margin in noise modeling and synthesis tasks. We also show that image denoisers trained on noisy images synthesized with our noise model outperforms those trained with noise from baselines models.

关键词： Training computer vision Computational modeling ISO Noise reduction Cameras Sensors

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RIFormer: Keep Your vision Backbone Effective But Removing Token Mixer

RIFormer: Keep Your Vision Backbone Effective But Removing T...

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

作者： Wang, Jiahao Zhang, Songyang Liu, Yong Wu, Taiqiang Yang, Yujiu Liu, Xihui Chen, Kai Luo, Ping Lin, Dahua Shanghai AI Lab Shanghai Peoples R China Univ HongKong Hong Kong Peoples R China Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China

ISBN: (纸本)9798350301298

This paper studies how to keep a vision backbone effective while removing token mixers in its basic building blocks. Token mixers, as self-attention for vision transformers (ViTs), are intended to perform information communication between different spatial tokens but suffer from considerable computational cost and latency. However, directly removing them will lead to an incomplete model structure prior, and thus brings a significant accuracy drop. To this end, we first develop an RepIdentityFormer base on the re-parameterizing idea, to study the token mixer free model architecture. And we then explore the improved learning paradigm to break the limitation of simple token mixer free backbone, and summarize the empirical practice into 5 guidelines. Equipped with the proposed optimization strategy, we are able to build an extremely simple vision backbone with encouraging performance, while enjoying the high efficiency during inference. Extensive experiments and ablative analysis also demonstrate that the inductive bias of network architecture, can be incorporated into simple network structure with appropriate optimization strategy. We hope this work can serve as a starting point for the exploration of optimization-driven efficient network design.

关键词： Deep learning architectures and techniques

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