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Deep Frequency Filtering for Domain Generalization

Deep Frequency Filtering for Domain Generalization

引用

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

作者： Lin, Shiqi Zhang, Zhizheng Huang, Zhipeng Lu, Yan Lan, Cuiling Chu, Peng You, Quanzeng Wang, Jiang Liu, Zicheng Parulkar, Amey Navkal, Viraj Chen, Zhibo Univ Sci & Technol China Hefei Peoples R China Microsoft Albuquerque NM USA Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

Improving the generalization ability of Deep Neural Networks (DNNs) is critical for their practical uses, which has been a longstanding challenge. Some theoretical studies have uncovered that DNNs have preferences for some frequency components in the learning process and indicated that this may affect the robustness of learned features. In this paper, we propose Deep Frequency Filtering (DFF) for learning domain-generalizable features, which is the first endeavour to explicitly modulate the frequency components of different transfer difficulties across domains in the latent space during training. To achieve this, we perform Fast Fourier Transform (FFT) for the feature maps at different layers, then adopt a light-weight module to learn attention masks from the frequency representations after FFT to enhance transferable components while suppressing the components not conducive to generalization. Further, we empirically compare the effectiveness of adopting different types of attention designs for implementing DFF. Extensive experiments demonstrate the effectiveness of our proposed DFF and show that applying our DFF on a plain baseline outperforms the state-of-the-art methods on different domain generalization tasks, including close-set classification and open-set retrieval.

关键词： detection recognition: Categorization retrieval

来源：评论

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Switchable Representation Learning Framework with Self-compatibility

Switchable Representation Learning Framework with Self-compa...

引用

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

作者： Wu, Shengsen Bai, Yan Lou, Yihang Linghu, Xiongkun He, Jianzhong Duan, Ling-Yu Peng Cheng Lab Shenzhen Peoples R China Peking Univ Beijing Peoples R China Huawei Technol Shenzhen Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Real-world visual search systems involve deployments on multiple platforms with different computing and storage resources. Deploying a unified model that suits the minimal-constrain platforms leads to limited accuracy. It is expected to deploy models with different capacities adapting to the resource constraints, which requires features extracted by these models to be aligned in the metric space. The method to achieve feature alignments is called "compatible learning". Existing research mainly focuses on the one-to-one compatible paradigm, which is limited in learning compatibility among multiple models. We propose a Switchable representation learning Framework with Self-Compatibility (SFSC). SFSC generates a series of compatible sub-models with different capacities through one training process. The optimization of sub-models faces gradients conflict, and we mitigate this problem from the perspective of the magnitude and direction. We adjust the priorities of sub-models dynamically through uncertainty estimation to co-optimize sub-models properly. Besides, the gradients with conflicting directions are projected to avoid mutual interference. SFSC achieves state-of-the-art performance on the evaluated datasets.

关键词： detection recognition: Categorization retrieval

来源：评论

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Detection Hub: Unifying Object Detection Datasets via Query Adaptation on Language Embedding

Detection Hub: Unifying Object Detection Datasets via Query ...

引用

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

作者： Meng, Lingchen Dai, Xiyang Chen, Yinpeng Zhang, Pengchuan Chen, Dongdong Liu, Mengchen Wang, Jianfeng Wu, Zuxuan Yuan, Lu Jiang, Yu-Gang Fudan Univ Shanghai Key Lab Intell Info Proc Sch CS Shanghai Peoples R China Shanghai Collaborat Innovat Ctr Intelligent Visua Shanghai Peoples R China Microsoft Redmond WA USA

ISBN: (纸本)9798350301298

Combining multiple datasets enables performance boost on many computer vision tasks. But similar trend has not been witnessed in object detection when combining multiple datasets due to two inconsistencies among detection datasets: taxonomy difference and domain gap. In this paper, we address these challenges by a new design (named Detection Hub) that is dataset-aware and category-aligned. It not only mitigates the dataset inconsistency but also provides coherent guidance for the detector to learn across multiple datasets. In particular, the dataset-aware design is achieved by learning a dataset embedding that is used to adapt object queries as well as convolutional kernels in detection heads. The categories across datasets are semantically aligned into a unified space by replacing one-hot category representations with word embedding and leveraging the semantic coherence of language embedding. Detection Hub fulfills the benefits of large data on object detection. Experiments demonstrate that joint training on multiple datasets achieves significant performance gains over training on each dataset alone. Detection Hub further achieves SoTA performance on UODB benchmark with wide variety of datasets.

关键词： detection recognition: Categorization retrieval

来源：评论

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Structured Sparsity Learning for Efficient Video Super-Resolution

Structured Sparsity Learning for Efficient Video Super-Resol...

引用

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

作者： Xia, Bin He, Jingwen Zhang, Yulun Wang, Yitong Tian, Yapeng Yang, Wenming Van Gool, Luc Tsinghua Univ Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China Swiss Fed Inst Technol Zurich Switzerland ByteDance Inc Beijing Peoples R China Univ Texas Dallas Dallas TX USA

ISBN: (纸本)9798350301298

The high computational costs of video super-resolution (VSR) models hinder their deployment on resource-limited devices, e.g., smartphones and drones. Existing VSR models contain considerable redundant filters, which drag down the inference efficiency. To prune these unimportant filters, we develop a structured pruning scheme called Structured Sparsity Learning (SSL) according to the properties of VSR. In SSL, we design pruning schemes for several key components in VSR models, including residual blocks, recurrent networks, and upsampling networks. Specifically, we develop a Residual Sparsity Connection (RSC) scheme for residual blocks of recurrent networks to liberate pruning restrictions and preserve the restoration information. For upsampling networks, we design a pixel-shuffle pruning scheme to guarantee the accuracy of feature channel-space conversion. In addition, we observe that pruning error would be amplified as the hidden states propagate along with recurrent networks. To alleviate the issue, we design Temporal Finetuning (TF). Extensive experiments show that SSL can significantly outperform recent methods quantitatively and qualitatively. The code is available at https://***/Zj-BinXia/SSL.

关键词： Low-level vision

来源：评论

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PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow

PyramidFlow: High-Resolution Defect Contrastive Localization...

引用

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

作者： Lei, Jiarui Hu, Xiaobo Wang, Yue Liu, Dong Zhejiang Univ State Key Lab Modern Opt Instrumentat Hangzhou Peoples R China ZJU Hangzhou Global Sci & Technol Innovat Ctr Hangzhou Peoples R China Jiaxing Key Lab Photon Sensing & Intelligent Imag Jiaxing Peoples R China Zhejiang Univ Jiaxing Res Inst Intelligent Opt & Photon Res Ctr Hangzhou Peoples R China

ISBN: (纸本)9798350301298

During industrial processing, unforeseen defects may arise in products due to uncontrollable factors. Although unsupervised methods have been successful in defect localization, the usual use of pre-trained models results in low-resolution outputs, which damages visual performance. To address this issue, we propose PyramidFlow, the first fully normalizing flow method without pre-trained models that enables high-resolution defect localization. Specifically, we propose a latent template-based defect contrastive localization paradigm to reduce intra-class variance, as the pre-trained models do. In addition, PyramidFlow utilizes pyramid-like normalizing flows for multi-scale fusing and volume normalization to help generalization. Our comprehensive studies on MVTecAD demonstrate the proposed method outperforms the comparable algorithms that do not use external priors, even achieving state-of-the-art performance in more challenging BTAD scenarios.

关键词： Low-level vision

来源：评论

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HIER: Metric Learning Beyond Class Labels via Hierarchical Regularization

HIER: Metric Learning Beyond Class Labels via Hierarchical R...

引用

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

作者： Kim, Sungyeon Jeong, Boseung Kwak, Suha POSTECH Dept CSE Pohang South Korea POSTECH Grad Sch AI Pohang South Korea

ISBN: (纸本)9798350301298

Supervision for metric learning has long been given in the form of equivalence between human-labeled classes. Although this type of supervision has been a basis of metric learning for decades, we argue that it hinders further advances in the field. In this regard, we propose a new regularization method, dubbed HIER, to discover the latent semantic hierarchy of training data, and to deploy the hierarchy to provide richer and more fine-grained supervision than inter-class separability induced by common metric learning losses. HIER achieves this goal with no annotation for the semantic hierarchy but by learning hierarchical proxies in hyperbolic spaces. The hierarchical proxies are learnable parameters, and each of them is trained to serve as an ancestor of a group of data or other proxies to approximate the semantic hierarchy among them. HIER deals with the proxies along with data in hyperbolic space since the geometric properties of the space are well-suited to represent their hierarchical structure. The efficacy of HIER is evaluated on four standard benchmarks, where it consistently improved the performance of conventional methods when integrated with them, and consequently achieved the best records, surpassing even the existing hyperbolic metric learning technique, in almost all settings.

关键词： detection recognition: Categorization retrieval

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Modality-invariant Visual Odometry for Embodied vision

Modality-invariant Visual Odometry for Embodied Vision

引用

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

作者： Memmel, Marius Bachmann, Roman Zamir, Amir Univ Washington Seattle WA 98195 USA Swiss Fed Inst Technol EPFL Lausanne Switzerland

ISBN: (纸本)9798350301298

Effectively localizing an agent in a realistic, noisy setting is crucial for many embodied vision tasks. Visual Odometry (VO) is a practical substitute for unreliable GPS and compass sensors, especially in indoor environments. While SLAM-based methods show a solid performance without large data requirements, they are less flexible and robust w.r.t. to noise and changes in the sensor suite compared to learning-based approaches. Recent deep VO models, however, limit themselves to a fixed set of input modalities, e.g., RGB and depth, while training on millions of samples. When sensors fail, sensor suites change, or modalities are intentionally looped out due to available resources, e.g., power consumption, the models fail catastrophically. Furthermore, training these models from scratch is even more expensive without simulator access or suitable existing models that can be fine-tuned. While such scenarios get mostly ignored in simulation, they commonly hinder a model's reusability in real-world applications. We propose a Transformer-based modality-invariant VO approach that can deal with diverse or changing sensor suites of navigation agents. Our model outperforms previous methods while training on only a fraction of the data. We hope this method opens the door to a broader range of real-world applications that can benefit from flexible and learned VO models.

关键词： Embodied vision: Active agents simulation

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Is BERT Blind? Exploring the Effect of vision-and-Language Pretraining on Visual Language Understanding

Is BERT Blind? Exploring the Effect of Vision-and-Language P...

引用

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

作者： Alper, Morris Fiman, Michael Averbuch-Elor, Hadar Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9798350301298

Most humans use visual imagination to understand and reason about language, but models such as BERT reason about language using knowledge acquired during text-only pretraining. In this work, we investigate whether vision-and-language pretraining can improve performance on text-only tasks that involve implicit visual reasoning, focusing primarily on zero-shot probing methods. We propose a suite of visual language understanding (VLU) tasks for probing the visual reasoning abilities of text encoder models, as well as various non-visual natural language understanding (NLU) tasks for comparison. We also contribute a novel zero-shot knowledge probing method, Stroop probing, for applying models such as CLIP to text-only tasks without needing a prediction head such as the masked language modelling head of models like BERT. We show that SOTA multimodally trained text encoders outperform unimodally trained text encoders on the VLU tasks while being under-performed by them on the NLU tasks, lending new context to previously mixed results regarding the NLU capabilities of multimodal models. We conclude that exposure to images during pretraining affords inherent visual reasoning knowledge that is reflected in language-only tasks that require implicit visual reasoning. Our findings bear importance in the broader context of multimodal learning, providing principled guidelines for the choice of text encoders used in such contexts.

关键词： and reasoning language vision

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UMat: Uncertainty-Aware Single Image High Resolution Material Capture

UMat: Uncertainty-Aware Single Image High Resolution Materia...

引用

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

作者： Rodriguez-Pardo, Carlos Dominguez-Elvira, Henar Pascual-Hernandez, David Garces, Elena SEDDI Madrid Spain Univ Rey Juan Carlos Madrid Spain

ISBN: (纸本)9798350301298

We propose a learning-based method to recover normals, specularity, and roughness from a single diffuse image of a material, using microgeometry appearance as our primary cue. Previous methods that work on single images tend to produce over-smooth outputs with artifacts, operate at limited resolution, or train one model per class with little room for generalization. In contrast, in this work, we propose a novel capture approach that leverages a generative network with attention and a U-Net discriminator, which shows outstanding performance integrating global information at reduced computational complexity. We showcase the performance of our method with a real dataset of digitized textile materials and show that a commodity flatbed scanner can produce the type of diffuse illumination required as input to our method. Additionally, because the problem might be illposed -more than a single diffuse image might be needed to disambiguate the specular reflection- or because the training dataset is not representative enough of the real distribution, we propose a novel framework to quantify the model's confidence about its prediction at test time. Our method is the first one to deal with the problem of modeling uncertainty in material digitization, increasing the trustworthiness of the process and enabling more intelligent strategies for dataset creation, as we demonstrate with an active learning experiment.

关键词： vision + graphics

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VILA: Learning Image Aesthetics from User Comments with vision-Language Pretraining

VILA: Learning Image Aesthetics from User Comments with Visi...

引用

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

作者： Ke, Junjie Ye, Keren Yu, Jiahui Wu, Yonghui Milanfar, Peyman Yang, Feng Google Res New York NY 10019 USA

ISBN: (纸本)9798350301298

Assessing the aesthetics of an image is challenging, as it is influenced by multiple factors including composition, color, style, and high-level semantics. Existing image aesthetic assessment (IAA) methods primarily rely on human-labeled rating scores, which oversimplify the visual aesthetic information that humans perceive. Conversely, user comments offer more comprehensive information and are a more natural way to express human opinions and preferences regarding image aesthetics. In light of this, we propose learning image aesthetics from user comments, and exploring vision-language pretraining methods to learn multimodal aesthetic representations. Specifically, we pretrain an image-text encoder-decoder model with image-comment pairs, using contrastive and generative objectives to learn rich and generic aesthetic semantics without human labels. To efficiently adapt the pretrained model for downstream IAA tasks, we further propose a lightweight rank-based adapter that employs text as an anchor to learn the aesthetic ranking concept. Our results show that our pretrained aesthetic vision-language model outperforms prior works on image aesthetic captioning over the AVA-Captions dataset, and it has powerful zero-shot capability for aesthetic tasks such as zero-shot style classification and zero-shot IAA, surpassing many supervised baselines. With only minimal fine-tuning parameters using the proposed adapter module, our model achieves state-of-the-art IAA performance over the AVA dataset.(1)

关键词： Computational imaging

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