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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition"

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HINet: Half Instance Normalization Network for Image Restoration

HINet: Half Instance Normalization Network for Image Restora...

引用

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

作者： Chen, Liangyu Lu, Xin Zhang, Jie Chu, Xiaojie Chen, Chengpeng MEGVII Technol Beijing Peoples R China Fudan Univ Shanghai Peoples R China Peking Univ Beijing Peoples R China

ISBN: (纸本)9781665448994

In this paper, we explore the role of Instance Normalization in low-level vision tasks. Specifically, we present a novel block: Half Instance Normalization Block (HIN Block), to boost the performance of image restoration networks. Based on HIN Block, we design a simple and powerful multi-stage network named HINet, which consists of two subnetworks. With the help of HIN Block, HINet surpasses the state-of-the-art (SOTA) on various image restoration tasks. For image denoising, we exceed it 0.11dB and 0.28 dB in PSNR on SIDD dataset, with only 7.5% and 30% of its multiplier-accumulator operations (MACs), 6.8 x and 2.9x speedup respectively. For image deblurring, we get comparable performance with 22.5% of its MACs and 3.3 x speedup on REDS and GoPro datasets. For image deraining, we exceed it by 0.3 dB in PSNR on the average result of multiple datasets with 1.4x speedup. With HINet, we won the 1st place on the NTIRE 2021 Image Deblurring Challenge - Track2. JPEG Artifacts, with a PSNR of 29.70.

关键词： Visualization computer vision conferences Transform coding Image restoration pattern recognition Task analysis

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Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations

Continual Semantic Segmentation via Repulsion-Attraction of ...

引用

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

作者： Michieli, Umberto Zanuttigh, Pietro Univ Padua Dept Informat Engn Padua Italy

ISBN: (纸本)9781665445092

Deep neural networks suffer from the major limitation of catastrophic forgetting old tasks when learning new ones. In this paper we focus on class incremental continual learning in semantic segmentation, where new categories are made available over time while previous training data is not retained. The proposed continual learning scheme shapes the latent space to reduce forgetting whilst improving the recognition of novel classes. Our framework is driven by three novel components which we also combine on top of existing techniques effortlessly. First, prototypes matching enforces latent space consistency on old classes, constraining the encoder to produce similar latent representation for previously seen classes in the subsequent steps. Second, features sparsification allows to make room in the latent space to accommodate novel classes. Finally, contrastive learning is employed to cluster features according to their semantics while tearing apart those of different classes. Extensive evaluation on the Pascal VOC2012 and ADE20K datasets demonstrates the effectiveness of our approach, significantly outperforming state-of-the-art methods.

关键词： Deep learning computer vision Limiting Shape Semantics Prototypes Training data

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MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection

MeGA-CDA: Memory Guided Attention for Category-Aware Unsuper...

引用

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

作者： Vibashan, V. S. Gupta, Vikram Oza, Poojan Sindagi, Vishwanath A. Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21218 USA Mercedes Benz Res & Dev India Bengaluru India

ISBN: (纸本)9781665445092

Existing approaches for unsupervised domain adaptive object detection perform feature alignment via adversarial training. While these methods achieve reasonable improvements in performance, they typically perform category-agnostic domain alignment, thereby resulting in negative transfer of features. To overcome this issue, in this work, we attempt to incorporate category information into the domain adaptation process by proposing Memory Guided Attention for Category-Aware Domain Adaptation (MeGA-CDA). The proposed method consists of employing category-wise discriminators to ensure category-aware feature alignment for learning domain-invariant discriminative features. However, since the category information is not available for the target samples, we propose to generate memory-guided category-specific attention maps which are then used to route the features appropriately to the corresponding category discriminator. The proposed method is evaluated on several benchmark datasets and is shown to outperform existing approaches.

关键词： Training computer vision Object detection Benchmark testing Feature extraction Routing pattern recognition

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Subjective Quality Optimized Efficient Image Compression

Subjective Quality Optimized Efficient Image Compression

引用

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

作者： Wang, Xining Chen, Tong Ma, Zhan Nanjing Univ Vis Lab Nanjing Peoples R China

ISBN: (纸本)9781665448994

In this paper, we propose an efficient image compression framework that is optimized for subjective quality. Our framework is mainly based on the NLAIC (NonLocal Attention Optimized Image Coding) model which applied Variational Autoencoder (VAE) and non-local attention module to end-to-end image compression. This work makes two major contributions to the NLAIC framework. First, our models are optimized for subjective-friendly loss functions rather than conventional MSE (Mean Squared Error) or MS-SSIM (Multiscale Structural Similarity) which was widely used in previous works. Second, we introduce block-based inference mechanism to reduce the running memory consumption of the image compression network, and suggest a partial post-processing step to alleviate block artifacts caused by block-based inference in a lightweight computational fashion. Experiments have proved that the image reconstructed by our method can preserve more texture details than models trained for optimal MSE or MS-SSIM and also present capability for high-throughput decoding.

关键词： Measurement computer vision Image coding Inference mechanisms conferences Memory management Distortion

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Towards Real-World Blind Face Restoration with Generative Facial Prior

Towards Real-World Blind Face Restoration with Generative Fa...

引用

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

作者： Wang, Xintao Li, Yu Zhang, Honglun Shan, Ying Tencent PCG Appl Res Ctr ARC Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios. In this work, we propose GFP-GAN that leverages rich and diverse priors encapsulated in a pretrained face GAN for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. Thanks to the powerful generative facial prior and delicate designs, our GFP-GAN could jointly restore facial details and enhance colors with just a single forward pass, while GAN inversion methods require image-specific optimization at inference. Extensive experiments show that our method achieves superior performance to prior art on both synthetic and real-world datasets.

关键词： Geometry computer vision Art Limiting Image color analysis Face recognition Transforms

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Cross-modal Speaker Verification and recognition: A Multilingual Perspective

Cross-modal Speaker Verification and Recognition: A Multilin...

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

作者： Nawaz, Shah Saeed, Muhammad Saad Morerio, Pietro Mahmood, Arif Gallo, Ignazio Yousaf, Muhammad Haroon Del Bue, Alessio Ist Italiano Tecnol IIT Pattern Anal & Comp Vis PAVIS Genoa Italy Ist Italiano Tecnol IIT Visual Geometry & Modelling VGM Genoa Italy Univ Insubria Varese VA Italy Univ Engn & Technol Taxila Rawalpindi Punjab India Informat Technol Univ Lahore Pakistan

ISBN: (纸本)9781665448994

Recent years have seen a surge in finding association between faces and voices within a cross-modal biometric application along with speaker recognition. Inspired from this, we introduce a challenging task in establishing association between faces and voices across multiple languages spoken by the same set of persons. The aim of this paper is to answer two closely related questions: "Is face-voice association language independent?" and "Can a speaker be recognized irrespective of the spoken language?". These two questions are important to understand effectiveness and to boost development of multilingual biometric systems. To answer these, we collected a Multilingual Audio-Visual dataset, containing human speech clips of 154 identities with 3 language annotations extracted from various videos uploaded online. Extensive experiments on the two splits of the proposed dataset have been performed to investigate and answer these novel research questions that clearly point out the relevance of the multilingual problem.

关键词： Training Annotations Face recognition Pipelines Speech recognition Speaker recognition Task analysis

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Kaleido-BERT: vision-Language Pre-training on Fashion Domain

Kaleido-BERT: Vision-Language Pre-training on Fashion Domain

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

作者： Zhuge, Mingchen Gao, Dehong Fan, Deng-Ping Jin, Linbo Chen, Ben Zhou, Haoming Qiu, Minghui Shao, Ling Alibaba Grp Hangzhou Peoples R China Incept Inst AI IIAI Abu Dhabi U Arab Emirates

ISBN: (纸本)9781665445092

We present a new vision-language (VL) pre-training model dubbed Kaleido-BERT, which introduces a novel kaleido strategy for fashion cross-modality representations from transformers. In contrast to random masking strategy of recent VL models, we design alignment guided masking to jointly focus more on image-text semantic relations. To this end, we carry out five novel tasks, i.e., rotation, jigsaw, camouflage, grey-to-color, and blank-to-color for self-supervised VL pre-training at patches of different scale. Kaleido-BERT is conceptually simple and easy to extend to the existing BERT framework, it attains state-of-the-art results by large margins on four downstream tasks, including text retrieval (R@1: 4.03% absolute improvement), image retrieval (R@1: 7.13% abs imv.), category recognition (ACC: 3.28% abs imv.), and fashion captioning (Bleu4: 1.2 abs imv.). We validate the efficiency of Kaleido-BERT on a wide range of e-commerical websites, demonstrating its broader potential in real-world applications.

关键词： computer vision Image recognition Text recognition Semantics Image retrieval Bit error rate Transformers

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Home Action Genome: Cooperative Compositional Action Understanding

Home Action Genome: Cooperative Compositional Action Underst...

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

作者： Rai, Nishant Chen, Haofeng Ji, Jingwei Desai, Rishi Kozuka, Kazuki Ishizaka, Shun Adeli, Ehsan Niebles, Juan Carlos Stanford Univ Stanford CA 94305 USA Panasonic Corp Kadoma Osaka Japan

ISBN: (纸本)9781665445092

Existing research on action recognition treats activities as monolithic events occurring in videos. Recently, the benefits of formulating actions as a combination of atomicactions have shown promise in improving action understanding with the emergence of datasets containing such annotations, allowing us to learn representations capturing this information. However, there remains a lack of studies that extend action composition and leverage multiple viewpoints and multiple modalities of data for representation learning. To promote research in this direction, we introduce Home Action Genome (HOMAGE): a multi-view action dataset with multiple modalities and view-points supplemented with hierarchical activity and atomic action labels together with dense scene composition labels. Leveraging rich multi-modal and multi-view settings, we propose Cooperative Compositional Action Understanding (CCAU), a cooperative learning framework for hierarchical action recognition that is aware of compositional action elements. CCAU shows consistent performance improvements across all modalities. Furthermore, we demonstrate the utility of co-learning compositions in few-shot action recognition by achieving 28.6% mAP with just a single sample.

关键词： Location awareness Learning systems computer vision Annotations Image color analysis Genomics Data visualization

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ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation

ViP-DeepLab: Learning Visual Perception with Depth-aware Vid...

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

作者： Qiao, Siyuan Zhu, Yukun Adam, Hartwig Yuille, Alan Chen, Liang-Chieh Johns Hopkins Univ Baltimore MD 21218 USA Google Res Mountain View CA USA

ISBN: (纸本)9781665445092

In this paper, we present ViP-DeepLab, a unified model attempting to tackle the long-standing and challenging inverse projection problem in vision, which we model as restoring the point clouds from perspective image sequences while providing each point with instance-level semantic interpretations. Solving this problem requires the vision models to predict the spatial location, semantic class, and temporally consistent instance label for each 3D point. ViP-DeepLab approaches it by jointly performing monocular depth estimation and video panoptic segmentation. We name this joint task as Depth-aware Video Panoptic Segmentation, and propose a new evaluation metric along with two derived datasets for it, which will be made available to the public. On the individual sub-tasks, ViP-DeepLab also achieves state-of-the-art results, outperforming previous methods by 5.1% VPQ on Cityscapes-VPS, ranking 1st on the KITTI monocular depth estimation benchmark, and 1st on KITTI MOTS pedestrian. The datasets and the evaluation codes are made publicly available(1).

关键词： Measurement Solid modeling Three-dimensional displays Semantics Estimation Predictive models pattern recognition

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DeepObjStyle: Deep Object-based Photo Style Transfer

DeepObjStyle: Deep Object-based Photo Style Transfer

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

作者： Mastan, Indra Deep Raman, Shanmuganathan Indian Inst Technol Gandhinagar Gandhinagar Gujarat India

ISBN: (纸本)9781665448994

One of the major challenges of style transfer is the appropriate image features supervision between the output image and the input images (style and content). An efficient strategy would be to define an object map between the objects of the style and the content images. However, such a mapping is not well established when there are semantic objects of different types and numbers in the style and the content images. It also leads to content mismatch in the style transfer output, which could reduce the visual quality of the results. We propose an object-based style transfer approach, called DeepObjStyle, for the style supervision in the training data-independent framework. DeepObjStyle preserves the semantics of the objects and achieves better style transfer in the challenging scenario when the style and the content images have a mismatch of image features. We also perform style transfer of images containing a word cloud to demonstrate that DeepObjStyle enables an appropriate image features supervision. We validate the results using quantitative comparisons and user studies.

关键词： Training Visualization computer vision conferences Semantics Tag clouds Quality assessment

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