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OSRT: Omnidirectional Image Super-Resolution with Distortion-aware Transformer

OSRT: Omnidirectional Image Super-Resolution with Distortion...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Fanghua Yu Xintao Wang Mingdeng Cao Gen Li Ying Shan Chao Dong ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences ARC Tencent PCG The University of Tokyo Platform Technologies Tencent Online Video Shanghai Artificial Intelligence Laboratory

Omnidirectional images (ODIs) have obtained lots of research interest for immersive experiences. Although ODIs require extremely high resolution to capture details of the entire scene, the resolutions of most ODIs are insufficient. Previous methods attempt to solve this issue by image super-resolution (SR) on equirectangular projection (ERP) images. However, they omit geometric properties of ERP in the degradation process, and their models can hardly generalize to real ERP images. In this paper, we propose Fisheye downsampling, which mimics the real-world imaging process and synthesizes more realistic low-resolution samples. Then we design a distortion-aware Transformer (OSRT) to modulate ERP distortions continuously and self-adaptively. Without a cumbersome process, OSRT outperforms previous methods by about 0.2dB on PSNR. Moreover, we propose a convenient data augmentation strategy, which synthesizes pseudo ERP images from plain images. This simple strategy can alleviate the over-fitting problem of large networks and significantly boost the performance of ODISR. Extensive experiments have demonstrated the state-of-the-art performance of our OSRT.

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Are Labels Always Necessary for Classifier Accuracy Evaluation?

Are Labels Always Necessary for Classifier Accuracy Evaluati...

引用

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

作者： Deng, Weijian Zheng, Liang Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9781665445092

To calculate the model accuracy on a computer vision task, e.g., object recognition, we usually require a test set composing of test samples and their ground truth labels. Whilst standard usage cases satisfy this requirement, many real-world scenarios involve unlabeled test data, rendering common model evaluation methods infeasible. We investigate this important and under-explored problem, Automatic model Evaluation (AutoEval). Specifically, given a labeled training set and a classifier, we aim to estimate the classification accuracy on unlabeled test datasets. We construct a meta-dataset: a dataset comprised of datasets generated from the original images via various transformations such as rotation, background substitution, foreground scaling, etc. As the classification accuracy of the model on each sample (dataset) is known from the original dataset labels, our task can be solved via regression. Using the feature statistics to represent the distribution of a sample dataset, we can train regression models (e.g., a regression neural network) to predict model performance. Using synthetic meta-dataset and real-world datasets in training and testing, respectively, we report a reasonable and promising prediction of the model accuracy. We also provide insights into the application scope, limitation, and potential future direction of AutoEval.

关键词： Training computer vision Computational modeling Predictive models Rendering (computer graphics) pattern recognition Object recognition

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InternImage: Exploring Large-Scale vision Foundation Models with Deformable Convolutions

InternImage: Exploring Large-Scale Vision Foundation Models ...

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conference on computer vision and pattern recognition (cvpr)

作者： Wenhai Wang Jifeng Dai Zhe Chen Zhenhang Huang Zhiqi Li Xizhou Zhu Xiaowei Hu Tong Lu Lewei Lu Hongsheng Li Xiaogang Wang Yu Qiao Shanghai AI Laboratory Tsinghua University Nanjing University Sense Time Research The Chinese University of Hong Kong

Compared to the great progress of large-scale vision transformers (ViTs) in recent years, large-scale models based on convolutional neural networks (CNNs) are still in an early state. This work presents a new large-scale CNN-based foundation model, termed InternImage, which can obtain the gain from increasing parameters and training data like ViTs. Different from the recent CNNs that focus on large dense kernels, InternImage takes deformable convolution as the core operator, so that our model not only has the large effective receptive field required for downstream tasks such as detection and segmentation, but also has the adaptive spatial aggregation conditioned by input and task information. As a result, the proposed InternImage reduces the strict inductive bias of traditional CNNs and makes it possible to learn stronger and more robust patterns with large-scale parameters from massive data like ViTs. The effectiveness of our model is proven on challenging benchmarks including ImageNet, COCO, andADE20K. It is worth mentioning that InternImage-H achieved a new record 65.4 mAP on COCO test-dev and 62.9 mIoU on ADE20K, outperforming current leading CNNs and ViTs.

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Towards High Fidelity Face Relighting with Realistic Shadows

Towards High Fidelity Face Relighting with Realistic Shadows

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

作者： Hou, Andrew Zhang, Ze Sarkis, Michel Bi, Ning Tong, Yiying Liu, Xiaoming Michigan State Univ E Lansing MI 48824 USA Qualcomm Technol Inc San Diego CA USA

ISBN: (纸本)9781665445092

Existing face relighting methods often struggle with two problems: maintaining the local facial details of the subject and accurately removing and synthesizing shadows in the relit image, especially hard shadows. We propose a novel deep face relighting method that addresses both problems. Our method learns to predict the ratio (quotient) image between a source image and the target image with the desired lighting, allowing us to relight the image while maintaining the local facial details. During training, our model also learns to accurately modify shadows by using estimated shadow masks to emphasize on the high-contrast shadow borders. Furthermore, we introduce a method to use the shadow mask to estimate the ambient light intensity in an image, and are thus able to leverage multiple datasets during training with different global lighting intensities. With quantitative and qualitative evaluations on the Multi-PIE and FFHQ datasets, we demonstrate that our proposed method faithfully maintains the local facial details of the subject and can accurately handle hard shadows while achieving state-of-the-art face relighting performance.

关键词： Training computer vision Face recognition Lighting Estimation Mouth Data models

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PatchMatch-Based Neighborhood Consensus for Semantic Correspondence

PatchMatch-Based Neighborhood Consensus for Semantic Corresp...

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

作者： Lee, Jae Yong DeGol, Joseph Fragoso, Victor Sinha, Sudipta N. Univ Illinois Chicago IL 60680 USA Microsoft Washington DC USA

ISBN: (纸本)9781665445092

We address estimating dense correspondences between two images depicting different but semantically related scenes. End-to-end trainable deep neural networks incorporating neighborhood consensus cues are currently the best methods for this task. However, these architectures require exhaustive matching and 4D convolutions over matching costs for all pairs of feature map pixels. This makes them computationally expensive. We present a more efficient neighborhood consensus approach based on Patch-Match. For higher accuracy, we propose to use a learned local 4D scoring function for evaluating candidates during the PatchMatch iterations. We have devised an approach to jointly train the scoring function and the feature extraction modules by embedding them into a proxy model which is end-to-end differentiable. The modules are trained in a supervised setting using a cross-entropy loss to directly incorporate sparse keypoint supervision. Our evaluation on PF- PASCAL and SPAIR-71K shows that our method significantly outperforms the state-of-the-art on both datasets while also being faster and using less memory.

关键词： Deep learning computer vision Costs Computational modeling Semantics Memory management Feature extraction

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Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attacks

Protecting Intellectual Property of Generative Adversarial N...

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

作者： Ong, Ding Sheng Chan, Chee Seng Ng, Kam Woh Fan, Lixin Yang, Qiang Univ Malaya Kuala Lumpur Malaysia WeBank AI Lab Kuala Lumpur Malaysia Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Ever since Machine Learning as a Service emerges as a viable business that utilizes deep learning models to generate lucrative revenue, Intellectual Property Right (IPR) has become a major concern because these deep learning models can easily be replicated, shared, and re-distributed by any unauthorized third parties. To the best of our knowledge, one of the prominent deep learning models - Generative Adversarial Networks (GANs) which has been widely used to create photorealistic image are totally unprotected despite the existence of pioneering IPR protection methodology for Convolutional Neural Networks (CNNs). This paper therefore presents a complete protection framework in both black-box and white-box settings to enforce IPR protection on GANs. Empirically, we show that the proposed method does not compromise the original GANs performance (i.e. image generation, image super-resolution, style transfer), and at the same time, it is able to withstand both removal and ambiguity attacks against embedded watermarks. Codes are available at https://***/dingsheng-ong/ipr-gan.

关键词： Deep learning Knowledge engineering computer vision Image synthesis Superresolution Intellectual property Watermarking

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Weakly-supervised Instance Segmentation via Class-agnostic Learning with Salient Images

Weakly-supervised Instance Segmentation via Class-agnostic L...

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

作者： Wang, Xinggang Feng, Jiapei Hu, Bin Ding, Qi Ran, Longjin Chen, Xiaoxin Liu, Wenyu Huazhong Univ Sci & Technol Wuhan Peoples R China VIVO Inc Pleasanton CA USA

ISBN: (纸本)9781665445092

Humans have a strong class-agnostic object segmentation ability and can outline boundaries of unknown objects precisely, which motivates us to propose a box-supervised class-agnostic object segmentation (BoxCaseg) based solution for weakly-supervised instance segmentation. The BoxCaseg model is jointly trained using box-supervised images and salient images in a multi-task learning manner. The fine-annotated salient images provide class-agnostic and precise object localization guidance for box-supervised images. The object masks predicted by a pretrained BoxCaseg model are refined via a novel merged and dropped strategy as proxy ground truth to train a Mask R-CNN for weakly-supervised instance segmentation. Only using 7991 salient images, the weakly-supervised Mask R-CNN is on par with fully-supervised Mask R-CNN on PASCAL VOC and significantly outperforms previous state-of-the-art box-supervised instance segmentation methods on COCO.

关键词： Training Location awareness Learning systems Image segmentation computer vision Codes Merging

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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...

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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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Dynamic Aggregated Network for Gait recognition

Dynamic Aggregated Network for Gait Recognition

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conference on computer vision and pattern recognition (cvpr)

作者： Kang Ma Ying Fu Dezhi Zheng Chunshui Cao Xuecai Hu Yongzhen Huang Beijing Institute of Technology WATRIX.AI Beijing Normal University

Gait recognition is beneficial for a variety of applications, including video surveillance, crime scene investigation, and social security, to mention a few. However, gait recognition often suffers from multiple exterior factors in real scenes, such as carrying conditions, wearing overcoats, and diverse viewing angles. Recently, various deep learning-based gait recognition methods have achieved promising results, but they tend to extract one of the salient features using fixed-weighted convolutional networks, do not well consider the relationship within gait features in key regions, and ignore the aggregation of complete motion patterns. In this paper, we propose a new perspective that actual gait features include global motion patterns in multiple key regions, and each global motion pattern is composed of a series of local motion patterns. To this end, we propose a Dynamic Aggregation Network (DANet) to learn more discriminative gait features. Specifically, we create a dynamic attention mechanism between the features of neighboring pixels that not only adaptively focuses on key regions but also generates more expressive local motion patterns. In addition, we develop a selfattention mechanism to select representative local motion patterns and further learn robust global motion patterns. Extensive experiments on three popular public gait datasets, i.e., CASIA-B, OUMVLP, and Gait3D, demonstrate that the proposed method can provide substantial improvements over the current state-of-the-art methods. 1

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Large-capacity Image Steganography Based on Invertible Neural Networks

Large-capacity Image Steganography Based on Invertible Neura...

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

作者： Lu, Shao-Ping Wang, Rong Zhong, Tao Rosin, Paul L. Nankai Univ CS TKLNDST Tianjin Peoples R China Cardiff Univ Sch Comp Sci & Informat Cardiff Wales

ISBN: (纸本)9781665445092

Many attempts have been made to hide information in images, where one main challenge is how to increase the payload capacity without the container image being detected as containing a message. In this paper, we propose a large-capacity Invertible Steganography Network (ISN) for image steganography. We take steganography and the recovery of hidden images as a pair of inverse problems on image domain transformation, and then introduce the forward and backward propagation operations of a single invertible network to leverage the image embedding and extracting problems. Sharing all parameters of our single ISN architecture enables us to efficiently generate both the container image and the revealed hidden image(s) with high quality. Moreover, in our architecture the capacity of image steganography is significantly improved by naturally increasing the number of channels of the hidden image branch. Comprehensive experiments demonstrate that with this significant improvement of the steganography payload capacity, our ISN achieves state-of-the-art in both visual and quantitative comparisons.

关键词： Backpropagation Steganography Visualization computer vision Inverse problems Neural networks computer architecture

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