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Thermal Image Super-Resolution Challenge - PBVS 2021

Thermal Image Super-Resolution Challenge - PBVS 2021

引用

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

作者： Rivadeneira, Rafael E. Sappa, Angel D. Vintimilla, Boris X. Nathan, Sabari Kansal, Priya Mehri, Armin Ardakani, Parichehr Behjati Dalal, Anurag Akula, Aparna Sharma, Darshika Pandey, Shashwat Kumar, Basant Yao, Jiaxin Wu, Rongyuan Feng, Kai Li, Ning Zhao, Yongqiang Patel, Heena Chudasama, Vishal Prajapati, Kalpesh Sarvaiya, Anjali Upla, Kishor P. Raja, Kiran Ramachandra, Raghavendra Busch, Christoph Almasri, Feras Vandamme, Thomas Debeir, Olivier Gutierrez, Nolan B. Nguyen, Quan H. Beksi, William J. Escuela Super Politecn Litoral ESPOL Fac Ingn Elect & Comp CIDIS Campus Gustavo Galindo Km 30-5 Via Perimetral Guayaquil Ecuador Comp Vis Ctr Campus UAB Barcelona 08193 Spain Couger Inc Tokyo Japan Cent Sci Instruments Org Chandigarh India Indian Inst Engn Sci & Technol Sibpur India MNNIT Allahabad Prayagraj India Northwestern Polytech Univ Xian Peoples R China SVNIT Surat India NTNU Gjovik Norway Univ Libre Bruxelles Brussels Belgium Univ Texas Arlington Arlington TX 76019 USA

ISBN: (纸本)9781665448994

This paper presents results from the second Thermal Image Super-Resolution (TISR) challenge organized in the framework of the Perception Beyond the Visible Spectrum (PBVS) 2021 workshop. For this second edition, the same thermal image dataset considered during the first challenge has been used;only mid-resolution (MR) and high-resolution (HR) sets have been considered. The dataset consists of 951 training images and 50 testing images for each resolution. A set of 20 images for each resolution is kept aside for evaluation. The two evaluation methodologies proposed for the first challenge are also considered in this opportunity. The first evaluation task consists of measuring the PSNR and SSIM between the obtained SR image and the corresponding ground truth (i.e., the HR thermal image downsampled by four). The second evaluation also consists of measuring the PSNR and SSIM, but in this case, considers the x2 SR obtained from the given MR thermal image;this evaluation is performed between the SR image with respect to the semi-registered HR image, which has been acquired with another camera. The results out-performed those from the first challenge, thus showing an improvement in both evaluation metrics.

关键词： Training Measurement computer vision conferences Superresolution Cameras pattern recognition

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StyleMix: Separating Content and Style for Enhanced Data Augmentation

StyleMix: Separating Content and Style for Enhanced Data Aug...

引用

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

作者： Hong, Minui Choi, Jinwoo Kim, Gunhee Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9781665445092

In spite of the great success of deep neural networks for many challenging classification tasks, the learned networks are vulnerable to overfitting and adversarial attacks. Recently, mixup based augmentation methods have been actively studied as one practical remedy for these drawbacks. However, these approaches do not distinguish between the content and style features of the image, but mix or cut-andpaste the images. We propose StyleMix and StyleCutMix as the first mixup method that separately manipulates the content and style information of input image pairs. By carefully mixing up the content and style of images, we can create more abundant and robust samples, which eventually enhance the generalization of model training. We also develop an automatic scheme to decide the degree of style mixing according to the pair's class distance, to prevent messy mixed images from too differently styled pairs. Our experiments on CIFAR-10, CIFAR-100 and ImageNet datasets show that StyleMix achieves better or comparable performance to state of the art mixup methods and learns more robust classifiers to adversarial attacks.

关键词： Training Deep learning computer vision Head Image color analysis Skin Robustness

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Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs

Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs

引用

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

作者： Wang, Hui-Po Yu, Ning Fritz, Mario CISPA Helmholtz Ctr Informat Secur Saarbrucken Germany Max Planck Inst Informat Saarland Informat Campus Saarbrucken Germany Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781665445092

While Generative Adversarial Networks (GANs) show increasing performance and the level of realism is becoming indistinguishable from natural images, this also comes with high demands on data and computation. We show that state-of-the-art GAN models - such as they are being publicly released by researchers and industry - can be used for a range of applications beyond unconditional image generation. We achieve this by an iterative scheme that also allows gaining control over the image generation process despite the highly non-linear latent spaces of the latest GAN models. We demonstrate that this opens up the possibility to re-use state-of-the-art, difficult to train, pre-trained GANs with a high level of control even if only black-box access is granted. Our work also raises concerns and awareness that the use cases of a published GAN model may well reach beyond the creators' intention, which needs to be taken into account before a full public release. Code is available at https://***/a514514772/hijackgan.

关键词： Industries computer vision Codes Image synthesis Computational modeling Process control Aerospace electronics

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Learning Continuous Image Representation with Local Implicit Image Function

Learning Continuous Image Representation with Local Implicit...

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

作者： Chen, Yinbo Liu, Sifei Wang, Xiaolong Univ Calif San Diego La Jolla CA 92093 USA NVIDIA Santa Clara CA USA

ISBN: (纸本)9781665445092

How to represent an image? While the visual world is presented in a continuous manner, machines store and see the images in a discrete way with 2D arrays of pixels. In this paper, we seek to learn a continuous representation for images. Inspired by the recent progress in 3D reconstruction with implicit neural representation, we propose Local Implicit Image Function (LIIF), which takes an image coordinate and the 2D deep features around the coordinate as inputs, predicts the RGB value at a given coordinate as an output. Since the coordinates are continuous, LIIF can be presented in arbitrary resolution. To generate the continuous representation for images, we train an encoder with LIIF representation via a self-supervised task with super-resolution. The learned continuous representation can be presented in arbitrary resolution even extrapolate to x30 higher resolution, where the training tasks are not provided. We further show that LIIF representation builds a bridge between discrete and continuous representation in 2D, it naturally supports the learning tasks with size-varied image ground-truths and significantly outperforms the method with resizing the ground-truths.

关键词： Training Bridges Visualization computer vision Three-dimensional displays Superresolution Image representation

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Information Bottleneck Disentanglement for Identity Swapping

Information Bottleneck Disentanglement for Identity Swapping

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

作者： Gao, Gege Huang, Huaibo Fu, Chaoyou Li, Zhaoyang He, Ran Univ Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Sch Artificial Intelligence Natl Lab Pattern RecognitCASIACAS Beijing Peoples R China

ISBN: (纸本)9781665445092

Improving the performance of face forgery detectors often requires more identity-swapped images of higher-quality. One core objective of identity swapping is to generate identity-discriminative faces that are distinct from the target while identical to the source. To this end, properly disentangling identity and identity-irrelevant information is critical and remains a challenging endeavor. In this work, we propose a novel information disentangling and swapping network, called InfoSwap, to extract the most expressive information for identity representation from a pre-trained face recognition model. The key insight of our method is to formulate the learning of disentangled representations as optimizing an information bottleneck tradeoff, in terms of finding an optimal compression of the pretrained latent features. Moreover, a novel identity contrastive loss is proposed for further disentanglement by requiring a proper distance between the generated identity and the target. While the most prior works have focused on using various loss functions to implicitly guide the learning of representations, we demonstrate that our model can provide explicit supervision for learning disentangled representations, achieving impressive performance in generating more identity-discriminative swapped faces.

关键词： computer vision Face recognition Buildings Detectors Benchmark testing Feature extraction Forgery

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Robustness of Visual Explanations to Common Data Augmentation Methods

Robustness of Visual Explanations to Common Data Augmentatio...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Lenka Tĕtková Lars Kai Hansen Department of Applied Mathematics and Computer Science Technical University of Denmark Lyngby Denmark

As the use of deep neural networks continues to grow, understanding their behaviour has become more crucial than ever. Post-hoc explainability methods are a potential solution, but their reliability is being called into question. Our research investigates the response of post-hoc visual explanations to naturally occurring transformations, often referred to as augmentations. We anticipate explanations to be invariant under certain transformations, such as changes to the colour map while responding in an equivariant manner to transformations like translation, object scaling, and rotation. We have found remarkable differences in robustness depending on the type of transformation, with some explainability methods (such as LRP composites and Guided Backprop) being more stable than others. We also explore the role of training with data augmentation. We provide evidence that explanations are typically less robust to augmentation than classification performance, regardless of whether data augmentation is used in training or not.

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Spatially Consistent Representation Learning

Spatially Consistent Representation Learning

引用

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

作者： Roh, Byungseok Shin, Wuhyun Kim, Ildoo Kim, Sungwoong Kakao Brain Seoul South Korea

ISBN: (纸本)9781665445092

Self-supervised learning has been widely used to obtain transferrable representations from unlabeled images. Especially, recent contrastive learning methods have shown impressive performances on downstream image classification tasks. While these contrastive methods mainly focus on generating invariant global representations at the image-level under semantic-preserving transformations, they are prone to overlook spatial consistency of local representations and therefore have a limitation in pretraining for localization tasks such as object detection and instance segmentation. Moreover, aggressively cropped views used in existing contrastive methods can minimize representation distances between the semantically different regions of a single image. In this paper, we propose a spatially consistent representation learning algorithm (SCRL) for multi-object and location-specific tasks. In particular, we devise a novel self-supervised objective that tries to produce coherent spatial representations of a randomly cropped local region according to geometric translations and zooming operations. On various downstream localization tasks with benchmark datasets, the proposed SCRL shows significant performance improvements over the image-level supervised pretraining as well as the state-of-the-art self-supervised learning methods.

关键词： Location awareness Learning systems Image segmentation computer vision Codes Object detection Benchmark testing

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LPSNet: A lightweight solution for fast panoptic segmentation

LPSNet: A lightweight solution for fast panoptic segmentatio...

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

作者： Hong, Weixiang Guo, Qingpei Zhang, Wei Chen, Jingdong Chu, Wei Ant Financial Serv Grp Hangzhou Peoples R China

ISBN: (纸本)9781665445092

Panoptic segmentation is a challenging task aiming to simultaneously segment objects (things) at instance level and background contents (stuff) at semantic level. Existing methods mostly utilize a two-stage detection network to attain instance segmentation results, and a fully convolutional network to produce a semantic segmentation prediction. Post-processing or additional modules are required to handle the conflicts between the outputs from these two nets, which makes such methods suffer from low efficiency, heavy memory consumption and complicated implementation. To simplify the pipeline and decrease computation/memory cost, we propose an one-stage approach called Lightweight Panoptic Segmentation Network (LPSNet), which does not involve a proposal, anchor or mask head. Instead, we predict a bounding box and semantic category at each pixel upon the feature map produced by an augmented feature pyramid, and design a parameter-free head to merge the per-pixel bounding box and semantic prediction into panoptic segmentation output. Our LPSNet is not only efficient in computation and memory, but also accurate in panoptic segmentation. Comprehensive experiments on COCO, Cityscapes and Mapillary Vistas datasets demonstrate the promising effectiveness and efficiency of the proposed LP-SNet.

关键词： computer vision Costs Semantics Memory management Pipelines Object detection Real-time systems

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UnsupervisedR&R: Unsupervised Point Cloud Registration via Differentiable Rendering

UnsupervisedR&R: Unsupervised Point Cloud Registration via D...

引用

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

作者： El Banani, Mohamed Gao, Luya Johnson, Justin Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781665445092

Aligning partial views of a scene into a single whole is essential to understanding one's environment and is a key component of numerous robotics tasks such as SLAM and SfM. Recent approaches have proposed end-to-end systems that can outperform traditional methods by leveraging pose supervision. However, with the rising prevalence of cameras with depth sensors, we can expect a new stream of raw RGB-D data without the annotations needed for supervision. We propose UnsupervisedR&R: an end-to-end unsupervised approach to learning point cloud registration from raw RGB-D video. The key idea is to leverage differentiable alignment and rendering to enforce photometric and geometric consistency between frames. We evaluate our approach on indoor scene datasets and find that we outperform existing traditional approaches with classical and learned descriptors while being competitive with supervised geometric point cloud registration approaches.

关键词： Training Simultaneous localization and mapping Robot vision systems Feature extraction Rendering (computer graphics) Cameras Sensors

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Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency

Weakly Supervised Instance Segmentation for Videos with Temp...

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

作者： Liu, Qing Ramanathan, Vignesh Mahajan, Dhruv Yuille, Alan Yang, Zhenheng Johns Hopkins Univ Baltimore MD 21218 USA Facebook Menlo Pk CA USA

ISBN: (纸本)9781665445092

Weakly supervised instance segmentation reduces the cost of annotations required to train models. However, existing approaches which rely only on image-level class labels predominantly suffer from errors due to (a) partial segmentation of objects and (b) missing object predictions. We show that these issues can be better addressed by training with weakly labeled videos instead of images. In videos, motion and temporal consistency of predictions across frames provide complementary signals which can help segmentation. We are the first to explore the use of these video signals to tackle weakly supervised instance segmentation. We propose two ways to leverage this information in our model. First, we adapt inter-pixel relation network (IRN) [1] to effectively incorporate motion information during training. Second, we introduce a new MaskConsist module, which addresses the problem of missing object instances by transferring stable predictions between neighboring frames during training. We demonstrate that both approaches together improve the instance segmentation metric AP(50) on video frames of two datasets: Youtube-VIS and Cityscapes by 5% and 3% respectively.

关键词： Training Measurement Image segmentation computer vision Costs Motion segmentation Computational modeling

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