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CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification

CLIP-Art: Contrastive Pre-training for Fine-Grained Art Clas...

引用

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

作者： Conde, Marcos, V Turgutlu, Kerem Univ Valladolid Valladolid Spain Adobe Mountain View CA USA

ISBN: (纸本)9781665448994

Existing computer vision research in artwork struggles with artwork's fine-grained attributes recognition and lack of curated annotated datasets due to their costly creation. In this work, we use CLIP (Contrastive Language-Image Pre-Training) [12] for training a neural network on a variety of art images and text pairs, being able to learn directly from raw descriptions about images, or if available, curated labels. Model's zero-shot capability allows predicting the most relevant natural language description for a given image, without directly optimizing for the task. Our approach aims to solve 2 challenges: instance retrieval and fine-grained artwork attribute recognition. We use the iMet Dataset [20], which we consider the largest annotated artwork dataset. Our code and models will be available at https://***/KeremTurgutlu/clip_art

关键词： Training computer vision Art Image recognition Databases Natural languages Neural networks

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MTLSegFormer: Multi-task Learning with Transformers for Semantic Segmentation in Precision Agriculture

MTLSegFormer: Multi-task Learning with Transformers for Sema...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Goncalves, Diogo Nunes Marcato, Jose Zamboni, Pedro Pistori, Hemerson Li, Jonathan Nogueira, Keiller Goncalves, Wesley Nunes Federal University of Mato Grosso Do Sul Faculty of Computer Science Av. Costa e Silva Campo Grande79070-900 MS Brazil Federal University of Mato Grosso Do Sul Faculty of Engineering Architecture and Urbanism and Geography Av. Costa e Silva Campo Grande79070-900 MS Brazil Dom Bosco Catholic University INOVISAO Avenida Tamandaré 6000 Campo Grande79117-900 MS Brazil University of Waterloo Department of Geography and Environmental Management WaterlooONN2L 3G1 Canada University of Stirling Scotland StirlingFK9 4LA United Kingdom

ISBN: (纸本)9798350302493

Multi-task learning has proven to be effective in improving the performance of correlated tasks. Most of the existing methods use a backbone to extract initial features with independent branches for each task, and the exchange of information between the branches usually occurs through the concatenation or sum of the feature maps of the branches. However, this type of information exchange does not directly consider the local characteristics of the image nor the level of importance or correlation between the tasks. In this paper, we propose a semantic segmentation method, MTLSegFormer, which combines multi-task learning and attention mechanisms. After the backbone feature extraction, two feature maps are learned for each task. The first map is proposed to learn features related to its task, while the second map is obtained by applying learned visual attention to locally re-weigh the feature maps of the other tasks. In this way, weights are assigned to local regions of the image of other tasks that have greater importance for the specific task. Finally, the two maps are combined and used to solve a task. We tested the performance in two challenging problems with correlated tasks and observed a significant improvement in accuracy, mainly in tasks with high dependence on the others. © 2023 ieee.

关键词： Semantics

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Shadow Removal with Paired and Unpaired Learning

Shadow Removal with Paired and Unpaired Learning

引用

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

作者： Vasluianu, Florin-Alexandru Romero, Andres Van Gool, Luc Timofte, Radu Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781665448994

Shadow removal is an important computer vision task aiming at the detection and successful removal of the shadow produced by an occluded light source and a photorealistic restoration of the image contents. Decades of research produced a multitude of hand-crafted restoration techniques and, more recently, learned solutions from shadowed and shadow free training image pairs. In this work, we propose a single image shadow removal solution via self-supervised learning by using a conditioned mask. We rely on self-supervision and jointly learn deep models to remove and add shadows to images. We derive two variants for learning from paired images and unpaired images, respectively. Our validation on the recently introduced ISTD and USR datasets demonstrate large quantitative and qualitative improvements over the state-of-the-art for both paired and unpaired learning settings.

关键词： Training computer vision conferences Image restoration pattern recognition Task analysis Light sources

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Pixel-Guided Dual-Branch Attention Network for Joint Image Deblurring and Super-Resolution

Pixel-Guided Dual-Branch Attention Network for Joint Image D...

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

作者： Xi, Si Wei, Jia Zhang, Weidong Netease Games AI Lab Hangzhou Peoples R China

ISBN: (纸本)9781665448994

Image deblurring and super-resolution (SR) are computer vision tasks aiming to restore image detail and spatial scale, respectively. Besides, only a few recent works of literature contribute to this task, as conventional methods deal with SR or deblurring separately. We focus on designing a novel Pixel-Guided dual-branch attention network (PDAN) that handles both tasks jointly to address this issue. Then, we propose a novel loss function better focus on large and medium range errors. Extensive experiments demonstrated that the proposed PDAN with the novel loss function not only generates remarkably clear HR images and achieves compelling results for joint image deblurring and SR tasks. In addition, our method achieves second place in NTIRE 2021 Challenge on track 1 of the Image Deblurring Challenge.

关键词： Training computer vision conferences Superresolution Feature extraction Image restoration pattern recognition

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Deep Graphics Encoder for Real-Time Video Makeup Synthesis from Example

Deep Graphics Encoder for Real-Time Video Makeup Synthesis f...

引用

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

作者： Kips, Robin Jiang, Ruowei Ba, Sileye Phung, Edmund Aarabi, Parham Gori, Pietro Perrot, Matthieu Bloch, Isabelle LOreal Res & Innovat Clichy France Inst Polytech Paris Telecom Paris LTCI Paris France Modiface Toronto ON Canada Sorbonne Univ CNRS LIP6 Paris France

ISBN: (纸本)9781665448994

While makeup virtual-try-on is now widespread, parametrizing a computer graphics rendering engine for synthesizing images of a given cosmetics product remains a challenging task. In this paper, we introduce an inverse computer graphics method for automatic makeup synthesis from a reference image, by learning a model that maps an example portrait image with makeup to the space of rendering parameters. This method can be used by artists to automatically create realistic virtual cosmetics image samples, or by consumers, to virtually try-on a makeup extracted from their favorite reference image.

关键词： computer vision conferences Estimation Streaming media Rendering (computer graphics) Real-time systems pattern recognition

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Can we generate real faces from rPPG signals? Probably not

Can we generate real faces from rPPG signals? Probably not

引用

ieee International conference on Pervasive Computing and Communications (PerCom)

作者： Li, Honghan Nhi Nguyen Casado, Constantino Alvarez Wu, Xiaoting Lopez, Miguel Bordallo Osaka Univ Grad Sch Engn Sci Div Bioengn Osaka Japan Univ Oulu Ctr Machine Vision & Signal Anal CMVS Oulu Finland

ISBN: (纸本)9798350304367;9798350304374

The potential of generating authentic human facial images from remote photoplethysmography (rPPG) signals is a compelling idea, with significant implications for biometric authentication and human-computer interaction. This study explores it by using a large-scale dataset to train a diffusion-based generative model, leveraging rPPG signals extracted from facial videos. The initial training phase yields promising results, with the model demonstrating a capacity to synthesize facial likenesses that closely match the corresponding subjects in the training dataset. However, the performance notably falters during validation with an independent dataset, where a marked divergence between generated and actual faces becomes apparent. A subsequent human perception study corroborates this discrepancy. These observations suggest that rPPG signals alone may not be reliable for accurately generating realistic facial imagery.

关键词： Large datasets

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Dissecting the High-Frequency Bias in Convolutional Neural Networks

Dissecting the High-Frequency Bias in Convolutional Neural N...

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

作者： Abello, Antonio A. Hirata Jr, Roberto Wang, Zhangyang Univ Sao Paulo Butanta Sao Paulo SP Brazil Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781665448994

For convolutional neural networks (CNNs), a common hypothesis that explains both their generalization capability and their characteristic brittleness is that these models are implicitly regularized to rely on imperceptible high-frequency patterns, more than humans would do. This hypothesis has seen some empirical validation, but most works do not rigorously divide the image frequency spectrum. We present a model to divide the spectrum in disjointed discs based on the distribution of energy and apply simple feature importance procedures to test whether high-frequencies are more important than lower ones. We find evidence that mid or high-level frequencies are disproportionately important for CNNs. The evidence is robust across different datasets and networks. Moreover, we find the diverse effects of the network's attributes, such as architecture and depth, on frequency bias and robustness in general.

关键词： computer vision conferences computer architecture Frequency conversion Robustness Frequency diversity pattern recognition

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One-Shot GAN: Learning to Generate Samples from Single Images and Videos

One-Shot GAN: Learning to Generate Samples from Single Image...

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

作者： Sushko, Vadim Gall, Juergen Khoreva, Anna Bosch Ctr Artificial Intelligence Stuttgart Germany Univ Bonn Bonn Germany

ISBN: (纸本)9781665448994

Training GANs in low-data regimes remains a challenge, as overfitting often leads to memorization or training divergence. In this work, we introduce One-Shot GAN that can learn to generate samples from a training set as little as one image or one video. We propose a two-branch discriminator, with content and layout branches designed to judge the internal content separately from the scene layout realism. This allows synthesis of visually plausible, novel compositions of a scene, with varying content and layout, while preserving the context of the original sample. Compared to previous single-image GAN models, One-Shot GAN achieves higher diversity and quality of synthesis. It is also not restricted to the single image setting, successfully learning in the introduced setting of a single video.

关键词： Training Visualization computer vision conferences Computational modeling Layout Tools

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ReMP: Rectified Metric Propagation for Few-Shot Learning

ReMP: Rectified Metric Propagation for Few-Shot Learning

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

作者： Zhao, Yang Li, Chunyuan Yu, Ping Chen, Changyou Univ Buffalo Buffalo NY 14260 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781665448994

Few-shot learning features the capability of generalizing from a few examples. In this paper, we first identify that a discriminative feature space, namely a rectified metric space, that is learned to maintain the metric consistency from training to testing, is an essential component to the success of metric-based few-shot learning. Numerous analyses indicate that a simple modification of the objective can yield substantial performance gains. The resulting approach, called rectified metric propagation (ReMP), further optimizes an attentive prototype propagation network, and applies a repulsive force to make confident predictions. Extensive experiments demonstrate that the proposed ReMP is effective and efficient, and outperforms the state of the arts on various standard few-shot learning datasets.

关键词： Training computer vision conferences Force Prototypes Performance gain Extraterrestrial measurements

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Live Demonstration: Incremental Motion Estimation for Event-based Cameras by Dispersion Minimisation

Live Demonstration: Incremental Motion Estimation for Event-...

引用

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

作者： Nunes, Urbano Miguel Demiris, Yiannis Imperial Coll London Personal Robot Lab London England

ISBN: (纸本)9781665448994

Live demonstration setup. (Left) The setup consists of a DAVIS346B event camera connected to a standard consumer laptop and undergoes some motion. (Right) The motion estimates are plotted in red and, for rotation-like motions, the angular velocities provided by the camera IMU are also plotted in blue. This plot exemplifies an event camera undergoing large rotational motions (up to ~ 1000 deg/s) around the (a) x-axis, (b) y-axis and (c) z-axis. Overall, the incremental motion estimation method follows the IMU measurements. Optionally, the resultant global optical flow can also be shown, as well as the corresponding generated events by accumulating them onto the image plane (bottom left corner).

关键词： Portable computers Motion estimation conferences Cameras Minimization pattern recognition Motion measurement

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