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Self Texture Transfer Networks for Low Bitrate Image Compression

Self Texture Transfer Networks for Low Bitrate Image Compres...

引用

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

作者： Iwai, Shoma Miyazaki, Tomo Sugaya, Yoshihiro Omachi, Shinichiro Tohoku Univ Grad Sch Engn Dept Commun Sendai Miyagi Japan

ISBN: (纸本)9781665448994

Lossy image compression causes a loss of texture, especially at low bitrate. To mitigate this problem, we propose a novel image compression method that utilizes a reference-based image super-resolution model. We use two image compression models and a self texture transfer model. The image compression models encode and decode a whole input image and selected reference patches. The reference patches are small but compressed with high quality. The self texture transfer model transfers the texture of reference patches into similar regions in the compressed image. The experimental results show that our method can reconstruct accurate texture by transferring the texture of reference patches.

关键词： computer vision Image coding conferences Bit rate Superresolution pattern recognition Image reconstruction

来源：评论

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What Makes Multimodal In-Context Learning Work?

What Makes Multimodal In-Context Learning Work?

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Folco Bertini Baldassini Mustafa Shukor Matthieu Cord Laure Soulier Benjamin Piwowarski CNRS ISIR Sorbonne Université Paris France Valeo.ai Paris France

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Large Language Models have demonstrated remarkable performance across various tasks, exhibiting the capacity to swiftly acquire new skills, such as through In-Context Learning (ICL) with minimal demonstration examples. In this work, we present a comprehensive framework for investigating Multimodal ICL (M-ICL) in the context of Large Multimodal Models. We consider the best open-source multimodal models (e.g., IDEFICS, OpenFlamingo) and a wide range of multimodal tasks. Our study unveils several noteworthy findings: (1) M-ICL primarily relies on text-driven mechanisms, showing little to no influence from the image modality. (2) When used with advanced-ICL strategy (like RICES), M-ICL is not better than a simple strategy based on majority voting over context examples. Moreover, we identify several biases and limitations of M-ICL that warrant consideration prior to deployment. Code available at ***/folbaeni/multimodal-icl

关键词： Training Analytical models computer vision Codes Large language models Impedance matching conferences

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Making use of unlabeled data: Comparing strategies for marine animal detection in long-tailed datasets using self-supervised and semi-supervised pre-training

Making use of unlabeled data: Comparing strategies for marin...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Tarun Sharma Danelle E. Cline Duane Edgington California Institute of Technology USA MBARI USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper discusses strategies for object detection in marine images from a practitioner’s perspective working with real-world long-tail distributed datasets with a large amount of additional unlabeled data on hand. The paper discusses the benefits of separating the localization and classification stages, making the case for robustness in localization through the amalgamation of additional datasets inspired by a widely used approach by practitioners in the camera-trap literature. For the classification stage, the paper compares strategies to use additional unlabeled data, comparing supervised, supervised iteratively, self-supervised, and semi-supervised pre-training approaches. Our findings reveal that semi-supervised pre-training, followed by supervised fine-tuning, yields a significantly improved balanced performance across the long-tail distribution, albeit occasionally with a trade-off in overall accuracy. These insights are validated through experiments on two real-world long-tailed underwater datasets collected by the Monterey Bay Aquarium Research Institute (MBARI).

关键词： Location awareness computer vision Accuracy conferences Distributed databases Object detection Robustness

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

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

引用

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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Calibration of Continual Learning Models

Calibration of Continual Learning Models

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Lanpei Li Elia Piccoli Andrea Cossu Davide Bacciu Vincenzo Lomonaco ISTI-CNR University of Pisa University of Pisa

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Continual Learning (CL) focuses on maximizing the predictive performance of a model across a non-stationary stream of data. Unfortunately, CL models tend to forget previous knowledge, thus often underperforming when compared with an offline model trained jointly on the entire data stream. Given that any CL model will eventually make mistakes, it is of crucial importance to build calibrated CL models: models that can reliably tell their confidence when making a prediction. Model calibration is an active research topic in machine learning, yet to be properly investigated in CL. We provide the first empirical study of the behavior of calibration approaches in CL, showing that CL strategies do not inherently learn calibrated models. To mitigate this issue, we design a continual calibration approach that improves the performance of post-processing calibration methods over a wide range of different benchmarks and CL strategies. CL does not necessarily need perfect predictive models, but rather it can benefit from reliable predictive models. We believe our study on continual calibration represents a first step towards this direction.

关键词： Continuing education computer vision conferences Computational modeling Machine learning Predictive models Data models

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Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot Images

Depth-Regularized Optimization for 3D Gaussian Splatting in ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jaeyoung Chung Jeongtaek Oh Kyoung Mu Lee Department of ECE ASRI IPAI Seoul National University Seoul Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper presents a method to optimize Gaussian splatting with a limited number of images while avoiding overfitting. Representing a 3D scene by combining numerous Gaussian splats has yielded outstanding visual quality. However, it tends to overfit the training views when only a few images are available. To address this issue, we employ an adjusted depth map as a geometric reference, derived from a pre-trained monocular depth estimation model and subsequently aligned with the sparse structure-from-motion points. We regularize the optimization process of 3D Gaussian splatting with the adjusted depth and an additional unsupervised smooth constraint, thereby effectively reducing the occurrence of floating artifacts. Our method is mainly validated on the NeRF-LLFF dataset with varying numbers of images, and we conduct multiple experiments with randomly selected training images, presenting the average value to ensure fairness. Our approach demonstrates robust geometry compared to the original method, which relied solely on images.

关键词： Training Geometry Visualization Solid modeling computer vision Three-dimensional displays conferences

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CSCO: Connectivity Search of Convolutional Operators

CSCO: Connectivity Search of Convolutional Operators

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Tunhou Zhang Shiyu Li Hsin-Pai Cheng Feng Yan Hai Li Yiran Chen ECE Department Duke University Durham NC Qualcomm AI Research San Diego CA Department of Computer Science University of Houston Houston TX

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Exploring dense connectivity of convolutional operators establishes critical "synapses" to communicate feature vectors from different levels and enriches the set of transformations on computer vision applications. Yet, even with heavy-machinery approaches such as Neural Architecture Search (NAS), discovering effective connectivity patterns requires tremendous efforts due to either constrained connectivity design space or a sub-optimal exploration process induced by an unconstrained search space. In this paper, we propose CSCO, a novel paradigm that fabricates effective connectivity of convolutional operators with minimal utilization of existing design motifs and further utilizes the discovered wiring to construct high-performing ConvNets. CSCO guides the exploration via a neural predictor as a surrogate of the ground-truth performance. We introduce Graph Isomorphism as data augmentation to improve sample efficiency and propose a Metropolis-Hastings Evolutionary Search (MH-ES) to evade locally optimal architectures and advance search quality. Results on ImageNet show ∼ 0.6% performance improvement over hand-crafted and NAS-crafted dense connectivity. Our code is publicly available here.

关键词： Convolutional codes Wiring computer vision Architecture Buildings computer architecture Data augmentation

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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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Generation of Structurally Realistic Retinal Fundus Images with Diffusion Models

Generation of Structurally Realistic Retinal Fundus Images w...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Sojung Go Younghoon Ji Sang Jun Park Soochahn Lee Department of Ophthalmology Seoul National University College of Medicine Seoul National University Bundang Hospital Seongnam Korea VUNO Inc. Seoul Korea School of Electrical Engineering Kookmin University Seoul Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce a new technique for generating retinal fundus images that have anatomically accurate vascular structures, using diffusion models. We generate artery/vein masks to create the vascular structure, which we then condition to produce retinal fundus images. The proposed method can generate high-quality images with more realistic vascular structures and can create a diverse range of images based on the strengths of the diffusion model. We present quantitative evaluations that demonstrate the performance improvement using our method for data augmentation on vessel segmentation and artery/vein classification. We also present Turing test results by clinical experts, showing that our generated images are difficult to distinguish with real images. We believe that our method can be applied to construct stand-alone datasets that are irrelevant of patient privacy.

关键词： Image segmentation Data privacy computer vision Accuracy conferences Retina Diffusion models

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