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TattTRN: Template Reconstruction Network for Tattoo Retrieval

TattTRN: Template Reconstruction Network for Tattoo Retrieva...

引用

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

作者： Lazaro Janier Gonzalez-Soler Maciej Salwowski Christian Rathgeb Daniel Fischer da/sec - Biometrics and Security Research Group Darmstadt Germany Faculty of Computer Science Technical University of Denmark Denmark

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Tattoos have been used effectively as soft biometrics to assist law enforcement in the identification of offenders and victims, as they contain discriminative information, and are a useful indicator to locate members of a criminal gang or organisation. Due to various privacy issues in the acquisition of images containing tattoos, only a limited number of databases exists. This lack of databases has delayed the development of new methods to effectively retrieve a potential suspect’s tattoo images from a candidate gallery. To mitigate this issue, in our work, we use an unsupervised generative approach to create a balanced database consisting of 28,550 semi-synthetic images with tattooed subjects from 571 tattoo categories. Further, we introduce a novel Tattoo Template Reconstruction Network (TattTRN), which learns to map the input tattoo sample to its respective tattoo template to enhance the distinguishing attributes of the final feature embedding. Experimental results with real data, i.e. WebTattoo and BIVTatt databases, demonstrate the soundness of the presented approach: an accuracy of up to 99% is achieved for checking at most the first 20 entries of the candidate list. 1

关键词： Image segmentation Data privacy computer vision Databases Law enforcement conferences Semantics

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ShadowRefiner: Towards Mask-free Shadow Removal via Fast Fourier Transformer

ShadowRefiner: Towards Mask-free Shadow Removal via Fast Fou...

引用

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

作者： Wei Dong Han Zhou Yuqiong Tian Jingke Sun Xiaohong Liu Guangtao Zhai Jun Chen McMaster University Shanghai Jiao Tong University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Shadow-affected images often exhibit pronounced spatial discrepancies in color and illumination, consequently degrading various vision applications including object detection and segmentation systems. To effectively eliminate shadows in real-world images while preserving intricate details and producing visually compelling outcomes, we introduce a mask-free Shadow Removal and Refinement network (ShadowRefiner) via Fast Fourier Transformer. Specifically, the Shadow Removal module in our method aims to establish effective mappings between shadow-affected and shadow-free images via spatial and frequency representation learning. To mitigate the pixel misalignment and further improve the image quality, we propose a novel Fast-Fourier Attention based Transformer (FFAT) architecture, where an innovative attention mechanism is designed for meticulous refinement. Our method wins the championship in the Perceptual Track and achieves the second best performance in the Fidelity Track of NTIRE 2024 Image Shadow Removal Challenge. Besides, comprehensive experiment result also demonstrate the compelling effectiveness of our proposed method. The code is publicly available: https://***/movingforward100/Shadow_R.

关键词： Representation learning Image quality Image segmentation computer vision Image color analysis conferences Lighting

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Weakly Supervised Set-Consistency Learning Improves Morphological Profiling of Single-Cell Images

Weakly Supervised Set-Consistency Learning Improves Morpholo...

引用

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

作者： Heming Yao Phil Hanslovsky Jan-Christian Huetter Burkhard Hoeckendorf David Richmond Biology Research | AI Development (BRAID) gCS Genentech

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Optical Pooled Screening (OPS) is a powerful tool combining high-content microscopy with genetic engineering to investigate gene function in disease. The characterization of high-content images remains an active area of research and is currently undergoing rapid innovation through the application of self-supervised learning and vision transformers. In this study, we propose a set-level consistency learning algorithm, Set-DINO, that combines self-supervised learning with weak supervision to improve learned representations of perturbation effects in single-cell images. Our method leverages the replicate structure of OPS experiments (i.e., cells undergoing the same genetic perturbation, both within and across batches) as a form of weak supervision. We conduct extensive experiments on a large-scale OPS dataset with more than 5000 genetic perturbations, and demonstrate that Set-DINO helps mitigate the impact of confounders and encodes more biologically meaningful information. In particular, Set-DINO recalls known biological relationships with higher accuracy compared to commonly used methods for morphological profiling, suggesting that it can generate more reliable insights from drug target discovery campaigns leveraging OPS.

关键词： computer vision Technological innovation Biomedical optical imaging Perturbation methods Self-supervised learning Optical imaging Genetics

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DELTA: Decoupling Long-Tailed Online Continual Learning

DELTA: Decoupling Long-Tailed Online Continual Learning

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

作者： Siddeshwar Raghavan Jiangpeng He Fengqing Zhu School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

A significant challenge in achieving ubiquitous Artificial Intelligence is the limited ability of models to rapidly learn new information in real-world scenarios where data follows long-tailed distributions, all while avoiding forgetting previously acquired knowledge. In this work, we study the under-explored problem of Long-Tailed Online Continual Learning (LTOCL), which aims to learn new tasks from sequentially arriving class-imbalanced data streams. Each data is observed only once for training without knowing the task data distribution. We present DELTA, a decoupled learning approach designed to enhance learning representations and address the substantial imbalance in LTOCL. We enhance the learning process by adapting supervised contrastive learning to attract similar samples and repel dissimilar (out-of-class) samples. Further, by balancing gradients during training using an equalization loss, DELTA significantly enhances learning outcomes and successfully mitigates catastrophic forgetting. Through extensive evaluation, we demonstrate that DELTA improves the capacity for incremental learning, surpassing existing OCL methods. Our results suggest considerable promise for applying OCL in real-world applications. Code is available online 1

关键词： Continuing education Training computer vision Incremental learning conferences Contrastive learning Data models

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The devil is in discretization discrepancy. Robustifying Differentiable NAS with Single-Stage Searching Protocol

The devil is in discretization discrepancy. Robustifying Dif...

引用

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

作者： Konstanty Subbotko Wojciech Jablonski Piotr Bilinski University of Warsaw

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural Architecture Search (NAS) has been widely adopted to design neural networks for various computer vision tasks. One of its most promising subdomains is differentiable NAS (DNAS), where the optimal architecture is found in a differentiable manner. However, gradient-based methods suffer from the discretization error, which can severely damage the process of obtaining the final architecture. In our work, we first study the risk of discretization error and show how it affects an unregularized supernet. Then, we present that penalizing high entropy, a common technique of architecture regularization, can hinder the supernet’s performance. Therefore, to robustify the DNAS framework, we introduce a novel single-stage searching protocol, which is not reliant on decoding a continuous architecture. Our results demonstrate that this approach outperforms other DNAS methods by achieving 75.3% in the searching stage on the Cityscapes validation dataset and attains performance 1.1% higher than the optimal network of DCNAS on the non-dense search space comprising short connections. The entire training process takes only 5.5 GPU days due to the weight reuse, and yields a computationally efficient architecture. Additionally, we propose a new dataset split procedure, which substantially improves results and prevents architecture degeneration in DARTS.

关键词： Training computer vision Protocols Neural networks DNA Graphics processing units computer architecture

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Localised-NeRF: Specular Highlights and Colour Gradient Localising in NeRF

Localised-NeRF: Specular Highlights and Colour Gradient Loca...

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

作者： Dharmendra Selvaratnam Dena Bazazian Faculty of Science and Engineering School of Engineering Computing and Mathematics (SECaM) University of Plymouth

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural Radiance Field (NeRF) based systems predominantly operate within the RGB (Red, Green, and Blue) space; however, the distinctive capability of the HSV (Hue, Saturation, and Value) space to discern between specular and diffuse regions is seldom utilised in the literature. We introduce Localised-NeRF, which projects the queried pixel point onto multiple training images to obtain a multi-view feature representation on HSV space and gradient space to obtain important features that can be used to synthesise novel view colour. This integration is pivotal in identifying specular highlights within scenes, thereby enriching the model’s understanding of specular changes as the viewing angle alters. Our proposed Localised-NeRF model uses an attention-driven approach that not only maintains local view direction consistency but also leverages image-based features namely the HSV colour space and colour gradients. These features serve as effective indirect priors for both the training and testing phases to predict the diffuse and specular colour. Our model exhibits competitive performance with prior NeRF-based models, as demonstrated on the Shiny Blender and Synthetic datasets. The code of Localised-NeRF is publicly available 1 .

关键词： Training computer vision Visualization Image color analysis Neural radiance field Rendering (computer graphics) Mathematical models

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ZInD-Tell: Towards Translating Indoor Panoramas into Descriptions

ZInD-Tell: Towards Translating Indoor Panoramas into Descrip...

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

作者： Tonmoay Deb Lichen Wang Zachary Bessinger Naji Khosravan Eric Penner Sing Bing Kang Northwestern University Zillow Group

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper focuses on bridging the gap between natural language descriptions, 360° panoramas, room shapes, and layouts/floorplans of indoor spaces. To enable new multimodal (image, geometry, language) research directions in indoor environment understanding, we propose a novel extension to the Zillow Indoor Dataset (ZInD) which we call ZInD-Tell 1 . We first introduce an effective technique for extracting geometric information from ZInD’s raw structural data, which facilitates the generation of accurate ground truth descriptions using GPT-4. A human-in-the-loop approach is then employed to ensure the quality of these descriptions. To demonstrate the vast potential of our dataset, we introduce the ZInD-Tell benchmark, focusing on two exemplary tasks: language-based home retrieval and indoor description generation. Furthermore, we propose an end-to-end, zero-shot baseline model, ZInD-Agent, designed to process an unordered set of panorama images and generate home descriptions. ZInD-Agent outperforms naïve methods in both tasks, hence, can be considered as a complement to the naïve to show potential use of the data and impact of geometry. We believe this work initiates new trajectories in leveraging computer vision techniques to analyze indoor panorama images descriptively by learning the latent relation between vision, geometry, and language modalities.

关键词： Geometry computer vision Shape Focusing Benchmark testing Information retrieval Human in the loop

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Data-Efficient and Robust Task Selection for Meta-Learning

Data-Efficient and Robust Task Selection for Meta-Learning

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

作者： Donglin Zhan James Anderson Columbia University New York NY

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Meta-learning methods typically learn tasks under the assumption that all tasks are equally important. However, this assumption is often not valid. In real-world applications, tasks can vary both in their importance during different training stages and in whether they contain noisy labeled data or not, making a uniform approach suboptimal. To address these issues, we propose the Data-Efficient and Robust Task Selection (DERTS) algorithm, which can be incorporated into both gradient and metric-based meta-learning algorithms. DERTS selects weighted subsets of tasks from task pools by minimizing the approximation error of the full gradient of task pools in the meta-training stage. The selected tasks are efficient for rapid training and robust towards noisy label scenarios. Unlike existing algorithms, DERTS does not require any architecture modification for training and can handle noisy label data in both the support and query sets. Analysis of DERTS shows that the algorithm follows similar training dynamics as learning on the full task pools. Experiments show that DERTS outperforms existing sampling strategies for meta-learning on both gradient-based and metric-based meta-learning algorithms in limited data budget and noisy task settings.

关键词： Metalearning Training computer vision Heuristic algorithms conferences computer architecture Approximation algorithms

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Efficient Multi-Lens Bokeh Effect Rendering and Transformation

Efficient Multi-Lens Bokeh Effect Rendering and Transformati...

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

作者： Tim Seizinger Marcos V. Conde Manuel Kolmet Tom E. Bishop Radu Timofte Computer Vision Lab CAIDAS & IFI University of Würzburg Germany Glass Imaging Inc. CA

Many advancements of mobile cameras aim to reach the visual quality of professional DSLR cameras. Great progress was shown over the last years in optimizing the sharp regions of an image and in creating virtual portrait effects with artificially blurred backgrounds. Bokeh is the aesthetic quality of the blur in out-of-focus areas of an image. This is a popular technique among professional photographers, and for this reason, a new goal in computational photography is to optimize the Bokeh effect *** paper introduces EBokehNet, a efficient state-of-the-art solution for Bokeh effect transformation and rendering. Our method can render Bokeh from an all-in-focus image, or transform the Bokeh of one lens to the effect of another lens without harming the sharp foreground regions in the image. Moreover we can control the shape and strength of the effect by feeding the lens properties i.e. type (Sony or Canon) and aperture, into the neural network as an additional input. Our method is a winning solution at the NTIRE 2023 Lens-to-Lens Bokeh Effect Transformation Challenge, and state-of-the-art at the EBB benchmark.

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Deep Convolutional Sparse Coding Networks for Interpretable Image Fusion

Deep Convolutional Sparse Coding Networks for Interpretable ...

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

作者： Zixiang Zhao Jiangshe Zhang Haowen Bai Yicheng Wang Yukun Cui Lilun Deng Kai Sun Chunxia Zhang Junmin Liu Shuang Xu Xi’an Jiaotong University Computer Vision Lab ETH Zurich The University of Melbourne Research and Development Institute of Northwestern Polytechnical University in Shenzhen Northwestern Polytechnical University

Image fusion is a significant problem in many fields including digital photography, computational imaging and remote sensing, to name but a few. Recently, deep learning has emerged as an important tool for image fusion. This paper presents CSCFuse, which contains three deep convolutional sparse coding (CSC) networks for three kinds of image fusion tasks (i.e., infrared and visible image fusion, multi-exposure image fusion, and multi-spectral image fusion). The CSC model and the iterative shrinkage and thresholding algorithm are generalized into dictionary convolution units. As a result, all hyper-parameters are learned from data. Our extensive experiments and comprehensive comparisons reveal the superiority of CSCF use with regard to quantitative evaluation and visual inspection.

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