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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024"

共 4655 条记录，以下是1341-1350 订阅

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Situation Monitor: Diversity-Driven Zero-Shot Out-of-Distribution Detection using Budding Ensemble Architecture for Object Detection

Situation Monitor: Diversity-Driven Zero-Shot Out-of-Distrib...

引用

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

作者： Syed Sha Qutub Michael Paulitsch Kay-Ulrich Scholl Neslihan Kose Cihangir Korbinian Hagn Fabian Oboril Gereon Hinz Alois Knoll Intel Labs Munich Germany Technical University of Munich Munich Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce Situation Monitor, a novel zero-shot Out-of-Distribution (OOD) detection approach for transformer-based object detection models to enhance reliability in safety-critical machine learning applications such as autonomous driving. The Situation Monitor utilizes the Diversity-based Budding Ensemble Architecture (DBEA) and increases the OOD performance by integrating a diversity loss into the training process on top of the budding ensemble architecture, detecting Far-OOD samples and minimizing false positives on Near-OOD samples. Moreover, utilizing the resulting DBEA increases the model’s OOD performance and improves the calibration of confidence scores, particularly concerning the intersection over union of the detected objects. The DBEA model achieves these advancements with a 14% reduction in trainable parameters compared to the vanilla model. This signifies a substantial improvement in efficiency without compromising the model’s ability to detect OOD instances and calibrate the confidence scores accurately.

关键词： Training Adaptation models Zero-shot learning Computational modeling Scalability computer architecture Object detection

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SwinFuSR: an image fusion-inspired model for RGB-guided thermal image super-resolution

SwinFuSR: an image fusion-inspired model for RGB-guided ther...

引用

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

作者： Cyprien Arnold Philippe Jouvet Lama Seoud Polytechnique Montréal Montréal Canada CHU Sainte Justine Montréal Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Thermal imaging plays a crucial role in various applications, but the inherent low resolution of commonly available infrared (IR) cameras limits its effectiveness. Conventional super-resolution (SR) methods often struggle with thermal images due to their lack of high-frequency details. Guided SR leverages information from a high-resolution image, typically in the visible spectrum, to enhance the reconstruction of a high-res IR image from the low-res input. Inspired by SwinFusion, we propose SwinFuSR, a guided SR architecture based on Swin transformers. In real world scenarios, however, the guiding modality (e.g. RBG image) may be missing, so we propose a training method that improves the robustness of the model in this case. Our method has few parameters and outperforms state of the art models in terms of Peak Signal to Noise Ratio (PSNR) and Structural SIMilarity (SSIM). In Track 2 of the PBVS 2024 Thermal Image Super-Resolution Challenge, it achieves 3rd place in the PSNR metric. Our code and pretained weights are available at https://***/visionICLab/SwinFuSR.

关键词： Training Measurement PSNR conferences Superresolution computer architecture Transformers

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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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UP-NAS: Unified Proxy for Neural Architecture Search

UP-NAS: Unified Proxy for Neural Architecture Search

引用

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

作者： Yi-Cheng Huang Wei-Hua Li Chih-Han Tsou Jun-Cheng Chen Chu-Song Chen National Taiwan University Academia Sinica

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recently, zero-cost proxies for neural architecture search (NAS) have attracted increasing attention. They allow us to discover top-performing neural networks through architecture scoring without requiring training a very large network (i.e., supernet). Thus, it can save significant computation resources to complete the search. However, to our knowledge, no single proxy works best for different tasks and scenarios. To consolidate the strength of different proxies and to reduce search bias, we propose a unified proxy neural architecture search framework (UP-NAS) which learns a multi-proxy estimator for predicting a unified score by combining multiple zero-cost proxies. The predicted score is then used for an efficient gradient-ascent architecture search in the embedding space of the neural network architectures. Our approach can not only save computational time required for multiple proxies during architecture search but also gain the flexibility to consolidate the existing proxies on different tasks. We conduct experiments on the search spaces of NAS-Bench-201 and DARTS in different datasets. The results demonstrate the effectiveness of the proposed approach. Code is available at https://***/AI-Application-andIntegration-Lab/UP-NAS.

关键词： Training computer vision Codes conferences Neural networks computer architecture pattern recognition

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Reconsidering CO2 emissions from computer vision

Reconsidering CO2 emissions from Computer Vision

引用

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

作者： Fu, Andre Hosseini, Mahdi S. Plataniotis, Konstantinos N. Univ Toronto Toronto ON Canada Univ New Brunswick Fredericton NB Canada

ISBN: (纸本)9781665448994

Climate change is a pressing issue that is currently affecting and will affect every part of our lives. It's becoming incredibly vital we, as a society, address the climate crisis as a universal effort, including those in the computer vision (CV) community. In this work, we analyze the total cost of CO2 emissions by breaking it into (1) the architecture creation cost and (2) the life-time evaluation cost. We show that over time, these costs are non-negligible and are having a direct impact on our future. Importantly, we conduct an ethical analysis of how the CV-community is unintentionally overlooking its own ethical AI principles by emitting this level of CO2. To address these concerns, we propose adding "enforcement" as a pillar of ethical AI and provide some recommendations for how architecture designers and broader CV community can curb the climate crisis.

关键词： computer vision Climate change conferences computer architecture Pressing pattern recognition Artificial intelligence

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Color Me Good: Branding in the Coloring Style of Movie Posters

Color Me Good: Branding in the Coloring Style of Movie Poste...

引用

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

作者： Agrawal, Rishabh Sivaprasad, Sarath Pedanekar, Niranjan TCS Res Tata Consultancy Serv Pune 411013 Maharashtra India

ISBN: (纸本)9781665448994

Brand logos are often rendered in a different style based on a context such as an event promotion. For example, Warner Bros. uses a different variety of their brand logo for different movies for promotion and aesthetic appeal. In this paper, we propose an automated method to render brand logos in the coloring style of branding material such as movie posters. For this, we adopt a photo-realistic neural style transfer method using movie posters as the style source. We propose a color-based image segmentation and matching method to assign style segments to logo segments. Using these, we render the well-known Warner Bros. logo in the coloring style of 141 movie posters. We also present survey results where 287 participants rate the machine-stylized logos for their representativeness and visual appeal.

关键词： Image segmentation Visualization computer vision Image color analysis conferences Semantics Brightness

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Dealing with Missing Modalities in the Visual Question Answer-Difference Prediction Task through Knowledge Distillation

Dealing with Missing Modalities in the Visual Question Answe...

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

作者： Cho, Jae Won Kim, Dong-Jin Choi, Jinsoo Jung, Yunjae Kweon, In So Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665448994

In this work, we address the issues of the missing modalities that have arisen from the Visual Question Answer-Difference prediction task and find a novel method to solve the task at hand. We address the missing modality-the ground truth answers-that are not present at test time and use a privileged knowledge distillation scheme to deal with the issue of the missing modality. In order to efficiently do so, we first introduce a model, the "Big" Teacher, that takes the image/question/answer triplet as its input and outperforms the baseline, then use a combination of models to distill knowledge to a target network (student) that only takes the image/question pair as its inputs. We experiment our models on the VizWiz and VQA-V2 Answer Difference datasets and show through extensive experimentation and ablation the performance of our method and a diverse possibility for future research.

关键词： Visualization computer vision conferences Knowledge discovery pattern recognition Task analysis Bars

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LLM-Seg: Bridging Image Segmentation and Large Language Model Reasoning

LLM-Seg: Bridging Image Segmentation and Large Language Mode...

引用

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

作者： Junchi Wang Lei Ke ETH Zurich

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Understanding human instructions to identify the target objects is vital for perception systems. In recent years, the advancements of Large Language Models (LLMs) have introduced new possibilities for image segmentation. In this work, we delve into reasoning segmentation, a novel task that enables segmentation system to reason and interpret implicit user intention via large language model reasoning and then segment the corresponding target. Our work on reasoning segmentation contributes on both the methodological design and dataset labeling. For the model, we propose a new framework named LLM-Seg. LLM-Seg effectively connects the current foundational Segmentation Anything Model and the LLM by mask proposals selection. For the dataset, we propose an automatic data generation pipeline and construct a new reasoning segmentation dataset named LLM-Seg40K. Experiments demonstrate that our LLM-Seg exhibits competitive performance compared with existing methods. Furthermore, our proposed pipeline can efficiently produce high-quality reasoning segmentation datasets. The LLM-Seg40K dataset, developed through this pipeline, serves as a new benchmark for training and evaluating various reasoning segmentation approaches. Our code, models and dataset are at https://***/wangjunchi/LLMSeg.

关键词： Training Image segmentation Large language models Design methodology Pipelines Cognition pattern recognition

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ConPro: Learning Severity Representation for Medical Images using Contrastive Learning and Preference Optimization

ConPro: Learning Severity Representation for Medical Images ...

引用

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

作者： Hong Nguyen Hoang Nguyen Melinda Chang Hieu Pham Shrikanth Narayanan Michael Pazzani University of Southern California Los Angeles United States Vinuni-Illinois Smart Health Center Hanoi Vietnam

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Understanding the severity of conditions shown in images in medical diagnosis is crucial, serving as a key guide for clinical assessment, treatment, as well as evaluating longitudinal progression. This paper proposes ConPrO: a novel representation learning method for severity assessment in medical images using Contrastive learning-integrated Preference Optimization. Different from conventional contrastive learning methods that maximize the distance between classes, ConPrO injects into the latent vector the distance preference knowledge between various severity classes and the normal class. We systematically examine the key components of our framework to illuminate how contrastive prediction tasks acquire valuable representations. We show that our representation learning framework offers valuable severity ordering in the feature space while outperforming previous state-of-the-art methods on classification tasks. We achieve a 6% and 20% relative improvement compared to a supervised and a self-supervised baseline, respectively. In addition, we derived discussions on severity indicators and related applications of preference comparison in the medical domain.

关键词： Representation learning Measurement Visualization Contrastive learning Prediction algorithms Vectors pattern recognition

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Dformer: Learning Efficient Image Restoration with Perceptual Guidance

Dformer: Learning Efficient Image Restoration with Perceptua...

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

作者： Nodirkhuja Khudjaev Roman Tsoy S M A Sharif Azamat Myrzabekov Seongwan Kim Jaeho Lee Opt-AI Inc. LG Sciencepark Seoul South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Image restoration tasks incorporate widespread real-world application. Apart from its significant practicability, generic deep image restoration methods still fail to handle complex tasks, like shadow removal, low-light enhancement, etc. This paper addresses the limitations of existing image restoration methods by introducing a novel deep architecture. The proposed model incorporates illumination mapping inspired by the Retinex theory within a double encoder-decoder network. Additionally, it utilizes a multi-head cross-attention mechanism to correlate input and reconstructed images to generate plausible and refined images. The proposed model employs a perceptual optimization strategy to tackle intricate restoration tasks effectively. It surpasses state-of-the-art methods in demanding tasks such as shadow removal, low-light image enhancement, and blind compress image enhancement, all while utilizing fewer trainable parameters. Our method is selected among the top solutions in the New Trends in Image Restoration and Enhancement’24 (NTIRE) challenge for shadow removal, securing a top position without resorting to score-boosting techniques such as ensembling.

关键词： Image coding Image edge detection Lighting Deep architecture Market research Image restoration pattern recognition

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