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Unsupervised Domain Adaptation Architecture Search with Self-Training for Land Cover Mapping

Unsupervised Domain Adaptation Architecture Search with Self...

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

作者： Clifford Broni-Bediako Junshi Xia Naoto Yokoya RIKEN RIKEN Center for Advanced Intelligence Project (AIP) Tokyo Japan Graduate School of Frontier Sciences The University of Tokyo Chiba Japan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Unsupervised domain adaptation (UDA) is a challenging open problem in land cover mapping. Previous studies show encouraging progress in addressing cross-domain distribution shifts on remote sensing benchmarks for land cover mapping. The existing works are mainly built on large neural network architectures, which makes them resource-hungry systems, limiting their practical impact for many real-world applications in resource-constrained environments. Thus, we proposed a simple yet effective framework to search for lightweight neural networks automatically for land cover mapping tasks under domain shifts. This is achieved by integrating Markov random field neural architecture search (MRF-NAS) into a self-training UDA framework to search for efficient and effective networks under a limited computation budget. This is the first attempt to combine NAS with self-training UDA as a single framework for land cover mapping. We also investigate two different pseudo-labelling approaches (confidence-based and energy-based) in self-training scheme. Experimental results on two recent datasets (OpenEarthMap & FLAIR #1) for remote sensing UDA demonstrate a satisfactory performance. With only less than 2M parameters and 30.16 G FLOPs, the best-discovered lightweight network reaches state-of-the-art performance on the regional target domain of OpenEarthMap (59.38% mIoU) and the considered target domain of FLAIR #1 (51.19% mIoU). The code is at https://***/cliffbb/UDA-NAS.

关键词： Limiting conferences Neural networks Land surface computer architecture Benchmark testing Search problems

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vision-language models for decoding provider attention during neonatal resuscitation

Vision-language models for decoding provider attention durin...

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

作者： Felipe Parodi Jordan K. Matelsky Alejandra Regla-Vargas Elizabeth E. Foglia Charis Lim Danielle Weinberg Konrad P. Kording Heidi M. Herrick Michael L. Platt Department of Neuroscience University of Pennsylvania Department of Bioengineering University of Pennsylvania Johns Hopkins University Applied Physics Laboratory Department of Sociology University of Pennsylvania Department of Pediatrics Division of Neonatology University of Pennsylvania Perelman School of Medicine Department of Pediatrics Division of Neonatology Children’s Hospital of Philadelphia Department of Marketing University of Pennsylvania Department of Psychology University of Pennsylvania

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neonatal resuscitations demand an exceptional level of attentiveness from providers, who must process multiple streams of information simultaneously. Gaze strongly influences decision making; thus, understanding where a provider is looking during neonatal resuscitations could inform provider training, enhance real-time decision support, and improve the design of delivery rooms and neonatal intensive care units (NICUs). Current approaches to quantifying neonatal providers’ gaze rely on manual coding or simulations, which limit scalability and utility. Here, we introduce an automated, real-time, deep learning approach capable of decoding provider gaze into semantic classes directly from first-person point-of-view videos recorded during live resuscitations. Combining state-of-the-art, real-time segmentation with vision-language models, our low-shot pipeline attains 91% classification accuracy in identifying gaze targets without training. Upon fine-tuning, the performance of our gaze-guided vision transformer exceeds 98% accuracy in semantic gaze analysis, approaching human-level precision. This system, capable of real-time inference, enables objective quantification of provider attention dynamics during live neonatal resuscitation. Our approach offers a scalable solution that seamlessly integrates with existing infrastructure for data-scarce gaze analysis, thereby offering new opportunities for understanding and refining clinical decision making.

关键词： Training Pediatrics computer vision Accuracy Semantics Decision making Transformers

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Color-cued Efficient Densification Method for 3D Gaussian Splatting

Color-cued Efficient Densification Method for 3D Gaussian Sp...

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

作者： Sieun Kim Kyungjin Lee Youngki Lee Seoul National University Republic of Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Many variants of Neural Radiance Fields (NeRF) have been explored in pursuit of high-quality results with reasonable data size and real-time rendering speed. 3D Gaussian Splatting (3DGS) gained popularity due to its ability to render quality images in real-time; however, it still faces challenges with large data sizes. Meanwhile, the densification process of 3DGS plays a large role in deciding the quality and the data size of a model. Hence, it is crucial to devise a densification method that can populate Gaussians efficiently so that quality can be enhanced and fewer Gaussians are used. An efficient densification method that results in fewer Gaussians can also promote efficiency in training time, GPU memory usage, and rendering ***, we propose a novel, efficient densification method based on color cues, aiming to achieve a more compact Gaussian model without sacrificing image quality. By expanding the original 3DGS densification scheme, we identify weaknesses in the original method that lead to redundant Gaussians and compromise quality. In contrast to the original approach, which relies solely on the 2D position gradient, our method additionally leverages the spherical harmonics (SH) gradient to consider color cues. This approach resolves the inefficiencies of the original densification by aligning with the expanded scheme. Our method achieves at least 9× data size reduction with increased perceptual quality, accompanied by additional efficiencies in training time, GPU memory usage, and rendering speed.

关键词： Training Image quality Three-dimensional displays Image color analysis Memory management Graphics processing units Rendering (computer graphics)

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DRHDR: A Dual branch Residual Network for Multi-Bracket High Dynamic Range Imaging

DRHDR: A Dual branch Residual Network for Multi-Bracket High...

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

作者： Juan Marí n-Vega Michael Sloth Peter Schneider-Kamp Richard Rö ttger Department of Mathematics and Computer Science (IMADA) University of Southern Denmark Esoft Systems

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

We introduce DRHDR, a Dual branch Residual Convolutional Neural Network for Multi-Bracket HDR Imaging. To address the challenges of fusing multiple brackets from dynamic scenes, we propose an efficient dual branch network that operates on two different resolutions. The full resolution branch uses a Deformable Convolutional Block to align features and retain high-frequency details. A low resolution branch with a Spatial Attention Block aims to attend wanted areas from the non-reference brackets, and suppress displaced features that could incur on ghosting artifacts. By using a dual branch approach we are able to achieve high quality results while constraining the computational resources required to estimate the HDR results.

关键词： computer vision conferences Imaging Dynamic range pattern recognition Complexity theory Convolutional neural networks

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Motorcyclist Helmet Violation Detection Framework by Leveraging Robust Ensemble and Augmentation Methods

Motorcyclist Helmet Violation Detection Framework by Leverag...

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

作者： Thien Van Luong Huu Si Phuc Nguyen Duy Khanh Dinh Viet Hung Duong Duy Hong Sam Vo Huan Vu Minh Tuan Hoang Tien Cuong Nguyen Faculty of Computer Science Phenikaa University Hanoi Vietnam VNPT Group VNPT AI Hanoi Vietnam University of Transport and Communications Hanoi Vietnam

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Traffic Monitoring Systems play a crucial role in real-life scenarios by improving traffic flow and reducing violations. Among these violations, helmet non-compliance is particularly common in countries where motorcycles are the primary mode of transportation. However, deploying an automatic violation capturing for helmet non-compliance in the real world presents challenges due to diverse traffic situations, environmental conditions, varying object sizes, and severely imbalanced datasets. In order to address these challenges, we propose a novel deep learning framework for helmet violation detection, which consists of four different modules, namely, object detection, object association, post-processing for tracking, and score correction. In particular, we develop a robust ensemble method to take advantage of various state-of-the-art object detection models such as YOLOv7, YOLOv8, Co-DETR, and EfficientDet. Furthermore, to address the issue of data imbalance, we propose two copy and paste data augmentation techniques for enriching data samples of rare classes. As a result, our approach yields a substantial 7.43% mAP enhancement over the baseline Co-DETR model, achieving a final score of 0.4792 in the 2024 AI City Challenge Track 5 test set and ranking 3rd among the competing teams.

关键词： Deep learning Head Urban areas Transportation Motorcycles Object detection Data augmentation

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NTIRE 2024 Challenge on Bracketing Image Restoration and Enhancement: Datasets, Methods and Results

NTIRE 2024 Challenge on Bracketing Image Restoration and Enh...

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

作者： Zhilu Zhang Shuohao Zhang Renlong Wu Wangmeng Zuo Radu Timofte Xiaoxia Xing Hyunhee Park Sejun Song Changho Kim Xiangyu Kong Jinlong Wu Jianxing Zhang Jingfan Tan Zikun Liu Wenhan Luo Wenjie Lin Chengzhi Jiang Mingyan Han Zhen Liu Ting Jiang Jinting Luo Shen Cheng Linze Li Xinhan Niu Shuaicheng Liu Kexin Dai Kangzhen Yang Tao Hu Xiangyu Chen Yu Cao Qingsen Yan Yanning Zhang Genggeng Chen Yongqing Yang Wei Dong Xinwei Dai Yuanbo Zhou Xintao Qiu Hui Tang Wei Deng Qingquan Gao Tong Tong Peng Zhang Yifei Chen Wenbo Xiong Zhijun Song Pu Cheng Taolue Feng Yunqing He Daiguo Zhou Ying Huang Xiaowen Ma Peng Wu

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Low-light photography presents significant challenges. Multi-image processing methods have made numerous attempts to obtain high-quality photos, yet remain unsatisfactory. Recently, bracketing image restoration and enhancement has received increased attention. By leveraging the full potential of multi-exposure images, several tasks (including denoising, deblurring, high dynamic range enhancement, and super-resolution) can be jointly addressed. This paper reviews the NTIRE 2024 challenge on bracketing image restoration and enhancement. In the challenge, participants are required to process multi-exposure RAW images to generate noise-free, blur-free, high dynamic range, and even higher-resolution RAW images. The challenge comprises two tracks. Track 1 does not incorporate the super-resolution task, whereas Track 2 does. Each track featured five teams participating in the final testing phase. The proposed methods establish new state-of-the-art performance benchmarks.

关键词： Photography computer vision Reviews conferences Superresolution Noise reduction Benchmark testing

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CoLa-SDF: Controllable Latent StyleSDF for Disentangled 3D Face Generation

CoLa-SDF: Controllable Latent StyleSDF for Disentangled 3D F...

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

作者： Rahul Dey Bernhard Egger Vishnu Naresh Boddeti Ye Wang Tim K. Marks Mitsubishi Electric Research Laboratories (MERL) Michigan State University (MSU) Friedrich-Alexander-Universitat (FAU) Erlangen-Nürnberg

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Generating 3D faces and rendering them to images has numerous practical applications in areas including AR/VR, dataset generation, and avatar creation. In recent years, there has been a significant surge in the development of high-fidelity 3D face generation techniques such as StyleSDF, which combine the benefits of 3D implicit neural representations with those of style-based 2D generative adversarial networks (GANs). Although these implicit 3D GAN approaches generate highly realistic faces using a 3D representation, the properties of the generated faces cannot easily be edited or controlled. Meanwhile, linear 3D morphable models (3DMMs) and their nonlinear extensions have also made significant strides in their expressive capacity and quality, but they have yet to match the image quality achieved by GANs. This paper proposes a new method, CoLa-SDF, which combines the controllability of nonlinear 3DMMs with the high fidelity of implicit 3D GANs. Inspired by the impressive photorealism and expressive 3D representations of StyleSDF, our model uses a similar architecture but enforces the latent space to match the interpretable and physical parameters of the nonlinear 3D morphable model MOST-GAN. We demonstrate high-fidelity image synthesis and subsequent 3D manipulation with full control over the disentangled latent parameters.

关键词： Image quality Solid modeling Photorealism Three-dimensional displays Image synthesis Generative adversarial networks Rendering (computer graphics)

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Wake-Sleep Energy Based Models for Continual Learning

Wake-Sleep Energy Based Models for Continual Learning

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

作者： Vaibhav Singh Anna Choromanska Shuang Li Yilun Du Concordia University Mila New York University University of Toronto Massachusetts Institute of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper introduces a novel approach for continually training Energy-Based Models (EBMs) on the classification problems in the challenging setting of class incremental learning. Despite the fact that EBMs offer longer retention of knowledge on prior tasks, training EBMs contrastively remains a challenge. Driven by biological plausibility, we leverage the observation that sleep in humans supports active system consolidation and propose a new approach for training EBMs, which we call Wake-Sleep Energy Based Models (WS-EBMs), which rely on wake-sleep cycles. Our training approach consists of short wake phases followed by long sleep phases. During the short wake phase, the free energy associated with ground truth labels is minimized, which conditions the model towards the correct solutions. This is followed by a long sleep phase, where the free energy of the whole system is minimized contrastively, which allows the model to push the energy of incorrect solutions further from the correct response. We provide a theoretical analysis of WS-EBM showing that it satisfies the sufficient condition for designing proper EBM loss. Our empirical evaluation confirms the plausibility of our approach and demonstrates favorable performance of WS-EBM compared to traditional EBM training as well as state-of-the-art class-incremental continual learning techniques. Furthermore, our proposed two-phase training strategy can be easily integrated with existing techniques resulting in substantial boosts in their performance. Finally, we also provide interesting insights justifying our approach by analyzing the orthogonality between the sequential task vectors, and flatness of the optimized energy surfaces, which may guide the design of class incremental continual learning strategies.

关键词： Training Continuing education Sufficient conditions computer vision Incremental learning Biological system modeling conferences

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DTLLM-VLT: Diverse Text Generation for Visual Language Tracking Based on LLM

DTLLM-VLT: Diverse Text Generation for Visual Language Track...

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

作者： Xuchen Li Xiaokun Feng Shiyu Hu Meiqi Wu Dailing Zhang Jing Zhang Kaiqi Huang CRISE Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Visual Language Tracking (VLT) enhances single object tracking (SOT) by integrating natural language descriptions from a video, for the precise tracking of a specified object. By leveraging high-level semantic information, VLT guides object tracking, alleviating the constraints associated with relying on a visual modality. Nevertheless, most VLT benchmarks are annotated in a single granularity and lack a coherent semantic framework to provide scientific guidance. Moreover, coordinating human annotators for high-quality annotations is laborious and time-consuming. To address these challenges, we introduce DTLLM-VLT, which automatically generates extensive and multi-granularity text to enhance environmental diversity. (1) DTLLM-VLT generates scientific and multi-granularity text descriptions using a cohesive prompt framework. Its succinct and highly adaptable design allows seamless integration into various visual tracking benchmarks. (2) We select three prominent benchmarks to deploy our approach: short-term tracking, long-term tracking, and global instance tracking. We offer four granularity combinations for these benchmarks, considering the extent and density of semantic information, thereby showcasing the practicality and versatility of DTLLM-VLT. (3) We conduct comparative experiments on VLT benchmarks with different text granularities, evaluating and analyzing the impact of diverse text on tracking performance. Conclusionally, this work leverages LLM to provide multi-granularity semantic information for VLT task from efficient and diverse perspectives, enabling fine-grained evaluation of multi-modal trackers. In the future, we believe this work can be extended to more datasets to support vision datasets understanding.

关键词： Visualization computer vision Annotations conferences Semantics Natural languages Benchmark testing

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Codebook VQ-VAE Approach for Prostate Cancer Diagnosis using Multiparametric MRI

Codebook VQ-VAE Approach for Prostate Cancer Diagnosis using...

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

作者： Ekaterina Redekop Mara Pleasure Zichen Wang Karthik V Sarma Adam Kinnaird William Speier Corey W Arnold University of California Los Angeles USA University of Alberta Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multiparametric magnetic resonance imaging (mpMRI) plays an essential role in prostate cancer diagnosis as it can noninvasively localize and grade lesions based on their suspicion of representing clinically significant prostate cancer (csPCa). With the development of deep learning, automatic solutions for csPCa detection based on mpMRI have been developed; however, mpMRI data introduces several difficulties, including data scarcity, heterogeneity in image quality across institutions, and missing modalities. This work addresses these difficulties by building a radiology-based foundational model for prostate cancer mpMRI. Foundation models are deep learning models pretrained on a large-scale dataset and they have recently gained significant interest in computer vision and natural language applications. After pretraining, these models are often adapted for a variety of downstream tasks using smaller datasets from within the same domain. In this work, a large prostate multiparametric MRI (mpMRI) dataset was collected by combining data from our institution with two publicly available datasets. Joint modeling of all mpMRI modalities is essential for accurate prostate cancer diagnosis; however, some of these modalities may be missing. Using unsupervised learning, we pretrained modality-specific vector quantized variational autoencoders (VQ-VAE) to form a radiology foundational model. The learned codebook from VQ-VAE was then used to train a multimodal transformer to perform the diagnosis of clinically significant prostate cancer (csPCa). The proposed multimodal transformer models long-range dependencies between latent representations of input modalities and is augmented with modality-level dropout to increase the model robustness to incomplete modalities. Our framework outperforms previously published work and achieves an average AUC/sensitivity/specificity of 0.764/0.690/0.781. Our results show that pretraining on a larger dataset in combination with the power of transfor

关键词： Deep learning Adaptation models computer vision Frequency modulation Computational modeling Magnetic resonance imaging Radiology

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