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检索条件"任意字段=2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022"

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Multi-View Spatial-Temporal Learning for Understanding Unusual Behaviors in Untrimmed Naturalistic Driving Videos

Multi-View Spatial-Temporal Learning for Understanding Unusu...

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

作者： Huy-Hung Nguyen Chi Dai Tran Long Hoang Pham Duong Nguyen-Ngoc Tran Tai Huu-Phuong Tran Duong Khac Vu Quoc Pham-Nam Ho Ngoc Doan-Minh Huynh Hyung-Min Jeon Hyung-Joon Jeon Jae Wook Jeon Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The task of Naturalistic Driving Action recognition aims to detect and temporally localize distracting driving behavior in untrimmed videos. In this paper, we introduce our framework for Track 3 of the 8 th AI City Challenge in 2024. The approach is primarily based on large model fine-tuning and ensemble techniques to train a set of action recognition models on a small-scale dataset. Starting with raw videos, we segment them into individual action sequences based on their annotation. We then fine-tune four different action recognition models, with K-fold cross-validation applied to the segmented data. Following this, we execute a multi-view ensemble, selecting the most visible camera views for each action class to generate clip-level classification results for each video. Finally, a multi-step post-processing algorithm, which is designed for the AI City Challenge dataset’s specific features, is employed to perform temporal action localization and produce temporal segments for the actions. Our solution achieves a final mOS score of 0.7798 and attains the 5 th rank on the public leaderboard for the test set A2 of the challenge. The source code will be publicly available at https://***/SKKUAutoLab/AIC24-Track03.

关键词： Location awareness Source coding Urban areas X3D Prediction algorithms Classification algorithms pattern recognition

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A Multimodal Approach Integrating Convolutional and Recurrent Neural Networks for Alzheimer’s Disease Temporal Progression Prediction

A Multimodal Approach Integrating Convolutional and Recurren...

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

作者： Durga Supriya HL Swetha Mary Thomas Sowmya Kamath S National Institute of Technology Karnataka India Mangalore Karnataka India Dept. of Information Technology Healthcare Analytics and Language Engineering (HALE) Lab National Institute of Technology Karnataka Mangaluru India

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Alzheimer’s Disease (AD) poses a substantial healthcare challenge marked by cognitive decline and a lack of definitive treatments. As the global population ages, the prevalence of AD escalates, underscoring the need for more advanced diagnostic techniques. Current single-modality methods have limitations, emphasizing the critical need for early detection and precise diagnosis to facilitate timely interventions and the development of effective therapies. We propose a novel multimodal medical diagnostic framework for AD employing a hybrid deep learning model. This framework integrates a 3D Convolutional Neural Network (CNN) to extract detailed intra-slice features from MRI volumes and a Long Short-Term Memory (LSTM) network to capture intricate inter-sequence patterns indicative of AD progression. By leveraging longitudinal MRI data alongside spatial, temporal, biomarkers, and cognitive scores, our framework significantly enhances diagnostic accuracy, particularly in the early stages of the disease. We incorporate Grad-CAM to enhance the interpretability of MRI-based diagnoses by highlighting relevant brain regions. This multimodal approach achieves a promising accuracy of 92.65%, outperforming state-of-the-art works by a margin of 6%.

关键词： Accuracy Three-dimensional displays Recurrent neural networks Magnetic resonance imaging Feature extraction Spatial databases pattern recognition

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Deepfake Catcher: Can a Simple Fusion be Effective and Outperform Complex DNNs?

Deepfake Catcher: Can a Simple Fusion be Effective and Outpe...

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

作者： Akshay Agarwal Nalini Ratha IISER Bhopal India University at Buffalo USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Despite having completely different configurations, deep learning architectures learn a specific set of features that are common across architectures. For example, the initial few layers learn the low-level edge features from the images. Based on this fact, in this research, we have showcased the potential of deep neural network fusion for simple and effective deepfake detection. The advantage of building an architecture in such a manner is to build a low-power-consuming and accurate defense that can be deployed on mobile devices. To utilize the pre-trained knowledge and obtain downstream task-specific knowledge, we have identified a breakpoint in different networks and divided the obtained knowledge of a network into fixed and adaptive information. We have kept the fixed knowledge intact while modifying the adaptive knowledge along with entirely new knowledge for the deepfake detection task. In the end, the decision of multiple deep architectures trained based on their breakpoint are combined for improved performance. Extensive comparisons performed with existing state-of-the-art architectures demonstrate the effectiveness of the proposed deepfake detection algorithm. The proposed algorithm not only surpasses the existing state-of-the-art (SOTA) algorithms but also needs low computational power. We have further challenged the proposed algorithm by evaluating it by collecting real-world deepfake images.

关键词： Knowledge engineering Image quality Deepfakes Social networking (online) Architecture Deep architecture computer architecture

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i-MAE: Are Latent Representations in Masked Autoencoders Linearly Separable?

i-MAE: Are Latent Representations in Masked Autoencoders Lin...

引用

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

作者： Kevin Zhang Zhiqiang Shen Peking University KNQ.AI Mohamed bin Zayed University of AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Masked image modeling (MIM) has been recognized as a strong self-supervised pre-training approach in the vision domain. However, the mechanism and properties of the learned representations by such a scheme, as well as how to further enhance the representations are so far not well-explored. In this paper, we aim to explore an interactive Masked Autoencoders (i-MAE) framework to enhance the representation capability from two aspects: (1) employing a two-way image reconstruction and a latent feature reconstruction with distillation loss to learn better features; (2) proposing a semantics-enhanced sampling strategy to boost the learned semantics in MAE. Upon the proposed i-MAE architecture, we can address two critical questions to explore the behaviors of the learned representations in MAE: (1) Whether the separability of latent representations in Masked Autoencoders is helpful for model performance? We study it by forcing the input as a mixture of two images instead of one. (2) Whether we can enhance the representations in the latent feature space by controlling the degree of semantics during sampling on Masked Autoencoders? To this end, we propose a sampling strategy within a mini-batch based on the semantics of training samples to examine this aspect. Extensive experiments are conducted on CIFAR-10/100, Tiny-ImageNet and ImageNet-1K datasets to verify the observations we discovered. Furthermore, in addition to qualitatively analyzing the characteristics of the latent representations, we examine the existence of linear separability and the degree of semantics in the latent space by proposing two evaluation schemes. The surprising and consistent results across the qualitative and quantitative experiments demonstrate that i-MAE is a superior framework design for understanding MAE frameworks, as well as achieving better representational ability.

关键词： Training Measurement computer vision Image recognition conferences Semantics Aerospace electronics

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Histopathological Image Classification with Cell Morphology Aware Deep Neural Networks

Histopathological Image Classification with Cell Morphology ...

引用

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

作者： Andrey Ignatov Josephine Yates Valentina Boeva Computer Vision Lab ETH Zurich Computational Cancer Genomics Group

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Histopathological images are widely used for the analysis of diseased (tumor) tissues and patient treatment selection. While the majority of microscopy image processing was previously done manually by pathologists, recent advances in computer vision allow for accurate recognition of lesion regions with deep learning-based solutions. Such models, however, usually require extensive annotated datasets for training, which is often not the case in the considered task, where the number of available patient data samples is very limited. To deal with this problem, we propose a novel DeepCMorph model pre-trained to learn cell morphology and identify a large number of different cancer types. The model consists of two modules: the first one performs cell nuclei segmentation and annotates each cell type, and is trained on a combination of 8 publicly available datasets to ensure its high generalizability and robustness. The second module combines the obtained segmentation map with the original microscopy image and is trained for the downstream task. We pre-trained this module on the Pan-Cancer TCGA dataset consisting of over 270K tissue patches extracted from 8736 diagnostic slides from 7175 patients. The proposed solution achieved a new state-of-the-art performance on the dataset under consideration, detecting 32 cancer types with over 82% accuracy and outperforming all previously proposed solutions by more than 4%. We demonstrate that the resulting pre-trained model can be easily fine-tuned on smaller microscopy datasets, yielding superior results compared to the current top solutions and models initialized with ImageNet weights. The codes and pre-trained models presented in this paper are available at: https://***/aiff22/DeepCMorph

关键词： Image segmentation computer vision Analytical models Accuracy Microscopy Microprocessors Computational modeling

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The Third Monocular Depth Estimation Challenge

The Third Monocular Depth Estimation Challenge

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

作者： Jaime Spencer Fabio Tosi Matteo Poggi Ripudaman Singh Arora Chris Russell Simon Hadfield Richard Bowden GuangYuan Zhou ZhengXin Li Qiang Rao YiPing Bao Xiao Liu Dohyeong Kim Jinseong Kim Myunghyun Kim Mykola Lavreniuk Rui Li Qing Mao Jiang Wu Yu Zhu Jinqiu Sun Yanning Zhang Suraj Patni Aradhye Agarwal Chetan Arora Pihai Sun Kui Jiang Gang Wu Jian Liu Xianming Liu Junjun Jiang Xidan Zhang Jianing Wei Fangjun Wang Zhiming Tan Jiabao Wang Albert Luginov Muhammad Shahzad Seyed Hosseini Aleksander Trajcevski James H. Elder Independent University of Bologna Blue River Technology Oxford Internet Institute University of Surrey ByteDance University of Chinese Academy of Science RGA Inc. Space Research Institute NASU-SSAU Kyiv Ukraine Northwestern Polytechnical University Indian Institute of Technology Delhi Harbin Institute of Technology Fujitsu GuangXi University University of Reading York University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging SYNS-Patches dataset, featuring complex scenes in natural and indoor settings. As with the previous edition, methods can use any form of supervision, i.e. supervised or self-supervised. The challenge received a total of 19 submissions outperforming the baseline on the test set: 10 among them submitted a report describing their approach, highlighting a diffused use of foundational models such as Depth Anything at the core of their method. The challenge winners drastically improved 3D F-Score performance, from 17.51% to 23.72%.

关键词： computer vision Three-dimensional displays conferences Computational modeling Estimation pattern recognition

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Simple In-place Data Augmentation for Surveillance Object Detection

Simple In-place Data Augmentation for Surveillance Object De...

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

作者： Munkh-Erdene Otgonbold Ganzorig Batnasan Munkhjargal Gochoo Department of Computer Science and Software Engineering United Arab Emirates University UAE Department of Electronics Mongolian University of Science and Technology Mongolia Emirates Center for Mobility Research United Arab Emirates University UAE

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Motivated by the need to improve model performance in traffic monitoring tasks with limited labeled samples, we propose a straightforward augmentation technique tailored for object detection datasets, specifically designed for stationary camera-based applications. Our approach focuses on placing objects in the same positions as the originals to ensure its effectiveness. By applying in-place augmentation on objects from the same camera input image, we address the challenge of overlapping with original and previously selected objects. Through extensive testing on two traffic monitoring datasets, we illustrate the efficacy of our augmentation strategy in improving model performance, particularly in scenarios with limited labeled samples and imbalanced class distributions. Notably, our method achieves comparable performance to models trained on the entire dataset while utilizing only 8.5 percent of the original data. Moreover, we report significant improvements, with mAP@.5 increasing from 0.4798 to 0.5025, and the mAP@.5:.95 rising from 0.29 to 0.3138 on the FishEye8K dataset. These results highlight the potential of our augmentation approach in enhancing object detection models for traffic monitoring applications.

关键词： Image segmentation Surveillance Scalability conferences Object detection Data augmentation Cameras

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Multi-modal Aerial View Image Challenge: Sensor Domain Translation

Multi-modal Aerial View Image Challenge: Sensor Domain Trans...

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

作者： Spencer Low Oliver Nina Dylan Bowald Angel D. Sappa Nathan Inkawhich Peter Bruns Brigham Young University Provo Utah Air Force Research Laboratory Dayton OH ESPOL Polytechnic University Ecuador Computer Vision Center Spain Air Force Research Laboratory Rome NY University of Utah Salt Lake UT

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper describes the design, outcomes, and top methods of the 2nd annual Multi-modal Aerial View Image Challenge (MAVIC) aimed at cross modality aerial image translation. The primary objective of this competition is to stimulate research efforts towards the development of models capable of translating co-aligned images between multiple modalities. Specifically, the challenge centers on translation between synthetic aperture radar (SAR), electro-optical (EO), camera (RGB), and infrared (IR) sensor modalities, a budding area of research that has begun to garner attention. While last year’s inaugural challenge demonstrated the feasibility of SAR→EO translation, this year’s challenge made significant improvements in dataset coverage, sensor variation, experimental design, and methods covering the tasks of SAR→EO, SAR→RGB, SAR→IR, RGB→IR introducing a new dataset called translation. By Multi-modal Aerial Gathered Image Composites (MAGIC); multimodal image translation is available for different comparisons. With a more rigorous set of translation performance metrics, winners were determined from aggregation of L1-norm, LPIPS (Learned Perceptual Image Patch Similarity, and FID (Frechet Inception Distance) scores. The wining methods included the pix2pixHD and LPIPS metrics as loss functions with an aggregated score 5% better separated by the SAR→EO and RGB→IR translation scores.

关键词： Measurement computer vision Design methodology conferences Cameras pattern recognition Synthetic aperture radar

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Multi-Modal Fusion of Event and RGB for Monocular Depth Estimation Using a Unified Transformer-based Architecture

Multi-Modal Fusion of Event and RGB for Monocular Depth Esti...

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

作者： Anusha Devulapally Md Fahim Faysal Khan Siddharth Advani Vijaykrishnan Narayanan The Pennsylvania State University Samsung Electronics America

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the field of robotics and autonomous navigation, accurate pixel-level depth estimation has gained significant importance. Event cameras or dynamic vision sensors, capture asynchronous changes in brightness at the pixel level, offering benefits such as high temporal resolution, no motion blur, and a wide dynamic range. However, unlike traditional cameras that measure absolute intensity, event cameras lack the ability to provide scene context. Efficiently combining the advantages of both asynchronous events and synchronous RGB images to enhance depth estimation remains a challenge. In our study, we introduce a unified transformer that combines both event and RGB modalities to achieve precise depth prediction. In contrast to individual transformers for input modalities, a unified transformer model captures inter-modal dependencies and uses self-attention to enhance event-RGB contextual interactions. This approach exceeds the performance of recurrent neural network (RNN) methods used in state-of-the-art models. To encode the temporal information from events, convLSTMs are used before the transformer to improve depth estimation. Our proposed architecture outperforms the existing approaches in terms of absolute mean depth error, achieving state-of-the-art results in most cases. Additionally, the performance is also seen in other metrics like RMSE, absolute relative difference and depth thresholds compared to the existing approaches. The source code is available at: https://***/anusha-devulapally/ER-F2D.

关键词： Measurement computer vision Accuracy Recurrent neural networks Robot vision systems Estimation computer architecture

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Breaking Through the Haze: An Advanced Non-Homogeneous Dehazing Method based on Fast Fourier Convolution and ConvNeXt

Breaking Through the Haze: An Advanced Non-Homogeneous Dehaz...

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

作者： Han Zhou Wei Dong Yangyi Liu Jun Chen Department of Electrical and Computer Engineering Mcmaster University Hamilton Canada Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

Haze usually leads to deteriorated images with low contrast, color shift and structural distortion. We observe that many deep learning based models exhibit exceptional performance on removing homogeneous haze, but they usually fail to address the challenge of non-homogeneous dehazing. Two main factors account for this situation. Firstly, due to the intricate and. non uniform distribution of dense haze, the recovery of structural and chromatic features with high fidelity is challenging, particularly in regions with heavy haze. Secondly, the existing small scale datasets for non-homogeneous dehazing are inadequate to support reliable learning of feature mappings between hazy images and. their corresponding haze-free counterparts by convolutional neural network (CNN)-based models. To tackle these two challenges, we propose a novel two branch network that leverages 2D discrete wavelete transform (DWT), fast Fourier convolution (FFC) residual block and a pretrained. ConvNeXt model. Specifically, in the DWT-FFC frequency branch, our model exploits DWT to capture more high-frequency features. Moreover, by taking advantage of the large receptive field, provided, by FFC residual blocks, our model is able to effectively explore global contextual information and. produce images with better perceptual quality In the prior knowledge branch, an ImageNet pretrained ConvNeXt as opposed, to Res2Net is adopted. This enables our model to learn more supplementary information and. acquire a stronger generalization ability. The feasibility and effectiveness of the proposed, method, is demonstrated, via extensive experiments and. ablation studies. The code is available at https://***/zhouhll5/DWT-FFC.

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