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共 8962 条记录，以下是1651-1660 订阅

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Enhancing Traffic Safety with Parallel Dense Video Captioning for End-to-End Event Analysis

Enhancing Traffic Safety with Parallel Dense Video Captionin...

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

作者： Maged Shoman Dongdong Wang Armstrong Aboah Mohamed Abdel-Aty Department of Civil Environmental and Construction Engineering Smart and Safe Transportation (SST) Lab University of Central Florida United States Department of Civil Construction and Environmental Engineering North Dakota State University United States Department of Civil Environmental and Construction Engineering Joint Appointment Department of Computer Science Smart and Safe Transportation (SST) Lab University of Central Florida United States

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper introduces our solution for Track 2 in AI City Challenge 2024. The task aims to solve traffic safety description and analysis with the dataset of Woven Traffic Safety (WTS), a real-world Pedestrian-Centric Traffic Video Dataset for Fine-grained Spatial-Temporal Understanding. Our solution mainly focuses on the following points: 1) To solve dense video captioning, we leverage the framework of dense video captioning with parallel decoding (PDVC) to model visual-language sequences and generate dense caption by chapters for video. 2) Our work leverages CLIP to extract visual features to more efficiently perform cross-modality training between visual and textual representations. 3) We conduct domain-specific model adaptation to mitigate domain shift problem that poses recognition challenge in video understanding. 4) Moreover, we leverage BDD-5K captioned videos to conduct knowledge transfer for better understanding WTS videos and more accurate captioning. Our solution has yielded on the test set, achieving 6th place in the competition. The open-source code will be available at https://***/UCF-SST-Lab/AICity2024CVPRW

关键词： Training Visualization Adaptation models Urban areas Feature extraction Weaving Tokenization

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ADAS: A Direct Adaptation Strategy for Multi-Target Domain Adaptive Semantic Segmentation

ADAS: A Direct Adaptation Strategy for Multi-Target Domain A...

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2022 ieee/CVF conference on computer vision and pattern recognition, CVPR 2022

作者： Lee, Seunghun Choi, Wonhyeok Kim, Changjae Choi, Minwoo Im, Sunghoon Dgist Department of Electrical Engineering and Computer Science Daegu Korea Republic of

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we present a direct adaptation strategy (ADAS), which aims to directly adapt a single model to multiple target domains in a semantic segmentation task without pretrained domain-specific models. To do so, we design a multi-target domain transfer network (MTDT-Net) that aligns visual attributes across domains by transferring the domain distinctive features through a new target adaptive denormalization (TAD) module. Moreover, we propose a bi-directional adaptive region selection (BARS) that reduces the attribute ambiguity among the class labels by adaptively selecting the regions with consistent feature statistics. We show that our single MTDT-Net can synthesize visually pleasing domain transferred images with complex driving datasets, and BARS effectively filters out the unnecessary region of training images for each target domain. With the collaboration of MTDT-Net and BARS, our ADAS achieves state-of-the-art performance for multi-target domain adaptation (MTDA). To the best of our knowledge, our method is the first MTDA method that directly adapts to multiple domains in semantic segmentation. © 2022 ieee.

关键词： Semantic Segmentation

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Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image Generative Models

Drop the GAN: In Defense of Patches Nearest Neighbors as Sin...

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2022 ieee/CVF conference on computer vision and pattern recognition, CVPR 2022

作者： Granot, Niv Feinstein, Ben Shocher, Assaf Bagon, Shai Irani, Michal The Weizmann Institute of Science Dept. of Computer Science and Applied Math Israel Israel

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Image manipulation dates back long before the deep learning era. The classical prevailing approaches were based on maximizing patch similarity between the input and generated output. Recently, single-image GANs were introduced as a superior and more sophisticated solution to image manipulation tasks. Moreover, they offered the opportunity not only to manipulate a given image, but also to generate a large and diverse set of different outputs from a single natural image. This gave rise to new tasks, which are considered 'GAN-only'. However, despite their impressiveness, single-image GANs require long training time (usually hours) for each image and each task and often suffer from visual artifacts. In this paper we revisit the classical patch-based methods, and show that - unlike previously believed - classical methods can be adapted to tackle these novel 'GAN-only' tasks. Moreover, they do so better and faster than single-image GAN-based methods. More specifically, we show that: (i) by introducing slight modifications, classical patch-based methods are able to unconditionally generate diverse images based on a single natural image;(ii) the generated output visual quality exceeds that of single-image GANs by a large margin (confirmed both quantitatively and qualitatively);(iii) they are orders of magnitude faster (runtime reduced from hours to seconds). 22This project received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 788535), and the Carolito Stiftung. Dr Bagon is a Robin Chemers Neustein AI Fellow. © 2022 ieee.

关键词： Training Deep learning Visualization Technological innovation computer vision Runtime Europe

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Brain Controlled Communication system using Machine Learning techniques of computer vision 5

Brain Controlled Communication system using Machine Learning...

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5th ieee International conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Sabharwal, Munish Sultania, Shantam Jindal, Harsh Galgotias University Greater Noida India Wissen Technology Banglore India Chandigarh University Dept. Of Computer Science And Engineering Mohali India

ISBN: (纸本)9798350330861

Nowadays computers have become a necessity for all computers have made a great leap for us and with the help of that we are able to move to a golden age of Artificial Intelligence. Artificial Intelligence or A.I has huge advantages and potentials for making the lives easy and more accessible. However, we as a society have to keep on innovating and developing new innovations and products to help different aspects of the community. The proposed system uses a brain-controlled interface, eye tracking along with a camera and screen for communication and training. The system requires input in the form of an image captured with the help of a camera. After the image is taken as an input, it will be used to perform eye-tracking and eye-blink detection. Both of these actions will be performed by employing a machine learning algorithm. The algorithm will process a morse code entered by the user, and then will enable the speech functionality using built-in speakers. Thus, rendering the system speak out whatever the user wanted to convey. The proposed system follows a coordinated approach for eye pattern detection and mapping. It detects the gazing spot of the eyes, and calculates the midpoint of the eye gaze. As a result, it predicts the eye blink pattern. This eye pattern recognition system is proposed to help out people with disabilities who find it difficult to communicate. © 2023 ieee.

关键词： Cameras

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Retina : Low-Power Eye Tracking with Event Camera and Spiking Hardware

Retina : Low-Power Eye Tracking with Event Camera and Spikin...

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

作者： Pietro Bonazzi Sizhen Bian Giovanni Lippolis Yawei Li Sadique Sheik Michele Magno ETH Zürich Inivation AG Synsense AG

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper introduces a neuromorphic dataset and methodology for eye tracking, harnessing event data captured streamed continuously by a Dynamic vision Sensor (DVS). The framework integrates a directly trained Spiking Neuron Network (SNN) regression model and leverages a state-of-the-art low power edge neuromorphic processor - Speck. First, it introduces a representative event-based eye-tracking dataset, "Ini-30," which was collected with two glass-mounted DVS cameras from thirty volunteers. Then, a SNN model, based on Integrate And Fire (IAF) neurons, named "Retina", is described , featuring only 64k parameters (6.63x fewer than 3ET) and achieving pupil tracking error of only 3.24 pixels in a 64x64 DVS input. The continuous regression output is obtained by means of temporal convolution using a non-spiking 1D filter slided across the output spiking layer over time. Retina is evaluated on the neuromorphic processor, showing an end-to-end power between 2.89-4.8 mW and a latency of 5.57-8.01 ms dependent on the time to slice the event-based video recording. The model is more precise than the latest event-based eye-tracking method, "3ET", on Ini-30, and shows comparable performance with significant model compression (35 times fewer MAC operations) in the synthetic dataset used in "3ET". We hope this work will open avenues for further investigation of close-loop neuromorphic solutions and true event-based training pursuing edge performance.

关键词： Neuromorphics Computational modeling Neurons Gaze tracking vision sensors Retina Cameras

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NTIRE 2024 Dense and Non-Homogeneous Dehazing Challenge Report

NTIRE 2024 Dense and Non-Homogeneous Dehazing Challenge Repo...

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

作者： Codruta O. Ancuti Cosmin Ancuti Florin-Alexandru Vasluianu Radu Timofte Yidi Liu Xingbo Wang Yurui Zhu Gege Shi Xin Lu Xueyang Fu Zheng-Jun Zha Wei Dong Han Zhou Ruiyi Wang Xiaohong Liu Guangtao Zhai Jun Chen Wei Song Yichang Gao Jiahao Xiong Hualiang Lin Xianger Li Dong Li Mohab Kishawy Ruibin Li Seyed Amirreza Mousavi Rana Rauf Yangyi Liu Huan Liu MingSheng Tu Kele Xu JiaWen Chen Qisheng Xu Tao Sun Jin Guo Ben Shao Tianli Liu Mohao Wu Xingzhuo Yan Minghan Fu Lehan Yang Xin Lin Lu Qi Jincen Song Xiaoqian Hu Linwei Tao Hongming Chen Xiang Chen Chuanlong Xie Zhao Zhang Junhu Wang Yanyan Wei Suiyi Zhao Shengeng Tang Sampada Malagi Amogh Joshi Nikhil Akalwadi Chaitra Desai Ramesh Ashok Tabib Uma Mudenagudi Wenjing Jiang Jagadeesh Kalyanshetti Vijayalaxmi Ashok Aralikatti Yashaswini P Nitish Upasi Dikshit Hegde Ujwala Patil Sujata C

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This study examines the results of the NTIRE 2024 Challenge on Dense and Non-Homogeneous Dehazing. Innovative methods were introduced and tested using a new image dataset named DNH-HAZE. The DNH-HAZE dataset comprises 50 pairs of authentic outdoor images showcasing dense and non-homogeneous haze alongside corresponding haze-free images of identical scenes. The haze was simulated using a professional setup designed to mirror real-world hazy conditions. The competition attracted 374 participants, with 16 teams presenting solutions for the final evaluation phase. The proposed solutions showed the leading edge of image dehazing technology.

关键词： computer vision Correlation Accuracy Image edge detection conferences computer architecture Network architecture

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Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection

Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Ob...

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

作者： Kaicheng Yu Tang Tao Hongwei Xie Zhiwei Lin Tingting Liang Bing Wang Peng Chen Dayang Hao Yongtao Wang Xiaodan Liang Autonomous Driving Lab Alibaba Group China Shenzhen Campus Sun Yat-sen University China Wangxuan Institute of Computer Technology Peking University China

To achieve autonomous driving, developing 3D detection fusion methods, which aim to fuse the camera and LiDAR information, has draw great research interest in recent years. As a common practice, people rely on large-scale datasets to fairly compare the performance of different methods. While these datasets have been carefully cleaned to ideally minimize any potential noise, we observe that they cannot truly reflect the data seen on a real autonomous vehicle, whose data tends to be noisy due to various reasons. This hinders the ability to simply estimate the robust performance under realistic noisy settings. To this end, we collect a series of real-world cases with noisy data distribution, and systematically formulate a robustness benchmark toolkit. It can simulate these cases on any clean dataset, which has the camera and LiDAR input modality. We showcase the effectiveness of our toolkit by establishing two novel robustness benchmarks on widely-adopted datasets, nuScenes and Waymo, then holistically evaluate the state-of-the-art fusion methods. We discover that: i) most fusion methods, when solely developed on these data, tend to fail inevitably when there is a disruption to the LiDAR input; ii) the improvement of the camera input is significantly inferior to the LiDAR one. We publish the robust fusion dataset, benchmark, detailed documents and instructions on https://***/robust-benchmark-website.

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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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PointOfView: A Multi-modal Network for Few-shot 3D Point Cloud Classification Fusing Point and Multi-view Image Features

PointOfView: A Multi-modal Network for Few-shot 3D Point Clo...

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

作者： Huantao Ren Jiyang Wang Minmin Yang Senem Velipasalar Electrical Engineering and Computer Science Dept Syracuse University Syracuse NY USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Most existing 3D point cloud analysis approaches employ traditional supervised methods, which require large amounts of labeled data, and data annotation is labor-intensive, and costly. On the other hand, although many existing works use either raw 3D point clouds or multiple 2D depth images, their joint use is relatively under-explored. To address these issues, we propose PointOfView, a novel, multi-modal few-shot 3D point cloud classification model, to classify never-before-seen classes with only a few annotated samples. A 2D multi-view learning branch is proposed for processing multiple projection images, and it contains two sub-branches to extract information at individual image level as well as among all six depth images. In addition, we propose a multi-scale 2D pooling layer, which employs various 2D max-pooling and 2D average pooling operations, with different pooling sizes. This allows fusing features at different scales. The second main branch processes raw 3D point clouds by first sorting them, and then using DGCNN to extract features. We perform within-dataset and cross-domain experiments on ModelNel40, ModelNet40-C and ScanobjectNN datasets, and compare with six state-of-the-art baselines. The results show that our approach outperforms all baselines in all experimental settings and achieve the state-of-the-art performance.

关键词： Point cloud compression Solid modeling computer vision Three-dimensional displays Shape Computational modeling conferences

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TransER: Hybrid Model and Ensemble-based Sequential Learning for Non-homogenous Dehazing

TransER: Hybrid Model and Ensemble-based Sequential Learning...

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

作者： Trung Hoang Haichuan Zhang Amirsaeed Yazdani Vishal Monga School of Electrical Engineering and Computer Science Pennsylvania State University Unversity Park USA

Image dehazing is one of the most challenging imaging inverse problems that estimates the haze-free images from hazy ones. While recent transformer/convolutional neural network-based methods have shown excellent performance in handling both homogeneous and non-homogeneous dehazing problems, these networks are often trained end-to-end to estimate the haze-free image directly and require a large number of parameters. In this work, we propose a novel, lightweight two-stage deep network for non-homogeneous dehazing. In particular, our proposed method, denoted as TransER, consists of two separate deep neural networks which are TransConv Fusion Dehaze (TFD) model in Stage I and Lightweight Ensemble Reconstruction (LER) network in Stage II. The first model (TFD) using transformer-based encoder and decoders generates two estimates of the haze-free image: a parameter-based dehazed output based on the physical modeling of the problem and a pseudo haze-free output generated directly by the model in an end-to-end fashion. LER in stage II reconstructs the final dehazed output fusing the two estimates from stage I. We incorporate knowledge distillation to develop a teacher network with the same architecture as LER, allowing it to supervise the intermediate features. Extensive experiments performed on challenging real and synthetic scene image datasets (NTIRE 2019-2023, and RESIDE-indoor) demonstrate that TransER can outperform many state-of-the-art competing methods while using a significantly lower number of parameters. The source code is available at https://***/trungpsu1210/TransER.

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