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Rip Current Segmentation: A Novel Benchmark and YOLOv8 Baseline Results

Rip Current Segmentation: A Novel Benchmark and YOLOv8 Basel...

引用

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

作者： Andrei Dumitriu Florin Tatui Florin Miron Radu Tudor Ionescu Radu Timofte Computer Vision Lab CAIDAS & IFI University of Würzburg Germany University of Bucharest Romania

Rip currents are the leading cause of fatal accidents and injuries on many beaches worldwide, emphasizing the importance of automatically detecting these hazardous surface water currents. In this paper, we address a novel task: rip current instance segmentation. We introduce a comprehensive dataset containing 2,466 images with newly created polygonal annotations for instance segmentation, used for training and validation. Additionally, we present a novel dataset comprising 17 drone videos (comprising about 24K frames) captured at 30 FPS, annotated with both polygons for instance segmentation and bounding boxes for object detection, employed for testing purposes. We train various versions of YOLOv8 for instance segmentation on static images and assess their performance on the test dataset (videos). The best results were achieved by the YOLOv8-nano model (runnable on a portable device), with an mAP50 of 88.94% on the validation dataset and 81.21% macro average on the test dataset. The results provide a baseline for future research in rip current segmentation. Our work contributes to the existing literature by introducing a detailed, annotated dataset, and training a deep learning model for instance segmentation of rip currents. The code, training details and the annotated dataset are made publicly available at https://***/Irikos/rip_currents.

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Learning unbiased classifiers from biased data with meta-learning

Learning unbiased classifiers from biased data with meta-lea...

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

作者： Ruggero Ragonesi Pietro Morerio Vittorio Murino Pattern Analysis and Computer Vision (PAVIS) - Istituto Italiano di Tecnologia Department of Computer Science University of Verona

It is well known that large deep architectures are powerful models when adequately trained, but may exhibit undesirable behavior leading to confident incorrect predictions, even when evaluated on slightly different test examples. Test data characterized by distribution shifts (from training data distribution), outliers, and adversarial samples are among the types of data affected by this problem. This situation worsens whenever data are biased, meaning that predictions are mostly based on spurious correlations present in the data. Unfortunately, since such correlations occur in the most of data, a model is prevented from correctly generalizing the considered classes. In this work, we tackle this problem from a meta-learning perspective. Considering the dataset as composed of unknown biased and unbiased samples, we first identify these two subsets by a pseudo-labeling algorithm, even if coarsely. Subsequently, we apply a bi-level optimization algorithm in which, in the inner loop, we look for the best parameters guiding the training of the two subsets, while in the outer loop, we train the final model taking benefit from augmented data generated using Mixup. Properly tuning the contributions of biased and unbiased data, together with the regularization introduced by the mixed data has proved to be an effective training strategy to learn unbiased models, showing superior generalization capabilities. Experimental results on synthetically and realistically biased datasets surpass state-of-the-art performance, as compared to existing methods.

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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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Attention Guidance Distillation Network for Efficient Image Super-Resolution

Attention Guidance Distillation Network for Efficient Image ...

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

作者： Hongyuan Wang Ziyan Wei Qingting Tang Shuli Cheng Liejun Wang Yongming Li School of Computer Science and Technology Xinjiang University Ürümqi China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Over the past decade, neural network-based super-resolution techniques have been developed on a large scale with impressive achievements. Many novel solutions have been proposed, among which lightweight solutions based on convolutional neural networks have been designed for applications in edge devices. To better realize this application, we propose a more lightweight attention guidance distillation network (AGDN). We design the attention guidance distillation block (AGDB) with more efficient space, channel and self-attention as the infrastructure of AGDN. Specifically, multi-level variance-aware spatial attention (MVSA) is designed to better capture structurally information-rich regions with new multi-scale convolution and local variance alignment. Reallocated contrast-aware channel attention (RCCA) is designed to enhance the processing of common information in all channels while redistributing weights across channels. Sparse global self-attention (SGSA) is introduced for selecting the most useful similarity values for image reconstruction. Extensive experiments demonstrate that AGDN strikes a better balance between performance and complexity compared to other models, achieving SOTA performance on several benchmark tests. In addition, our AGDN-S ranks first in the FLOPs track and second in the Parameters track of the NTIRE 2024 Efficient SR Challenge. The code is available at https://***/daydreamer2024/AGDN.

关键词： Runtime Limiting Convolution Superresolution Feature extraction pattern recognition Image restoration

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NTIRE 2024 Challenge on HR Depth from Images of Specular and Transparent Surfaces

NTIRE 2024 Challenge on HR Depth from Images of Specular and...

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

作者： Pierluigi Zama Ramirez Fabio Tosi Luigi Di Stefano Radu Timofte Alex Costanzino Matteo Poggi Samuele Salti Stefano Mattoccia Yangyang Zhang Cailin Wu Zhuangda He Shuangshuang Yin Jiaxu Dong Yangchenxu Liu Hao Jiang Jun Shi Yong A Yixiang Jin Dingzhe Li Bingxin Ke Anton Obukhov Tinafu Wang Nando Metzger Shengyu Huang Konrad Schindler Yachuan Huang Jiaqi Li Junrui Zhang Yiran Wang Zihao Huang Tianqi Liu Zhiguo Cao Pengzhi Li Jui-Lin Wang Wenjie Zhu Hui Geng Yuxin Zhang Long Lan Kele Xu Tao Sun Qisheng Xu Sourav Saini Aashray Gupta Sahaj K. Mistry Aryan Shukla Vinit Jakhetiya Sunil Jaiswal Yuejin Sun Zhuofan Zheng Yi Ning Jen-Hao Cheng Hou-I Liu Hsiang-Wei Huang Cheng-Yen Yang Zhongyu Jiang Yi-Hao Peng Aishi Huang Jenq-Neng Hwang

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reports on the NTIRE 2024 challenge on HR Depth From images of Specular and Transparent surfaces, held in conjunction with the New Trends in Image Restoration and Enhancement (NTIRE) workshop at CVPR 2024. This challenge aims to advance the research on depth estimation, specifically to address two of the main open issues in the field: high-resolution and non-Lambertian surfaces. The challenge proposes two tracks on stereo and single-image depth estimation, attracting about 120 registered participants. In the final testing stage, 2 and 8 participating teams submitted their models and fact sheets for the two tracks.

关键词： computer vision conferences Computational modeling Estimation Market research pattern recognition Image restoration

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CrisisHateMM: Multimodal Analysis of Directed and Undirected Hate Speech in Text-Embedded Images from Russia-Ukraine Conflict

CrisisHateMM: Multimodal Analysis of Directed and Undirected...

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

作者： Aashish Bhandari Siddhant B. Shah Surendrabikram Thapa Usman Naseem Mehwish Nasim Department of CSE Delhi Technological University India Department of Computer Science Virginia Tech USA School of Computer Science The University of Sydney Australia School of Physics Mathematics and Computing The University of Western Australia Australia College of Science and Engineering Flinders University Australia

Text-embedded images are frequently used on social media to convey opinions and emotions, but they can also be a medium for disseminating hate speech, propaganda, and extremist ideologies. During the Russia-Ukraine war, both sides used text-embedded images extensively to spread propaganda and hate speech. To aid in moderating such content, this paper introduces CrisisHateMM, a novel multimodal dataset of over 4,700 text-embedded images from the Russia-Ukraine conflict, annotated for hate and non-hate speech. The hate speech is annotated for directed and undirected hate speech, with directed hate speech further annotated for individual, community, and organizational targets. We benchmark the dataset using unimodal and multimodal algorithms, providing insights into the effectiveness of different approaches for detecting hate speech in text-embedded images. Our results show that multimodal approaches outperform unimodal approaches in detecting hate speech, highlighting the importance of combining visual and textual features. This work provides a valuable resource for researchers and practitioners in automated content moderation and social media analysis. The CrisisHateMM dataset and codes are made publicly available at https://***/aabhandari/CrisisHateMM.

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De-noised vision-language Fusion Guided by Visual Cues for E-commerce Product Search

De-noised Vision-language Fusion Guided by Visual Cues for E...

引用

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

作者： Zhizhang Hu Shasha Li Ming Du Arnab Dhua Douglas Gray University of California Merced Amazon Visual Search & AR

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In e-commerce applications, vision-language multimodal transformer models play a pivotal role in product search. The key to successfully training a multimodal model lies in the alignment quality of image-text pairs in the dataset. However, the data in practice is often automatically collected with minimal manual intervention. Hence the alignment of image-text pairs is far from ideal. In e-commerce, this misalignment can stem from noisy and redundant non-visual-descriptive text attributes in the product description. To address this, we introduce the MultiModal alignment-guided Learned Token Pruning (MM-LTP) method. MM-LTP employs token pruning, conventionally used for computational efficiency, to perform online text cleaning during multimodal model training. By enabling the model to discern and discard unimportant tokens, it is able to train with implicitly cleaned image-text pairs. We evaluate MM-LTP using a benchmark multimodal e-commerce dataset comprising over 710,000 unique Amazon products. Our evaluation hinges on visual search, a prevalent e-commerce feature. Through MM-LTP, we demonstrate that refining text tokens enhances the paired image branch's training, which leads to significantly improved visual search performance.

关键词： Training Visualization Computational modeling Refining Manuals Fasteners Transformers

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Advances in Self-Supervised Learning for Synthetic Aperture Sonar Data Processing, Classification, and pattern recognition

Advances in Self-Supervised Learning for Synthetic Aperture ...

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2023 MTS/ieee U.S. Gulf Coast, OCEANS 2023

作者： Sheffield, Brandon W. Bobe, Frank E. Marchand, Bradley Emigh, Matthew S. Naval Surface Warfare Center Panama City Division United States

ISBN: (纸本)9798218142186

Synthetic Aperture Sonar (SAS) imaging has become a crucial technology for underwater exploration because of its unique ability to maintain resolution at increasing ranges, a characteristic absent in conventional sonar techniques. However, the effective application of deep learning to SAS data processing is often limited due to the scarcity of labeled data. To address this challenge, this paper proposes MoCo-SAS that leverages self-supervised learning (SSL) for SAS data processing, classification, and pattern recognition. The experimental results demonstrate that MoCo-SAS significantly outperforms traditional supervised learning methods, as evidenced by significant improvements observed in terms of the F1-score. These findings highlight the potential of SSL in advancing the state-of-the-art in SAS data processing, offering promising avenues for enhanced underwater object detection and classification. © 2023 The Marine Technology society (MTS).

关键词： computer vision

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Leveraging Future Trajectory Prediction for Multi-Camera People Tracking

Leveraging Future Trajectory Prediction for Multi-Camera Peo...

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

作者： Yuntae Jeon Dai Quoc Tran Minsoo Park Seunghee Park Department of Global Smart City Sungkyunkwan University South Korea Global Engineering Insitute for Ultimate Society Sungkyunkwan University South Korea Sungkyun AI Research Institute Sungkyunkwan University South Korea School of Civil Architectural Engineering and Landscape Architecture Sungkyunkwan University South Korea

Artificial intelligence-based surveillance system, one of the essential systems for smart cities, plays a critical role in ensuring the safety and well-being of individuals. In this paper, we propose a real-time, low-computation cost Multi-Camera Multi-Target (MCMT) tracking system for people, leveraging deep-learning-based trajectory prediction with spatial-temporal information and social information. By predicting people’s future trajectories, our algorithm effectively handles object occlusion problems and maintains accurate tracking while keeping computational costs low. Our approach addresses object occlusion without relying on computationally expensive re-identification, and improves MCMT tracking performance using graph-based tracklet representation, and spectral clustering. As a result, our proposed approach is tested on the 2023 AI City Challenge Track 1 test dataset, automatically generated on the NVIDIA Omiverse Platform, our method achieves an IDF1 score of 0.6171 and real-time performance at 27.6 FPS. Code and pre-trained models are publicly available at https://***/yuntaeJ/SCIT-MCMT-Tracking.

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Motion Matters: Difference-based Multi-scale Learning for Infrared UAV Detection

Motion Matters: Difference-based Multi-scale Learning for In...

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

作者： Ruian He Shili Zhou Ri Cheng Yuqi Sun Weimin Tan Bo Yan School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

Unmanned Aerial Vehicle (UAV) detection in the wild is a challenging task due to the presence of background noise and the varying size of the object. To address these obstacles, we propose a novel learning framework for robust UAV detectors, which we call Difference-based Multi-scale Learning (DML). We argue that motion information matters in UAV detection because of the low recognition in one frame. Our method utilizes the frame difference of multiple previous frames, extracting motion information and blocking background noise. We also fuse multiple spatial-temporal scales for training and inferencing, enabling fusion from different sources. In addition, to better evaluate the performance of UAV detection in different scales, we propose Multi-Scale Average Precision (MSAP) metric to aggregate the detection accuracy over multiple scales. Through extensive experiments, we demonstrate that our proposed approach improves the detection accuracy of baseline models. Notably, we achieve SOTA performance in the 3rd Anti-UAV Challenge, with 2nd place in Track 2 and 4th place in Track 1. 1

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