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

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

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

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

ISBN: (纸本)9798350302493

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. © 2023 ieee.

关键词： Antennas

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Universal Watermark Vaccine: Universal Adversarial Perturbations for Watermark Protection

Universal Watermark Vaccine: Universal Adversarial Perturbat...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Chen, Jianbo Liu, Xinwei Liang, Siyuan Jia, Xiaojun Xun, Yuan Hunan University College of Computer Science and Electronic Engineering Hunan China Chinese Academy of Sciences Institute of Information Engineering Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9798350302493

As computing ability continues to rapidly develop, neural networks have found widespread use in various fields. However, in the realm of visible watermarking for image copyright protection, neural networks have made image protection through watermarking less effective. Some research has even shown that watermarks can be removed without damaging to the original image, posing a significant threat to digital copyright protection. In response, the community has introduced adversarial perturbations for watermark protection, but these are sample-specific and time-consuming in real-world scenarios. To address this issue, we propose a new universal adversarial perturbation for watermark removal networks that offers two options. The first option involves adding perturbations to the entire host image, bringing the output of the watermark removal network closer to the original image and providing protection. The second option involves adding perturbations only to the watermark position, reducing the impact of the perturbation on the image and enhancing stealthiness. Our experiments demonstrate that our method effectively resists watermark removal networks and has good generalizability across different images. © 2023 ieee.

关键词： Copyrights

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Efficient Multi-Lens Bokeh Effect Rendering and Transformation

Efficient Multi-Lens Bokeh Effect Rendering and Transformati...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Seizinger, Tim Conde, Marcos V. Kolmet, Manuel Bishop, Tom E. Timofte, Radu University of Würzburg Computer Vision Lab CAIDAS & IFI Germany Glass Imaging Inc. CA United States

ISBN: (纸本)9798350302493

Many advancements of mobile cameras aim to reach the visual quality of professional DSLR cameras. Great progress was shown over the last years in optimizing the sharp regions of an image and in creating virtual portrait effects with artificially blurred backgrounds. Bokeh is the aesthetic quality of the blur in out-of-focus areas of an image. This is a popular technique among professional photographers, and for this reason, a new goal in computational photography is to optimize the Bokeh effect *** paper introduces EBokehNet, a efficient state-of-the-art solution for Bokeh effect transformation and rendering. Our method can render Bokeh from an all-in-focus image, or transform the Bokeh of one lens to the effect of another lens without harming the sharp foreground regions in the image. Moreover we can control the shape and strength of the effect by feeding the lens properties i.e. type (Sony or Canon) and aperture, into the neural network as an additional input. Our method is a winning solution at the NTIRE 2023 Lens-to-Lens Bokeh Effect Transformation Challenge, and state-of-the-art at the EBB benchmark. © 2023 ieee.

关键词： Color photography

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Exploring Joint Embedding Architectures and Data Augmentations for Self-Supervised Representation Learning in Event-Based vision

Exploring Joint Embedding Architectures and Data Augmentatio...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Barchid, Sami Mennesson, José Djéraba, Chaabane Univ. Lille Cnrs Centrale Lille Umr 9189 CRIStAL France Univ. Lille Centre for Digital Systems Imt Nord Europe Institut Mines-Télécom LilleF-59000 France

ISBN: (纸本)9798350302493

This paper proposes a self-supervised representation learning (SSRL) framework for event-based vision, which leverages various lightweight convolutional neural networks (CNNs) including 2D-, 3D-, and Spiking CNNs. The method uses a joint embedding architecture to maximize the agreement between features extracted from different views of the same event sequence. Popular event data augmentation techniques are employed to design an efficient augmentation policy for event-based SSRL, and we provide novel data augmentation methods to enhance the pretraining pipeline. Given the novelty of SSRL for event-based vision, we elaborate standard evaluation protocols and use them to evaluate our approach. Our study demonstrates that pretrained CNNs acquire effective and transferable features, enabling them to achieve competitive performance in object or action recognition across various commonly used event-based datasets, even in a low-data regime. This paper also conducts an experimental analysis of the extracted features regarding the Uniformity-Tolerance tradeoff to assess their quality, and measure the similarity of representations using linear Center Kernel Alignement. These quantitative measurements reinforce our observations from the performance benchmarks and show substantial differences between the learned representations of all types of CNNs despite being optimized with the same approach. © 2023 ieee.

关键词： Embeddings

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WEDGE: A multi-weather autonomous driving dataset built from generative vision-language models

WEDGE: A multi-weather autonomous driving dataset built from...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Marathe, Aboli Ramanan, Deva Walambe, Rahee Kotecha, Ketan Carnegie Mellon University Machine Learning Department PA United States Carnegie Mellon University Robotics Institute PA United States India India

ISBN: (纸本)9798350302493

The open road poses many challenges to autonomous perception, including poor visibility from extreme weather conditions. Models trained on good-weather datasets frequently fail at detection in these out-of-distribution settings. To aid adversarial robustness in perception, we introduce WEDGE (WEather images by DALL-E GEneration): a synthetic dataset generated with a vision-language generative model via prompting. WEDGE consists of 3360 images in 16 extreme weather conditions manually annotated with 16513 bounding boxes, supporting research in the tasks of weather classification and 2D object detection. We have analyzed WEDGE from research standpoints, verifying its effectiveness for extreme-weather autonomous perception. We establish baseline performance for classification and detection with 53.87% test accuracy and 45.41 mAP. Most importantly, WEDGE can be used to fine-tune state-of-the-art detectors, improving SOTA performance on real-world weather benchmarks (such as DAWN) by 4.48 AP for well-generated classes like trucks. WEDGE has been collected under OpenAI's terms 1 of use and is released for public use under the CC BY-NC-SA 4.0 license. The repository for this work and dataset is available at https://***/WEDGE. © 2023 ieee.

关键词： Benchmarking

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A New Dataset and Approach for Timestamp Supervised Action Segmentation Using Human Object Interaction

A New Dataset and Approach for Timestamp Supervised Action S...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Sayed, Saif Ghoddoosian, Reza Trivedi, Bhaskar Athitsos, Vassilis University of Texas Arlington United States

ISBN: (纸本)9798350302493

This paper focuses on leveraging Human Object Interaction (HOI) information to improve temporal action segmentation under timestamp supervision, where only one frame is annotated for each action segment. This information is obtained from an off-the-shelf pre-trained HOI detector, that requires no additional HOI-related annotations in our experimental datasets. Our approach generates pseudo labels by expanding the annotated timestamps into intervals and allows the system to exploit the spatio-temporal continuity of human interaction with an object to segment the video. We also propose the (3+1)Real-time Cooking (ReC)1 dataset as a realistic collection of videos from 30 participants cooking 15 breakfast items. Our dataset has three main properties: 1) to our knowledge, the first to offer synchronized third and first person videos, 2) it incorporates diverse actions and tasks, and 3) it consists of high resolution frames to detect fine-grained information. In our experiments we benchmark state-of-the-art segmentation methods under different levels of supervision on our dataset. We also quantitatively show the advantages of using HOI information, as our framework improves its baseline segmentation method on several challenging datasets with varying viewpoints, providing improvements of up to 10.9% and 5.3% in F1 score and frame-wise accuracy respectively. © 2023 ieee.

关键词： computer vision

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Weakly Supervised Visual Question Answer Generation

Weakly Supervised Visual Question Answer Generation

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Alampalle, Charani Hegde, Shamanthak Jahagirdar, Soumya Gangisetty, Shankar AlphaICs Bengaluru India Kle Technological University Hubballi India Cvit Iiit Hyderabad Hyderabad India Iiit Hyderabad Hyderabad India

ISBN: (纸本)9798350302493

Growing interest in conversational agents promote two-way human-computer communications involving asking and answering visual questions have become an active area of research in AI. Thus, generation of visual question-answer pair(s) becomes an important and challenging task. To address this issue, we propose a weakly-supervised visual question answer generation method that generates a relevant question-answer pairs for a given input image and associated caption. Most of the prior works are supervised and depend on the annotated question-answer datasets. In our work, we present a weakly supervised method that synthetically generates question-answer pairs procedurally from visual information and captions. The proposed method initially extracts list of answer words, then does nearest question generation that uses the caption and answer word to generate synthetic question. Next, the relevant question generator converts the nearest question to relevant language question by dependency parsing and in-order tree traversal, finally, fine-tune a ViLBERT model with the question-answer pair(s) generated at end. We perform an exhaustive experimental analysis on VQA dataset and see that our model significantly outperform SOTA methods on BLEU scores. We also show the results wrt baseline models and ablation study. © 2023 ieee.

关键词： computer vision

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Where are they looking in the 3D space?

Where are they looking in the 3D space?

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Horanyi, Nora Zheng, Linfang Chong, Eunji Leonardis, Aleš Chang, Hyung Jin University of Birmingham School of Computer Science United Kingdom *** Inc.

ISBN: (纸本)9798350302493

We propose a novel depth-aware joint attention target estimation framework that estimates the attention target in 3D space. Our goal is to mimic human's ability to understand where each person is looking in their proximity. In this work, we tackle the previously unexplored problem of utilising a depth prior along with a 3D joint FOV probability map to estimate the joint attention target of people in the scene. We leverage the insight that besides the 2D image content, strong gaze-related constraints exist in the depth order of the scene and different subject-specific attributes. Extensive experiments show that our method outperforms favourably against existing joint attention target estimation methods on the VideoCoAtt benchmark dataset. Despite the proposed framework being designed for joint attention target estimation, we show that it outperforms single attention target estimation methods on both the GazeFollow image and the VideoAttentionTarget video benchmark datasets. © 2023 ieee.

关键词： computer vision

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Spatial-Temporal Graph-Based AU Relationship Learning for Facial Action Unit Detection

Spatial-Temporal Graph-Based AU Relationship Learning for Fa...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Wang, Zihan Song, Siyang Luo, Cheng Zhou, Yuzhi Wu, Shiling Xie, Weicheng Shen, Linlin Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China University of Leicester School of Computing and Mathematical Sciences United Kingdom

ISBN: (纸本)9798350302493

This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence;(ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation;and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition. Our code is publicly available at https://***/wzh125/ABAW-5. © 2023 ieee.

关键词： Graphic methods

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End-to-End High-Risk Tackle Detection System for Rugby

End-to-End High-Risk Tackle Detection System for Rugby

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

作者： Nonaka, Naoki Fujihira, Ryo Nishio, Monami Murakami, Hidetaka Tajima, Takuya Yamada, Mutsuo Maeda, Akira Seita, Jun RIKEN Informat R&D & Strategy Headquarters Adv Data Sci Project Tokyo Japan Murakami Surg Hosp Nagasaki Japan Univ Miyazaki Fac Med Miyazaki Japan Ryutsu Keizai Univ Fac Hlth & Sport Sci Ryugasaki Japan Hakata Knee & Sports Clin Fukuoka Japan Kurume Univ Fac Human Hlth Kurume Fukuoka Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Reducing risk of severe injury such as concussion is a high priority for any contact sports. In rugby, Head Injury Assessment (HIA) protocol has been introduced to identify and protect players showing symptoms of concussion and having potential risk of concussion. However, on-field decisions by officials are sometimes difficult and subjective, and HIA is affordable only for elite leagues since it requires medical specialists. To make rugby matches more safe, we aim to develop a system to detect high-risk tackles, potential triggers of concussion, based on deep learning models. Our system takes rugby match video, then first identifies frame with tackle, subsequently detects location of tackle and estimate pose of the ball carrier and the tackler, and finally evaluate the risk of tackle using posture pair of players. Among the model combinations we have examined, the best performance was achieved with the combination of ResNet (2+1)D as tackle frame selection model, RetinaNet as tackle detection model and CenterTrack as pose estimation model. Evaluation using test data, a set of short clips from broadcasted rugby match videos, showed our system was able to detect 50% of high-risk tackles without any human intervention. This result opens a path for automated systems to detect high-risk events, leading to less expensive and more objective monitoring not only for rugby but also for any contact sports.

关键词： Deep learning computer vision Protocols Head conferences Pose estimation pattern recognition

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