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Dense Multitask Learning to Reconfigure Comics

Dense Multitask Learning to Reconfigure Comics

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Bhattacharjee, Deblina Susstrunk, Sabine Salzmann, Mathieu Epfl School of Computer and Communication Sciences Switzerland

ISBN: (纸本)9798350302493

In this paper, we develop a MultiTask Learning (MTL) model to achieve dense predictions for comics panels to, in turn, facilitate the transfer of comics from one publication channel to another by assisting authors in the task of reconfiguring their narratives. Our MTL method can successfully identify the semantic units as well as the embedded notion of 3D in comics panels. This is a significantly challenging problem because comics comprise disparate artistic styles, illustrations, layouts, and object scales that depend on the author's creative process. Typically, dense image-based prediction techniques require a large corpus of data. Finding an automated solution for dense prediction in the comics domain, therefore, becomes more difficult with the lack of ground-truth dense annotations for the comics images. To address these challenges, we develop the following solutions- we leverage a commonly-used strategy known as unsupervised image-to-image translation, which allows us to utilize a large corpus of real-world annotations;- we utilize the results of the translations to develop our multitasking approach that is based on a vision transformer backbone and a domain transferable attention module;-we study the feasibility of integrating our MTL dense-prediction method with an existing retargeting method, thereby reconfiguring comics. © 2023 ieee.

关键词： Forecasting

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SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data

SynthASpoof: Developing Face Presentation Attack Detection B...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Fang, Meiling Huber, Marco Damer, Naser Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany TU Darmstadt Department of Computer Science Darmstadt Germany

ISBN: (纸本)9798350302493

Recently, significant progress has been made in face presentation attack detection (PAD), which aims to secure face recognition systems against presentation attacks, owing to the availability of several face PAD datasets. However, all available datasets are based on privacy and legally-sensitive authentic biometric data with a limited number of subjects. To target these legal and technical challenges, this work presents the first synthetic-based face PAD dataset, named SynthASpoof, as a large-scale PAD development dataset. The bona fide samples in SynthASpoof are synthetically generated and the attack samples are collected by presenting such synthetic data to capture systems in a real attack scenario. The experimental results demonstrate the feasibility of using SynthASpoof for the development of face PAD. Moreover, we boost the performance of such a solution by incorporating the domain generalization tool MixStyle into the PAD solutions. Additionally, we showed the viability of using synthetic data as a supplement to enrich the diversity of limited authentic training data and consistently enhance PAD performances. The SynthASpoof dataset, containing 25,000 bona fide and 78,800 attack samples, the implementation, and the pre-trained weights are made publicly available https://***/meilfang/***. © 2023 ieee.

关键词： Face recognition

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The Manifold Hypothesis for Gradient-Based Explanations

The Manifold Hypothesis for Gradient-Based Explanations

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Bordt, Sebastian Upadhyay, Uddeshya Akata, Zeynep Von Luxburg, Ulrike University of Tübingen Germany Tübingen Ai Center Germany

ISBN: (纸本)9798350302493

When do gradient-based explanation algorithms provide perceptually-aligned explanations? We propose a criterion: the feature attributions need to be aligned with the tangent space of the data manifold. To provide evidence for this hypothesis, we introduce a framework based on variational autoencoders that allows to estimate and generate image manifolds. Through experiments across a range of different datasets - MNIST, EMNIST, CIFAR10, X-ray pneumonia and Diabetic Retinopathy detection - we demonstrate that the more a feature attribution is aligned with the tangent space of the data, the more perceptually-aligned it tends to be. We then show that the attributions provided by popular post-hoc methods such as Integrated Gradients and SmoothGrad are more strongly aligned with the data manifold than the raw gradient. Adversarial training also improves the alignment of model gradients with the data manifold. As a consequence, we suggest that explanation algorithms should actively strive to align their explanations with the data manifold. An extended version of this paper is available at https://***/abs/2206.07387. © 2023 ieee.

关键词： computer vision

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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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Open-set Fine-grained Retrieval via Prompting vision-Language Evaluator

Open-set Fine-grained Retrieval via Prompting Vision-Languag...

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

作者： Wang, Shijie Chang, Jianlong Li, Haojie Wang, Zhihui Ouyang, Wanli Tian, Qi Dalian Univ Technol Int Sch Informat Sci & Engn Dalian Peoples R China Huawei Cloud & AI Shenzhen Peoples R China Shandong Univ Sci & Technol Coll Comp & Engn Qingdao Peoples R China Univ Sydney Sense Time Comp Vis Res Grp Sydney NSW Australia

ISBN: (纸本)9798350301298

Open-set fine-grained retrieval is an emerging challenge that requires an extra capability to retrieve unknown subcategories during evaluation. However, current works focus on close-set visual concepts, where all the subcategories are pre-defined, and make it hard to capture discriminative knowledge from unknown subcategories, consequently failing to handle unknown subcategories in open-world scenarios. In this work, we propose a novel Prompting vision-Language Evaluator (PLEor) framework based on the recently introduced contrastive language-image pretraining (CLIP) model, for open-set fine-grained retrieval. PLEor could leverage pre-trained CLIP model to infer the discrepancies encompassing both pre-defined and unknown subcategories, called category-specific discrepancies, and transfer them to the backbone network trained in the close-set scenarios. To make pre-trained CLIP model sensitive to category-specific discrepancies, we design a dual prompt scheme to learn a vision prompt specifying the category-specific discrepancies, and turn random vectors with category names in a text prompt into category-specific discrepancy descriptions. Moreover, a vision-language evaluator is proposed to semantically align the vision and text prompts based on CLIP model, and reinforce each other. In addition, we propose an open-set knowledge transfer to transfer the category-specific discrepancies into the backbone network using knowledge distillation mechanism. Quantitative and qualitative experiments show that our PLEor achieves promising performance on open-set fine-grained datasets.

关键词： vision applications and systems

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Trade-off between Robustness and Accuracy of vision Transformers

Trade-off between Robustness and Accuracy of Vision Transfor...

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

作者： Li, Yanxi Xu, Chang Univ Sydney Fac Engn Sch Comp Sci Sydney NSW Australia

ISBN: (纸本)9798350301298

Although deep neural networks (DNNs) have shown great successes in computer vision tasks, they are vulnerable to perturbations on inputs, and there exists a trade-off between the natural accuracy and robustness to such perturbations, which is mainly caused by the existence of robust non-predictive features and non-robust predictive features. Recent empirical analyses find vision Transformers (ViTs) are inherently robust to various kinds of perturbations, but the aforementioned trade-off still exists for them. In this work, we propose Trade-off between Robustness and Accuracy of vision Transformers (TORA-ViTs), which aims to efficiently transfer ViT models pretrained on natural tasks for both accuracy and robustness. TORA-ViTs consist of two major components, including a pair of accuracy and robustness adapters to extract predictive and robust features, respectively, and a gated fusion module to adjust the trade-off. The gated fusion module takes outputs of a pretrained ViT block as queries and outputs of our adapters as keys and values, and tokens from different adapters at different spatial locations are compared with each other to generate attention scores for a balanced mixing of predictive and robust features. Experiments on ImageNet with various robust benchmarks show that our TORA-ViTs can efficiently improve the robustness of naturally pretrained ViTs while maintaining competitive natural accuracy. Our most balanced setting (TORA-ViTs with lambda = 0.5) can maintain 83.7% accuracy on clean ImageNet and reach 54.7% and 38.0% accuracy under FGSM and PGD white-box attacks, respectively. In terms of various ImageNet variants, it can reach 39.2% and 56.3% accuracy on ImageNet-A and ImageNet-R and reach 34.4% mCE on ImageNet-C.

关键词： Adversarial attack and defense

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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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BEVHeight: A Robust Framework for vision-based Roadside 3D Object Detection

BEVHeight: A Robust Framework for Vision-based Roadside 3D O...

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

作者： Yang, Lei Yu, Kaicheng Tang, Tao Li, Jun Yuan, Kun Wang, Li Zhang, Xinyu Chen, Peng Tsinghua Univ State Key Lab Automot Safety & Energy Beijing Peoples R China Alibaba Grp Autonomous Driving Lab Hangzhou Peoples R China Sun Yat Sen Univ Shenzhen Campus Guangzhou Peoples R China Peking Univ Ctr Machine Learning Res Beijing Peoples R China

ISBN: (纸本)9798350301298

While most recent autonomous driving system focuses on developing perception methods on ego-vehicle sensors, people tend to overlook an alternative approach to leverage intelligent roadside cameras to extend the perception ability beyond the visual range. We discover that the state-of-the-art vision-centric bird's eye view detection methods have inferior performances on roadside cameras. This is because these methods mainly focus on recovering the depth regarding the camera center, where the depth difference between the car and the ground quickly shrinks while the distance increases. In this paper, we propose a simple yet effective approach, dubbed BEVHeight, to address this issue. In essence, instead of predicting the pixel-wise depth, we regress the height to the ground to achieve a distance-agnostic formulation to ease the optimization process of camera-only perception methods. On popular 3D detection benchmarks of roadside cameras, our method surpasses all previous vision-centric methods by a significant margin. The code is available at https://***/ADLab-AutoDrive/BEVHeight.

关键词： Autonomous driving

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PEFAT: Boosting Semi-supervised Medical Image Classification via Pseudo-loss Estimation and Feature Adversarial Training

PEFAT: Boosting Semi-supervised Medical Image Classification...

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

作者： Zeng, Qingjie Xie, Yutong Lu, Zilin Xia, Yong Northwestern Polytech Univ Sch Comp Sci & Engn Xian Peoples R China Univ Adelaide Adelaide SA 5005 Australia

ISBN: (纸本)9798350301298

Pseudo-labeling approaches have been proven beneficial for semi-supervised learning (SSL) schemes in computer vision and medical imaging. Most works are dedicated to finding samples with high-confidence pseudo-labels from the perspective of model predicted probability. Whereas this way may lead to the inclusion of incorrectly pseudo-labeled data if the threshold is not carefully adjusted. In addition, low-confidence probability samples are frequently disregarded and not employed to their full potential. In this paper, we propose a novel Pseudo-loss Estimation and Feature Adversarial Training semi-supervised framework, termed as PEFAT, to boost the performance of multiclass and multi-label medical image classification from the point of loss distribution modeling and adversarial training. Specifically, we develop a trustworthy data selection scheme to split a high-quality pseudo-labeled set, inspired by the dividable pseudo-loss assumption that clean data tend to show lower loss while noise data is the opposite. Instead of directly discarding these samples with low-quality pseudo-labels, we present a novel regularization approach to learn discriminate information from them via injecting adversarial noises at the feature-level to smooth the decision boundary. Experimental results on three medical and two natural image benchmarks validate that our PEFAT can achieve a promising performance and surpass other state-of-the-art methods. The code is available at https://***/maxwell0027/PEFAT.

关键词： cell microscopy Medical and biological vision

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Accurate 3D Hand Pose Estimation for Whole-Body 3D Human Mesh Estimation

Accurate 3D Hand Pose Estimation for Whole-Body 3D Human Mes...

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

作者： Moon, Gyeongsik Choi, Hongsuk Lee, Kyoung Mu Seoul Natl Univ Dept ECE & ASRI Seoul South Korea Seoul Natl Univ IPAI Seoul South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Whole-body 3D human mesh estimation aims to reconstruct the 3D human body, hands, and face simultaneously. Although several methods have been proposed, accurate prediction of 3D hands, which consist of 3D wrist and fingers, still remains challenging due to two reasons. First, the human kinematic chain has not been carefully considered when predicting the 3D wrists. Second, previous works utilize body features for the 3D fingers, where the body feature barely contains finger information. To resolve the limitations, we present Hand4Whole, which has two strong points over previous works. First, we design Pose2Pose, a module that utilizes joint features for 3D joint rotations. Using Pose2Pose, Hand4Whole utilizes hand MCP joint features to predict 3D wrists as MCP joints largely contribute to 3D wrist rotations in the human kinematic chain. Second, Hand4Whole discards the body feature when predicting 3D finger rotations. Our Hand4Whole is trained in an end-to-end manner and produces much better 3D hand results than previous whole-body 3D human mesh estimation methods. The codes are available here (1).

关键词： Wrist computer vision Three-dimensional displays Codes Face recognition conferences Pose estimation

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