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

作者： Bhandari, Aashish Shah, Siddhant B. Thapa, Surendrabikram Naseem, Usman Nasim, Mehwish Delhi Technological University Department of CSE India Virginia Tech Department of Computer Science United States 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

ISBN: (纸本)9798350302493

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. © 2023 ieee.

关键词： Modal analysis

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Towards Evaluating Explanations of vision Transformers for Medical Imaging

Towards Evaluating Explanations of Vision Transformers for M...

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

作者： Komorowski, Piotr Baniecki, Hubert Biecek, Przemyslaw University of Warsaw Poland Warsaw University of Technology Poland

ISBN: (纸本)9798350302493

As deep learning models increasingly find applications in critical domains such as medical imaging, the need for transparent and trustworthy decision-making becomes paramount. Many explainability methods provide insights into how these models make predictions by attributing importance to input features. As vision Transformer (ViT) becomes a promising alternative to convolutional neural networks for image classification, its interpretability remains an open research question. This paper investigates the performance of various interpretation methods on a ViT applied to classify chest X-ray images. We introduce the notion of evaluating faithfulness, sensitivity, and complexity of ViT explanations. The obtained results indicate that Layerwise relevance propagation for transformers outperforms Local interpretable model-agnostic explanations and Attention visualization, providing a more accurate and reliable representation of what a ViT has actually learned. Our findings provide insights into the applicability of ViT explanations in medical imaging and highlight the importance of using appropriate evaluation criteria for comparing them. © 2023 ieee.

关键词： Decision making

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OTB-morph: One-Time Biometrics via Morphing applied to Face Templates

OTB-morph: One-Time Biometrics via Morphing applied to Face ...

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22nd ieee/CVF Winter conference on Applications of computer vision (WACV)

作者： Ghafourian, Mahdi Fierrez, Julian Vera-Rodriguez, Ruben Serna, Ignacio Morales, Aythami Univ Autonoma Madrid BiDA Lab Biometr & Data Pattern Analyt Madrid Spain

ISBN: (纸本)9781665458245

Cancelable biometrics refers to a group of techniques in which the biometric inputs are transformed intentionally using a key before processing or storage. This transformation is repeatable enabling subsequent biometric comparisons. This paper introduces a new scheme for cancelable biometrics aimed at protecting the templates against potential attacks, applicable to any biometric-based recognition system. Our proposed scheme is based on timevarying keys obtained from morphing random biometric information. An experimental implementation of the proposed scheme is given for face biometrics. The results confirm that the proposed approach is able to withstand against leakage attacks while improving the recognition performance.

关键词： Visualization computer vision Face recognition conferences Aging Servers Cryptography

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INDIFACE: Illuminating India's Deepfake Landscape with a Comprehensive Synthetic Dataset 18

INDIFACE: Illuminating India's Deepfake Landscape with a Com...

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18th International conference on Automatic Face and Gesture recognition (FG)

作者： Kuckreja, Kartik Hoque, Ximi Poddar, Nishit Reddy, Shukesh Dhall, Abhinav Das, Abhijit Birla Inst Technol & Sci Pilani Hyderabad Campus Pilani India Indian Inst Technol Ropar India

ISBN: (纸本)9798350394948;9798350394955

Due to the recent progress in Deepfake generation, several datasets and manipulation techniques have been proposed in the recent literature with various effective face-swap and face-reenactment methods. Deepfake is an emerging threat to society and government as it can jeopardize law enforcement and cause personal loss. Investigations in the literature established that demographic variation had impacted the performance of Deepfake detection. To date, Deepfake detection has not been studied in the Indian context;hence, in this work, we proposed a Deepfake dataset INDIFACE entirely with Indian subjects. We have collected 101 original videos and used two different manipulation techniques for Deepfake generation. We provide detailed benchmarking with state-of-the-art methods on Deepfake datasets, showcasing that the existing model is insufficient to detect Deepfake detection for the Indian scenario. Hence, more attention is required to this area of research. The proposed dataset INDIFACE is publicly available at

关键词： computer vision

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NTIRE 2022 Spectral Demosaicing Challenge and Data Set

NTIRE 2022 Spectral Demosaicing Challenge and Data Set

引用

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

作者： Arad, Boaz Timofte, Radu Yahel, Rony Morag, Nimrod Bernat, Amir Wu, Yaqi Wu, Xun Fan, Zhihao Xia, Chenjie Zhang, Feng Liu, Shuai Li, Yongqiang Feng, Chaoyu Lei, Lei Zhang, Mingwei Feng, Kai Zhang, Xun Yao, Jiaxin Zhao, Yongqiang Ma, Suina He, Fan Dong, Yangyang Yu, Shufang Qiu, Difa Liu, Jinhui Bi, Mengzhao Song, Beibei Sun, WenFang Zheng, Jiesi Zhao, Bowen Cao, Yanpeng Yang, Jiangxin Cao, Yanlong Kong, Xiangyu Yu, Jingbo Xue, Yuanyang Xie, Zheng Oddity Tech Ltd New York NY USA Voyage81 Ltd New York NY USA Univ Wurzburg Ctr Artificial Intelligence & Data Sci Wurzburg Germany Swiss Fed Inst Technol Computat Vision Lab Zurich Switzerland Acad Coll Tel Aviv Yaffo Tel Aviv Israel Tel Aviv Univ Tel Aviv Israel Harbin Inst Technol Harbin 150001 Peoples R China Tsinghua Univ Beijing 100084 Peoples R China Univ Shanghai Sci & Technol Shanghai 200093 Peoples R China Zhejiang Univ Hangzhou 310027 Peoples R China Xiaomi Inc Beijing Peoples R China Northwestern Polytech Univ Xian Peoples R China Changan Univ Xian Peoples R China Xidian Univ Xian Peoples R China Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power & Mech Syst Hangzhou 310027 Peoples R China Zhejiang Univ Sch Mech Engn Key Lab Adv Mfg Technol Zhejiang Prov Hangzhou 310027 Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper presents the first challenge on demosaicing of natural spectral images for snapshot hyperspectral imaging systems (HIS) which utilize a multi-spectral filer array (MSFA), i.e., the recovery of whole-scene hyperspectral information from spatially sub-sampled hyperspectral information. This challenge expands the "ARAD_1K" data set to a first-of-its-kind large-scale data set for multi-spectral filter array demosaicing of natural scenes containing 1,000 images. Challenge participants were required to recover hyperspectral information from synthetically generated MSFA images simulating capture by a known calibrated snapshot mosaic hyperspectral camera. The challenge was attended by 157 teams, with 29 teams competing in the final testing phase, 7 of which provided detailed descriptions of their methodology which are included in this report. The performance of these submissions is reviewed and provided here as a gauge for the current state-of-the-art in multi-spectral filter array demosaicing of natural images.

关键词： Performance evaluation Training Runtime Neural networks Cameras Real-time systems pattern recognition

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A Data-Centric Solution to NonHomogeneous Dehazing via vision Transformer

A Data-Centric Solution to NonHomogeneous Dehazing via Visio...

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

作者： Liu, Yangyi Liu, Huan Li, Liangyan Wu, Zijun Chen, Jun McMaster University Hamilton Canada China Telecom Research Institute Shanghai China

ISBN: (纸本)9798350302493

Recent years have witnessed an increased interest in image dehazing. Many deep learning methods have been proposed to tackle this challenge, and have made significant accomplishments dealing with homogeneous haze. However, these solutions cannot maintain comparable performance when they are applied to images with non-homogeneous haze, e.g., NH-HAZE23 dataset introduced by NTIRE challenge. One of the reasons for such failures is that non-homogeneous haze does not obey one of the assumptions that is required for modeling homogeneous haze. In addition, a large number of pairs of non-homogeneous hazy image and the clean counterpart is required using traditional end-to-end training approaches, while NH-HAZE23 dataset is of limited quantities. Although it is possible to augment the NH-HAZE23 dataset by leveraging other non-homogeneous dehazing datasets, we observe that it is necessary to design a proper data-preprocessing technique that reduces the distribution gaps between the target dataset and the augmented one. This finding indeed aligns with the essence of data-centric AI. With a novel network architecture and a principled data-preprocessing approach that systematically enhances data quality, we present an innovative dehazing method. Specifically, we apply RGB-channel-wise transformations on the augmented datasets, and incorporate the state-of-the-art transformers as the backbone in the two-branch framework. We conduct extensive experiments and ablation studies to demonstrate the effectiveness of our proposed method. The source code is available at https://***/yangyiliu21/ntire2023-ITBdehaze. © 2023 ieee.

关键词： Network architecture

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Relational Edge-Node Graph Attention Network for Classification of Micro-Expressions

Relational Edge-Node Graph Attention Network for Classificat...

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

作者： Rakesh Kumar, Ankith Jain Bhanu, Bir University of California Department of Electrical and Computer Engineering Riverside United States

ISBN: (纸本)9798350302493

Facial micro-expressions (MEs) refer to subtle, transient, and involuntary muscle movements expressing a person's true feelings. This paper presents a novel two-stream relational edge-node graph attention network-based approach to classify MEs in a video by selecting the high-intensity frames and edge-node features that can provide valuable information about the relationship between nodes and structural information in a graph structure. The paper examines the impact of different edge-node features and their relationships on the graphs. The first step involves extracting high-intensity-emotion frames from the video using optical flow. Second, node feature embeddings are calculated using the node location coordinate features and the patch size information of the optical flow across each node location. Additionally, we obtain the global and local structural similarity score using the jaccard's similarity score and radial basis function as the edge features. Third, a self-attention graph pooling layer helps to remove the nodes with lower attention scores based on the top-k selection. As the final step, the network employs a two-stream edge-node graph attention network that focuses on finding correlations among the edge and node features, such as landmark coordinates, optical flow, and global and local edge features. A three-frame graph structure is designed to obtain spatiotemporal information. For 3 and 5 expression classes, the results are compared for SMIC and CASME II databases. © 2023 ieee.

关键词： Optical flows

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Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery

Region-based Appearance and Flow Characteristics for Anomaly...

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

作者： Gaus, Yona Falinie A. Bhowmik, Neelanjan Isaac-Medina, Brian K. S. Shum, Hubert P. H. Atapour-Abarghouei, Amir Breckon, Toby P. Durham University Department of Computer Science Durham United Kingdom Durham University Department of Engineering Durham United Kingdom

ISBN: (纸本)9798350302493

Anomaly detection is a classical problem within automated visual surveillance, namely the determination of the normal from the abnormal when operational data availability is highly biased towards one class (normal) due to both insufficient sample size, and inadequate distribution coverage for the other class (abnormal). In this work, we propose the dual use of both visual appearance and localized motion characteristics, derived from optic flow, applied on a per-region basis to facilitate object-wise anomaly detection within this context. Leveraging established object localization techniques from a region proposal network, optic flow is extracted from each object region and combined with appearance in the far infrared (thermal) band to give a 3-channel spatiotemporal tensor representation for each object (1 × thermal - spatial appearance;2 × optic flow magnitude as x and y components - temporal motion). This formulation is used as the basis for training contemporary semi-supervised anomaly detection approaches in a region-based manner such that anomalous objects can be detected as a combination of appearance and/or motion within the scene. Evaluation is performed using the Long-Term infrared (thermal) Imaging (LTD) benchmark dataset against which successful detection of both anomalous object appearance and motion characteristics are demonstrated using a range of semi-supervised anomaly detection approaches. © 2023 ieee.

关键词： Anomaly detection

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

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

ISBN: (纸本)9798350302493

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. © 2023 ieee.

关键词： Cameras

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

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

ISBN: (纸本)9798350302493

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. © 2023 ieee.

关键词： Distillation

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