pwc-archive/papers-with-abstracts huggingface dataset:
Uncovering Bias in Large Vision-Language Models at Scale with Counterfactuals
Authors: Phillip Howard, Kathleen C. Fraser, Anahita Bhiwandiwalla, Svetlana Kiritchenko
Date: 2024-05-30 00:00:00
Tasks: counterfactual, Question Answering, Visual Question Answering

With the advent of Large Language Models (LLMs) possessing increasingly impressive capabilities, a number of Large Vision-Language Models (LVLMs) have been proposed to augment LLMs with visual inputs. Such models condition generated text on both an input image and a text prompt, enabling a variety of use cases such as visual question answering and multimodal chat. While prior studies have examined the social biases contained in text generated by LLMs, this topic has been relatively unexplored in LVLMs. Examining social biases in LVLMs is particularly challenging due to the confounding contributions of bias induced by information contained across the text and visual modalities. To address this challenging problem, we conduct a large-scale study of text generated by different LVLMs under counterfactual changes to input images. Specifically, we present LVLMs with identical open-ended text prompts while conditioning on images from different counterfactual sets, where each set contains images which are largely identical in their depiction of a common subject (e.g., a doctor), but vary only in terms of intersectional social attributes (e.g., race and gender). We comprehensively evaluate the text produced by different models under this counterfactual generation setting at scale, producing over 57 million responses from popular LVLMs. Our multi-dimensional analysis reveals that social attributes such as race, gender, and physical characteristics depicted in input images can significantly influence the generation of toxic content, competency-associated words, harmful stereotypes, and numerical ratings of depicted individuals. We additionally explore the relationship between social bias in LVLMs and their corresponding LLMs, as well as inference-time strategies to mitigate bias.
OpenDAS: Open-Vocabulary Domain Adaptation for 2D and 3D Segmentation
Authors: Gonca Yilmaz, Songyou Peng, Marc Pollefeys, Francis Engelmann, Hermann Blum
Date: 2024-05-30 00:00:00
Tasks: 3D Instance Segmentation, 3D Open-Vocabulary Instance Segmentation, Autonomous Driving, Domain Adaptation, Image Captioning, Instance Segmentation, open vocabulary 3d instance segmentation, Open Vocabulary Semantic Segmentation, Open-Vocabulary Semantic Segmentation, Segmentation, Semantic Segmentation, Triplet

Recently, Vision-Language Models (VLMs) have advanced segmentation techniques by shifting from the traditional segmentation of a closed-set of predefined object classes to open-vocabulary segmentation (OVS), allowing users to segment novel classes and concepts unseen during training of the segmentation model. However, this flexibility comes with a trade-off: fully-supervised closed-set methods still outperform OVS methods on base classes, that is on classes on which they have been explicitly trained. This is due to the lack of pixel-aligned training masks for VLMs (which are trained on image-caption pairs), and the absence of domain-specific knowledge, such as autonomous driving. Therefore, we propose the task of open-vocabulary domain adaptation to infuse domain-specific knowledge into VLMs while preserving their open-vocabulary nature. By doing so, we achieve improved performance in base and novel classes. Existing VLM adaptation methods improve performance on base (training) queries, but fail to fully preserve the open-set capabilities of VLMs on novel queries. To address this shortcoming, we combine parameter-efficient prompt tuning with a triplet-loss-based training strategy that uses auxiliary negative queries. Notably, our approach is the only parameter-efficient method that consistently surpasses the original VLM on novel classes. Our adapted VLMs can seamlessly be integrated into existing OVS pipelines, e.g., improving OVSeg by +6.0% mIoU on ADE20K for open-vocabulary 2D segmentation, and OpenMask3D by +4.1% AP on ScanNet++ Offices for open-vocabulary 3D instance segmentation without other changes. The project page is available at https://open-das.github.io/.
A Multimodal Dangerous State Recognition and Early Warning System for Elderly with Intermittent Dementia
Authors: Liyun Deng, Lei Jin, Guangcheng Wang, Quan Shi, Han Wang
Date: 2024-05-30 00:00:00
Tasks: Cloud Computing

In response to the social issue of the increasing number of elderly vulnerable groups going missing due to the aggravating aging population in China, our team has developed a wearable anti-loss device and intelligent early warning system for elderly individuals with intermittent dementia using artificial intelligence and IoT technology. This system comprises an anti-loss smart helmet, a cloud computing module, and an intelligent early warning application on the caregiver's mobile device. The smart helmet integrates a miniature camera module, a GPS module, and a 5G communication module to collect first-person images and location information of the elderly. Data is transmitted remotely via 5G, FTP, and TCP protocols. In the cloud computing module, our team has proposed for the first time a multimodal dangerous state recognition network based on scene and location information to accurately assess the risk of elderly individuals going missing. Finally, the application software interface designed for the caregiver's mobile device implements multi-level early warnings. The system developed by our team requires no operation or response from the elderly, achieving fully automatic environmental perception, risk assessment, and proactive alarming. This overcomes the limitations of traditional monitoring devices, which require active operation and response, thus avoiding the issue of the digital divide for the elderly. It effectively prevents accidental loss and potential dangers for elderly individuals with dementia.
Federated and Transfer Learning for Cancer Detection Based on Image Analysis
Authors: Amine Bechar, Youssef Elmir, Yassine Himeur, Rafik Medjoudj, Abbes Amira
Date: 2024-05-30 00:00:00
Tasks: Federated Learning, Transfer Learning

This review article discusses the roles of federated learning (FL) and transfer learning (TL) in cancer detection based on image analysis. These two strategies powered by machine learning have drawn a lot of attention due to their potential to increase the precision and effectiveness of cancer diagnosis in light of the growing importance of machine learning techniques in cancer detection. FL enables the training of machine learning models on data distributed across multiple sites without the need for centralized data sharing, while TL allows for the transfer of knowledge from one task to another. A comprehensive assessment of the two methods, including their strengths, and weaknesses is presented. Moving on, their applications in cancer detection are discussed, including potential directions for the future. Finally, this article offers a thorough description of the functions of TL and FL in image-based cancer detection. The authors also make insightful suggestions for additional study in this rapidly developing area.
Infinite 3D Landmarks: Improving Continuous 2D Facial Landmark Detection
Authors: Prashanth Chandran, Gaspard Zoss, Paulo Gotardo, Derek Bradley
Date: 2024-05-30 00:00:00
Tasks: Facial Landmark Detection

In this paper, we examine 3 important issues in the practical use of state-of-the-art facial landmark detectors and show how a combination of specific architectural modifications can directly improve their accuracy and temporal stability. First, many facial landmark detectors require face normalization as a preprocessing step, which is accomplished by a separately-trained neural network that crops and resizes the face in the input image. There is no guarantee that this pre-trained network performs the optimal face normalization for landmark detection. We instead analyze the use of a spatial transformer network that is trained alongside the landmark detector in an unsupervised manner, and jointly learn optimal face normalization and landmark detection. Second, we show that modifying the output head of the landmark predictor to infer landmarks in a canonical 3D space can further improve accuracy. To convert the predicted 3D landmarks into screen-space, we additionally predict the camera intrinsics and head pose from the input image. As a side benefit, this allows to predict the 3D face shape from a given image only using 2D landmarks as supervision, which is useful in determining landmark visibility among other things. Finally, when training a landmark detector on multiple datasets at the same time, annotation inconsistencies across datasets forces the network to produce a suboptimal average. We propose to add a semantic correction network to address this issue. This additional lightweight neural network is trained alongside the landmark detector, without requiring any additional supervision. While the insights of this paper can be applied to most common landmark detectors, we specifically target a recently-proposed continuous 2D landmark detector to demonstrate how each of our additions leads to meaningful improvements over the state-of-the-art on standard benchmarks.
RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection
Authors: Zhiyuan He, Pin-Yu Chen, Tsung-Yi Ho
Date: 2024-05-30 00:00:00
Tasks: Image Generation

The rapid advances in generative AI models have empowered the creation of highly realistic images with arbitrary content, raising concerns about potential misuse and harm, such as Deepfakes. Current research focuses on training detectors using large datasets of generated images. However, these training-based solutions are often computationally expensive and show limited generalization to unseen generated images. In this paper, we propose a training-free method to distinguish between real and AI-generated images. We first observe that real images are more robust to tiny noise perturbations than AI-generated images in the representation space of vision foundation models. Based on this observation, we propose RIGID, a training-free and model-agnostic method for robust AI-generated image detection. RIGID is a simple yet effective approach that identifies whether an image is AI-generated by comparing the representation similarity between the original and the noise-perturbed counterpart. Our evaluation on a diverse set of AI-generated images and benchmarks shows that RIGID significantly outperforms existing trainingbased and training-free detectors. In particular, the average performance of RIGID exceeds the current best training-free method by more than 25%. Importantly, RIGID exhibits strong generalization across different image generation methods and robustness to image corruptions.
FMARS: Annotating Remote Sensing Images for Disaster Management using Foundation Models
Authors: Edoardo Arnaudo, Jacopo Lungo Vaschetti, Lorenzo Innocenti, Luca Barco, Davide Lisi, Vanina Fissore, Claudio Rossi
Date: 2024-05-30 00:00:00
Tasks: Domain Adaptation, Segmentation Of Remote Sensing Imagery, Semantic Segmentation, Unsupervised Domain Adaptation

Very-High Resolution (VHR) remote sensing imagery is increasingly accessible, but often lacks annotations for effective machine learning applications. Recent foundation models like GroundingDINO and Segment Anything (SAM) provide opportunities to automatically generate annotations. This study introduces FMARS (Foundation Model Annotations in Remote Sensing), a methodology leveraging VHR imagery and foundation models for fast and robust annotation. We focus on disaster management and provide a large-scale dataset with labels obtained from pre-event imagery over 19 disaster events, derived from the Maxar Open Data initiative. We train segmentation models on the generated labels, using Unsupervised Domain Adaptation (UDA) techniques to increase transferability to real-world scenarios. Our results demonstrate the effectiveness of leveraging foundation models to automatically annotate remote sensing data at scale, enabling robust downstream models for critical applications. Code and dataset are available at \url{https://github.com/links-ads/igarss-fmars}.
Rapid Wildfire Hotspot Detection Using Self-Supervised Learning on Temporal Remote Sensing Data
Authors: Luca Barco, Angelica Urbanelli, Claudio Rossi
Date: 2024-05-30 00:00:00
Tasks: Self-Supervised Learning, Time Series

Rapid detection and well-timed intervention are essential to mitigate the impacts of wildfires. Leveraging remote sensed data from satellite networks and advanced AI models to automatically detect hotspots (i.e., thermal anomalies caused by active fires) is an effective way to build wildfire monitoring systems. In this work, we propose a novel dataset containing time series of remotely sensed data related to European fire events and a Self-Supervised Learning (SSL)-based model able to analyse multi-temporal data and identify hotspots in potentially near real time. We train and evaluate the performance of our model using our dataset and Thraws, a dataset of thermal anomalies including several fire events, obtaining an F1 score of 63.58.
VAAD: Visual Attention Analysis Dashboard applied to e-Learning
Authors: Miriam Navarro, Álvaro Becerra, Roberto Daza, Ruth Cobos, Aythami Morales, Julian Fierrez
Date: 2024-05-30 00:00:00
Tasks: Descriptive

In this paper, we present an approach in the Multimodal Learning Analytics field. Within this approach, we have developed a tool to visualize and analyze eye movement data collected during learning sessions in online courses. The tool is named VAAD, an acronym for Visual Attention Analysis Dashboard. These eye movement data have been gathered using an eye-tracker and subsequently processed and visualized for interpretation. The purpose of the tool is to conduct a descriptive analysis of the data by facilitating its visualization, enabling the identification of differences and learning patterns among various learner populations. Additionally, it integrates a predictive module capable of anticipating learner activities during a learning session. Consequently, VAAD holds the potential to offer valuable insights into online learning behaviors from both descriptive and predictive perspectives.
Typography Leads Semantic Diversifying: Amplifying Adversarial Transferability across Multimodal Large Language Models
Authors: Hao Cheng, Erjia Xiao, Jiayan Yang, Jiahang Cao, Qiang Zhang, Le Yang, Jize Zhang, Kaidi Xu, Jindong Gu, Renjing Xu
Date: 2024-05-30 00:00:00
Tasks: Diversity

Recently, Multimodal Large Language Models (MLLMs) achieve remarkable performance in numerous zero-shot tasks due to their outstanding cross-modal interaction and comprehension abilities. However, MLLMs are found to still be vulnerable to human-imperceptible adversarial examples. In the exploration of security vulnerabilities in real-world scenarios, transferability, which can achieve cross-model impact, is considered the greatest threat posed by adversarial examples. However, there is currently no systematic research on the threat of cross-MLLMs adversarial transferability. Therefore, this paper as the first step to provide a comprehensive evaluation of the transferability of adversarial examples generated by various MLLMs. Furthermore, leveraging two key factors that influence transferability performance: 1) The strength of information diversity involved in the adversarial generation process; 2) Editing across vision-language modality information. We propose a boosting method called Typography Augment Transferability Method (TATM) to investigate the adversarial transferability performance across MLLMs further. Through extensive experimental validation, our TATM demonstrates exceptional performance in real-world applications of "Harmful Word Insertion" and "Important Information Protection".
Estimating Human Poses Across Datasets: A Unified Skeleton and Multi-Teacher Distillation Approach
Authors: Muhammad Saif Ullah Khan, Dhavalkumar Limbachiya, Didier Stricker, Muhammad Zeshan Afzal
Date: 2024-05-30 00:00:00
Tasks: Activity Recognition, Knowledge Distillation, Pose Estimation

Human pose estimation is a key task in computer vision with various applications such as activity recognition and interactive systems. However, the lack of consistency in the annotated skeletons across different datasets poses challenges in developing universally applicable models. To address this challenge, we propose a novel approach integrating multi-teacher knowledge distillation with a unified skeleton representation. Our networks are jointly trained on the COCO and MPII datasets, containing 17 and 16 keypoints, respectively. We demonstrate enhanced adaptability by predicting an extended set of 21 keypoints, 4 (COCO) and 5 (MPII) more than original annotations, improving cross-dataset generalization. Our joint models achieved an average accuracy of 70.89 and 76.40, compared to 53.79 and 55.78 when trained on a single dataset and evaluated on both. Moreover, we also evaluate all 21 predicted points by our two models by reporting an AP of 66.84 and 72.75 on the Halpe dataset. This highlights the potential of our technique to address one of the most pressing challenges in pose estimation research and application - the inconsistency in skeletal annotations.
Faces of the Mind: Unveiling Mental Health States Through Facial Expressions in 11,427 Adolescents
Authors: Xiao Xu, Keyin Zhou, Yan Zhang, Yang Wang, Fei Wang, Xizhe Zhang
Date: 2024-05-30 00:00:00

Mood disorders, including depression and anxiety, often manifest through facial expressions. While previous research has explored the connection between facial features and emotions, machine learning algorithms for estimating mood disorder severity have been hindered by small datasets and limited real-world application. To address this gap, we analyzed facial videos of 11,427 participants, a dataset two orders of magnitude larger than previous studies. This comprehensive collection includes standardized facial expression videos from reading tasks, along with a detailed psychological scale that measures depression, anxiety, and stress. By examining the relationships among these emotional states and employing clustering analysis, we identified distinct subgroups embodying different emotional profiles. We then trained tree-based classifiers and deep learning models to estimate emotional states from facial features. Results indicate that models previously effective on small datasets experienced decreased performance when applied to our large dataset, highlighting the importance of data scale and mitigating overfitting in practical settings. Notably, our study identified subtle shifts in pupil dynamics and gaze orientation as potential markers of mood disorders, providing valuable information on the interaction between facial expressions and mental health. This research marks the first large-scale and comprehensive investigation of facial expressions in the context of mental health, laying the groundwork for future data-driven advancements in this field.
N-Dimensional Gaussians for Fitting of High Dimensional Functions
Authors: Stavros Diolatzis, Tobias Zirr, Alexandr Kuznetsov, Georgios Kopanas, Anton Kaplanyan
Date: 2024-05-30 00:00:00

In the wake of many new ML-inspired approaches for reconstructing and representing high-quality 3D content, recent hybrid and explicitly learned representations exhibit promising performance and quality characteristics. However, their scaling to higher dimensions is challenging, e.g. when accounting for dynamic content with respect to additional parameters such as material properties, illumination, or time. In this paper, we tackle these challenges for an explicit representations based on Gaussian mixture models. With our solutions, we arrive at efficient fitting of compact N-dimensional Gaussian mixtures and enable efficient evaluation at render time: For fast fitting and evaluation, we introduce a high-dimensional culling scheme that efficiently bounds N-D Gaussians, inspired by Locality Sensitive Hashing. For adaptive refinement yet compact representation, we introduce a loss-adaptive density control scheme that incrementally guides the use of additional capacity towards missing details. With these tools we can for the first time represent complex appearance that depends on many input dimensions beyond position or viewing angle within a compact, explicit representation optimized in minutes and rendered in milliseconds.
Can the accuracy bias by facial hairstyle be reduced through balancing the training data?
Authors: Kagan Ozturk, Haiyu Wu, Kevin W. Bowyer
Date: 2024-05-30 00:00:00
Tasks: Data Augmentation, Face Recognition

Appearance of a face can be greatly altered by growing a beard and mustache. The facial hairstyles in a pair of images can cause marked changes to the impostor distribution and the genuine distribution. Also, different distributions of facial hairstyle across demographics could cause a false impression of relative accuracy across demographics. We first show that, even though larger training sets boost the recognition accuracy on all facial hairstyles, accuracy variations caused by facial hairstyles persist regardless of the size of the training set. Then, we analyze the impact of having different fractions of the training data represent facial hairstyles. We created balanced training sets using a set of identities available in Webface42M that both have clean-shaven and facial hair images. We find that, even when a face recognition model is trained with a balanced clean-shaven / facial hair training set, accuracy variation on the test data does not diminish. Next, data augmentation is employed to further investigate the effect of facial hair distribution in training data by manipulating facial hair pixels with the help of facial landmark points and a facial hair segmentation model. Our results show facial hair causes an accuracy gap between clean-shaven and facial hair images, and this impact can be significantly different between African-Americans and Caucasians.
Enhancing Plant Disease Detection: A Novel CNN-Based Approach with Tensor Subspace Learning and HOWSVD-MD
Authors: Abdelmalik Ouamane, Ammar Chouchane, Yassine Himeur, Abderrazak Debilou, Abbes Amira, Shadi Atalla, Wathiq Mansoor, Hussain Al Ahmad
Date: 2024-05-30 00:00:00
Tasks: Management

Machine learning has revolutionized the field of agricultural science, particularly in the early detection and management of plant diseases, which are crucial for maintaining crop health and productivity. Leveraging advanced algorithms and imaging technologies, researchers are now able to identify and classify plant diseases with unprecedented accuracy and speed. Effective management of tomato diseases is crucial for enhancing agricultural productivity. The development and application of tomato disease classification methods are central to this objective. This paper introduces a cutting-edge technique for the detection and classification of tomato leaf diseases, utilizing insights from the latest pre-trained Convolutional Neural Network (CNN) models. We propose a sophisticated approach within the domain of tensor subspace learning, known as Higher-Order Whitened Singular Value Decomposition (HOWSVD), designed to boost the discriminatory power of the system. Our approach to Tensor Subspace Learning is methodically executed in two phases, beginning with HOWSVD and culminating in Multilinear Discriminant Analysis (MDA). The efficacy of this innovative method was rigorously tested through comprehensive experiments on two distinct datasets, namely PlantVillage and the Taiwan dataset. The findings reveal that HOWSVD-MDA outperforms existing methods, underscoring its capability to markedly enhance the precision and dependability of diagnosing tomato leaf diseases. For instance, up to 98.36\% and 89.39\% accuracy scores have been achieved under PlantVillage and the Taiwan datasets, respectively.