pwc-archive/papers-with-abstracts huggingface dataset:
Disrupting Style Mimicry Attacks on Video Imagery
Authors: Josephine Passananti, Stanley Wu, Shawn Shan, Haitao Zheng, Ben Y. Zhao
Date: 2024-05-11 00:00:00

Generative AI models are often used to perform mimicry attacks, where a pretrained model is fine-tuned on a small sample of images to learn to mimic a specific artist of interest. While researchers have introduced multiple anti-mimicry protection tools (Mist, Glaze, Anti-Dreambooth), recent evidence points to a growing trend of mimicry models using videos as sources of training data. This paper presents our experiences exploring techniques to disrupt style mimicry on video imagery. We first validate that mimicry attacks can succeed by training on individual frames extracted from videos. We show that while anti-mimicry tools can offer protection when applied to individual frames, this approach is vulnerable to an adaptive countermeasure that removes protection by exploiting randomness in optimization results of consecutive (nearly-identical) frames. We develop a new, tool-agnostic framework that segments videos into short scenes based on frame-level similarity, and use a per-scene optimization baseline to remove inter-frame randomization while reducing computational cost. We show via both image level metrics and an end-to-end user study that the resulting protection restores protection against mimicry (including the countermeasure). Finally, we develop another adaptive countermeasure and find that it falls short against our framework.
Linear Explanations for Individual Neurons
Authors: Tuomas Oikarinen, Tsui-Wei Weng
Date: 2024-05-10 00:00:00

In recent years many methods have been developed to understand the internal workings of neural networks, often by describing the function of individual neurons in the model. However, these methods typically only focus on explaining the very highest activations of a neuron. In this paper we show this is not sufficient, and that the highest activation range is only responsible for a very small percentage of the neuron's causal effect. In addition, inputs causing lower activations are often very different and can't be reliably predicted by only looking at high activations. We propose that neurons should instead be understood as a linear combination of concepts, and develop an efficient method for producing these linear explanations. In addition, we show how to automatically evaluate description quality using simulation, i.e. predicting neuron activations on unseen inputs in vision setting.
CasCalib: Cascaded Calibration for Motion Capture from Sparse Unsynchronized Cameras
Authors: James Tang, Shashwat Suri, Daniel Ajisafe, Bastian Wandt, Helge Rhodin
Date: 2024-05-10 00:00:00
Tasks: Camera Calibration

It is now possible to estimate 3D human pose from monocular images with off-the-shelf 3D pose estimators. However, many practical applications require fine-grained absolute pose information for which multi-view cues and camera calibration are necessary. Such multi-view recordings are laborious because they require manual calibration, and are expensive when using dedicated hardware. Our goal is full automation, which includes temporal synchronization, as well as intrinsic and extrinsic camera calibration. This is done by using persons in the scene as the calibration objects. Existing methods either address only synchronization or calibration, assume one of the former as input, or have significant limitations. A common limitation is that they only consider single persons, which eases correspondence finding. We attain this generality by partitioning the high-dimensional time and calibration space into a cascade of subspaces and introduce tailored algorithms to optimize each efficiently and robustly. The outcome is an easy-to-use, flexible, and robust motion capture toolbox that we release to enable scientific applications, which we demonstrate on diverse multi-view benchmarks. Project website: https://github.com/jamestang1998/CasCalib.
Bridging the Gap: Protocol Towards Fair and Consistent Affect Analysis
Authors: Guanyu Hu, Eleni Papadopoulou, Dimitrios Kollias, Paraskevi Tzouveli, Jie Wei, Xinyu Yang
Date: 2024-05-10 00:00:00
Tasks: Decision Making, Fairness

The increasing integration of machine learning algorithms in daily life underscores the critical need for fairness and equity in their deployment. As these technologies play a pivotal role in decision-making, addressing biases across diverse subpopulation groups, including age, gender, and race, becomes paramount. Automatic affect analysis, at the intersection of physiology, psychology, and machine learning, has seen significant development. However, existing databases and methodologies lack uniformity, leading to biased evaluations. This work addresses these issues by analyzing six affective databases, annotating demographic attributes, and proposing a common protocol for database partitioning. Emphasis is placed on fairness in evaluations. Extensive experiments with baseline and state-of-the-art methods demonstrate the impact of these changes, revealing the inadequacy of prior assessments. The findings underscore the importance of considering demographic attributes in affect analysis research and provide a foundation for more equitable methodologies. Our annotations, code and pre-trained models are available at: https://github.com/dkollias/Fair-Consistent-Affect-Analysis
G-FARS: Gradient-Field-based Auto-Regressive Sampling for 3D Part Grouping
Authors: Junfeng Cheng, Tania Stathaki
Date: 2024-05-10 00:00:00
Proceeding: CVPR 2024 1
Tasks: Graph Neural Network

This paper proposes a novel task named "3D part grouping". Suppose there is a mixed set containing scattered parts from various shapes. This task requires algorithms to find out every possible combination among all the parts. To address this challenge, we propose the so called Gradient Field-based Auto-Regressive Sampling framework (G-FARS) tailored specifically for the 3D part grouping task. In our framework, we design a gradient-field-based selection graph neural network (GNN) to learn the gradients of a log conditional probability density in terms of part selection, where the condition is the given mixed part set. This innovative approach, implemented through the gradient-field-based selection GNN, effectively captures complex relationships among all the parts in the input. Upon completion of the training process, our framework becomes capable of autonomously grouping 3D parts by iteratively selecting them from the mixed part set, leveraging the knowledge acquired by the trained gradient-field-based selection GNN. Our code is available at: https://github.com/J-F-Cheng/G-FARS-3DPartGrouping.
Synchronized Object Detection for Autonomous Sorting, Mapping, and Quantification of Materials in Circular Healthcare
Authors: Federico Zocco, Daniel R. Lake, Seán McLoone, Shahin Rahimifard
Date: 2024-05-10 00:00:00
Tasks: object-detection, Object Detection

The circular economy paradigm is gaining interest as a solution to reducing both material supply uncertainties and waste generation. One of the main challenges in realizing this paradigm is monitoring materials, since in general, something that is not measured cannot be effectively managed. In this paper, we propose a real-time synchronized object detection framework that enables, at the same time, autonomous sorting, mapping, and quantification of solid materials. We begin by introducing the general framework for real-time wide-area material monitoring, and then, we illustrate it using a numerical example. Finally, we develop a first prototype whose working principle is underpinned by the proposed framework. The prototype detects 4 materials from 5 different models of inhalers and, through a synchronization mechanism, it combines the detection outputs of 2 vision units running at 12-22 frames per second (Fig. 1). This led us to introduce the notion of synchromaterial and to conceive a robotic waste sorter as a node compartment of a material network. Dataset, code, and demo videos are publicly available.
Dual-Task Vision Transformer for Rapid and Accurate Intracerebral Hemorrhage CT Image Classification
Authors: Jialiang Fan, Xinhui Fan, Chengyan Song, Xiaofan Wang, Bingdong Feng, Lucan Li, Guoyu Lu
Date: 2024-05-10 00:00:00
Tasks: Classification, image-classification, Image Classification

Intracerebral hemorrhage (ICH) is a severe and sudden medical condition caused by the rupture of blood vessels in the brain, leading to permanent damage to brain tissue and often resulting in functional disabilities or death in patients. Diagnosis and analysis of ICH typically rely on brain CT imaging. Given the urgency of ICH conditions, early treatment is crucial, necessitating rapid analysis of CT images to formulate tailored treatment plans. However, the complexity of ICH CT images and the frequent scarcity of specialist radiologists pose significant challenges. Therefore, we collect a dataset from the real world for ICH and normal classification and three types of ICH image classification based on the hemorrhage location, i.e., Deep, Subcortical, and Lobar. In addition, we propose a neural network structure, dual-task vision transformer (DTViT), for the automated classification and diagnosis of ICH images. The DTViT deploys the encoder from the Vision Transformer (ViT), employing attention mechanisms for feature extraction from CT images. The proposed DTViT framework also incorporates two multilayer perception (MLP)-based decoders to simultaneously identify the presence of ICH and classify the three types of hemorrhage locations. Experimental results demonstrate that DTViT performs well on the real-world test dataset. The code and newly collected dataset for this work are available at: https://github.com/jfan1997/DTViT.
Self-Consistent Recursive Diffusion Bridge for Medical Image Translation
Authors: Fuat Arslan, Bilal Kabas, Onat Dalmaz, Muzaffer Ozbey, Tolga Çukur
Date: 2024-05-10 00:00:00
Tasks: Denoising, Scheduling, Translation

Denoising diffusion models (DDM) have gained recent traction in medical image translation given improved training stability over adversarial models. DDMs learn a multi-step denoising transformation to progressively map random Gaussian-noise images onto target-modality images, while receiving stationary guidance from source-modality images. As this denoising transformation diverges significantly from the task-relevant source-to-target transformation, DDMs can suffer from weak source-modality guidance. Here, we propose a novel self-consistent recursive diffusion bridge (SelfRDB) for improved performance in medical image translation. Unlike DDMs, SelfRDB employs a novel forward process with start- and end-points defined based on target and source images, respectively. Intermediate image samples across the process are expressed via a normal distribution with mean taken as a convex combination of start-end points, and variance from additive noise. Unlike regular diffusion bridges that prescribe zero variance at start-end points and high variance at mid-point of the process, we propose a novel noise scheduling with monotonically increasing variance towards the end-point in order to boost generalization performance and facilitate information transfer between the two modalities. To further enhance sampling accuracy in each reverse step, we propose a novel sampling procedure where the network recursively generates a transient-estimate of the target image until convergence onto a self-consistent solution. Comprehensive analyses in multi-contrast MRI and MRI-CT translation indicate that SelfRDB offers superior performance against competing methods.
SAM3D: Zero-Shot Semi-Automatic Segmentation in 3D Medical Images with the Segment Anything Model
Authors: Trevor J. Chan, Aarush Sahni, Yijin Fang, Jie Li, Alisha Luthra, Alison Pouch, Chamith S. Rajapakse
Date: 2024-05-10 00:00:00
Tasks: Diagnostic, Zero Shot Segmentation

We introduce SAM3D, a new approach to semi-automatic zero-shot segmentation of 3D images building on the existing Segment Anything Model. We achieve fast and accurate segmentations in 3D images with a four-step strategy involving: user prompting with 3D polylines, volume slicing along multiple axes, slice-wide inference with a pretrained model, and recomposition and refinement in 3D. We evaluated SAM3D performance qualitatively on an array of imaging modalities and anatomical structures and quantify performance for specific structures in abdominal pelvic CT and brain MRI. Notably, our method achieves good performance with zero model training or finetuning, making it particularly useful for tasks with a scarcity of preexisting labeled data. By enabling users to create 3D segmentations of unseen data quickly and with dramatically reduced manual input, these methods have the potential to aid surgical planning and education, diagnostic imaging, and scientific research.
GraphRelate3D: Context-Dependent 3D Object Detection with Inter-Object Relationship Graphs
Authors: MingYu Liu, Ekim Yurtsever, Marc Brede, Jun Meng, Walter Zimmer, Xingcheng Zhou, Bare Luka Zagar, Yuning Cui, Alois Knoll
Date: 2024-05-10 00:00:00
Tasks: 3D Object Detection, Autonomous Vehicles, Graph Neural Network, Object, object-detection, Object Detection

Accurate and effective 3D object detection is critical for ensuring the driving safety of autonomous vehicles. Recently, state-of-the-art two-stage 3D object detectors have exhibited promising performance. However, these methods refine proposals individually, ignoring the rich contextual information in the object relationships between the neighbor proposals. In this study, we introduce an object relation module, consisting of a graph generator and a graph neural network (GNN), to learn the spatial information from certain patterns to improve 3D object detection. Specifically, we create an inter-object relationship graph based on proposals in a frame via the graph generator to connect each proposal with its neighbor proposals. Afterward, the GNN module extracts edge features from the generated graph and iteratively refines proposal features with the captured edge features. Ultimately, we leverage the refined features as input to the detection head to obtain detection results. Our approach improves upon the baseline PV-RCNN on the KITTI validation set for the car class across easy, moderate, and hard difficulty levels by 0.82%, 0.74%, and 0.58%, respectively. Additionally, our method outperforms the baseline by more than 1% under the moderate and hard levels BEV AP on the test server.
Shape Conditioned Human Motion Generation with Diffusion Model
Authors: Kebing Xue, Hyewon Seo
Date: 2024-05-10 00:00:00
Tasks: model, Motion Generation, Motion Synthesis

Human motion synthesis is an important task in computer graphics and computer vision. While focusing on various conditioning signals such as text, action class, or audio to guide the generation process, most existing methods utilize skeleton-based pose representation, requiring additional skinning to produce renderable meshes. Given that human motion is a complex interplay of bones, joints, and muscles, considering solely the skeleton for generation may neglect their inherent interdependency, which can limit the variability and precision of the generated results. To address this issue, we propose a Shape-conditioned Motion Diffusion model (SMD), which enables the generation of motion sequences directly in mesh format, conditioned on a specified target mesh. In SMD, the input meshes are transformed into spectral coefficients using graph Laplacian, to efficiently represent meshes. Subsequently, we propose a Spectral-Temporal Autoencoder (STAE) to leverage cross-temporal dependencies within the spectral domain. Extensive experimental evaluations show that SMD not only produces vivid and realistic motions but also achieves competitive performance in text-to-motion and action-to-motion tasks when compared to state-of-the-art methods.
Common Corruptions for Enhancing and Evaluating Robustness in Air-to-Air Visual Object Detection
Authors: Anastasios Arsenos, Vasileios Karampinis, Evangelos Petrongonas, Christos Skliros, Dimitrios Kollias, Stefanos Kollias, Athanasios Voulodimos
Date: 2024-05-10 00:00:00
Tasks: Object, object-detection, Object Detection, Object Tracking

The main barrier to achieving fully autonomous flights lies in autonomous aircraft navigation. Managing non-cooperative traffic presents the most important challenge in this problem. The most efficient strategy for handling non-cooperative traffic is based on monocular video processing through deep learning models. This study contributes to the vision-based deep learning aircraft detection and tracking literature by investigating the impact of data corruption arising from environmental and hardware conditions on the effectiveness of these methods. More specifically, we designed $7$ types of common corruptions for camera inputs taking into account real-world flight conditions. By applying these corruptions to the Airborne Object Tracking (AOT) dataset we constructed the first robustness benchmark dataset named AOT-C for air-to-air aerial object detection. The corruptions included in this dataset cover a wide range of challenging conditions such as adverse weather and sensor noise. The second main contribution of this letter is to present an extensive experimental evaluation involving $8$ diverse object detectors to explore the degradation in the performance under escalating levels of corruptions (domain shifts). Based on the evaluation results, the key observations that emerge are the following: 1) One-stage detectors of the YOLO family demonstrate better robustness, 2) Transformer-based and multi-stage detectors like Faster R-CNN are extremely vulnerable to corruptions, 3) Robustness against corruptions is related to the generalization ability of models. The third main contribution is to present that finetuning on our augmented synthetic data results in improvements in the generalisation ability of the object detector in real-world flight experiments.
Ensuring UAV Safety: A Vision-only and Real-time Framework for Collision Avoidance Through Object Detection, Tracking, and Distance Estimation
Authors: Vasileios Karampinis, Anastasios Arsenos, Orfeas Filippopoulos, Evangelos Petrongonas, Christos Skliros, Dimitrios Kollias, Stefanos Kollias, Athanasios Voulodimos
Date: 2024-05-10 00:00:00
Tasks: Collision Avoidance, Depth Estimation, Image-to-Image Translation, Navigate, Object, object-detection, Object Detection, Object Tracking

In the last twenty years, unmanned aerial vehicles (UAVs) have garnered growing interest due to their expanding applications in both military and civilian domains. Detecting non-cooperative aerial vehicles with efficiency and estimating collisions accurately are pivotal for achieving fully autonomous aircraft and facilitating Advanced Air Mobility (AAM). This paper presents a deep-learning framework that utilizes optical sensors for the detection, tracking, and distance estimation of non-cooperative aerial vehicles. In implementing this comprehensive sensing framework, the availability of depth information is essential for enabling autonomous aerial vehicles to perceive and navigate around obstacles. In this work, we propose a method for estimating the distance information of a detected aerial object in real time using only the input of a monocular camera. In order to train our deep learning components for the object detection, tracking and depth estimation tasks we utilize the Amazon Airborne Object Tracking (AOT) Dataset. In contrast to previous approaches that integrate the depth estimation module into the object detector, our method formulates the problem as image-to-image translation. We employ a separate lightweight encoder-decoder network for efficient and robust depth estimation. In a nutshell, the object detection module identifies and localizes obstacles, conveying this information to both the tracking module for monitoring obstacle movement and the depth estimation module for calculating distances. Our approach is evaluated on the Airborne Object Tracking (AOT) dataset which is the largest (to the best of our knowledge) air-to-air airborne object dataset.
Malayalam Sign Language Identification using Finetuned YOLOv8 and Computer Vision Techniques
Authors: Abhinand K., Abhiram B. Nair, Dhananjay C., Hanan Hamza, Mohammed Fawaz J., Rahma Fahim K., Anoop V. S
Date: 2024-05-08 00:00:00
Tasks: Language Identification

Technological advancements and innovations are advancing our daily life in all the ways possible but there is a larger section of society who are deprived of accessing the benefits due to their physical inabilities. To reap the real benefits and make it accessible to society, these talented and gifted people should also use such innovations without any hurdles. Many applications developed these days address these challenges, but localized communities and other constrained linguistic groups may find it difficult to use them. Malayalam, a Dravidian language spoken in the Indian state of Kerala is one of the twenty-two scheduled languages in India. Recent years have witnessed a surge in the development of systems and tools in Malayalam, addressing the needs of Kerala, but many of them are not empathetically designed to cater to the needs of hearing-impaired people. One of the major challenges is the limited or no availability of sign language data for the Malayalam language and sufficient efforts are not made in this direction. In this connection, this paper proposes an approach for sign language identification for the Malayalam language using advanced deep learning and computer vision techniques. We start by developing a labeled dataset for Malayalam letters and for the identification we use advanced deep learning techniques such as YOLOv8 and computer vision. Experimental results show that the identification accuracy is comparable to other sign language identification systems and other researchers in sign language identification can use the model as a baseline to develop advanced models.
A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds
Authors: Anton Orlichenko, Gang Qu, Ziyu Zhou, Anqi Liu, Hong-Wen Deng, Zhengming Ding, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, Yu-Ping Wang
Date: 2024-05-13 00:00:00
Tasks: Functional Connectivity

Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods: We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevelopmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results: We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion: Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance: Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.