pwc-archive/papers-with-abstracts huggingface dataset:
MTabVQA: Evaluating Multi-Tabular Reasoning of Language Models in Visual Space
Authors: Anshul Singh, Chris Biemann, Jan Strich
Date: 2025-06-13 00:00:00
Tasks: Question Answering, Visual Question Answering

Vision-Language Models (VLMs) have demonstrated remarkable capabilities in interpreting visual layouts and text. However, a significant challenge remains in their ability to interpret robustly and reason over multi-tabular data presented as images, a common occurrence in real-world scenarios like web pages and digital documents. Existing benchmarks typically address single tables or non-visual data (text/structured). This leaves a critical gap: they don't assess the ability to parse diverse table images, correlate information across them, and perform multi-hop reasoning on the combined visual data. We introduce MTabVQA, a novel benchmark specifically designed for multi-tabular visual question answering to bridge that gap. MTabVQA comprises 3,745 complex question-answer pairs that necessitate multi-hop reasoning across several visually rendered table images. We provide extensive benchmark results for state-of-the-art VLMs on MTabVQA, revealing significant performance limitations. We further investigate post-training techniques to enhance these reasoning abilities and release MTabVQA-Instruct, a large-scale instruction-tuning dataset. Our experiments show that fine-tuning VLMs with MTabVQA-Instruct substantially improves their performance on visual multi-tabular reasoning. Code and dataset (https://huggingface.co/datasets/mtabvqa/MTabVQA-Eval) are available online (https://anonymous.4open.science/r/MTabVQA-EMNLP-B16E).
FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes
Authors: Wasim Ahmad, Yan-Tsung Peng, Yuan-Hao Chang
Date: 2025-06-13 00:00:00
Tasks: DeepFake Detection, Face Swapping

The widespread emergence of face-swap Deepfake videos poses growing risks to digital security, privacy, and media integrity, necessitating effective forensic tools for identifying the source of such manipulations. Although most prior research has focused primarily on binary Deepfake detection, the task of model attribution -- determining which generative model produced a given Deepfake -- remains underexplored. In this paper, we introduce FAME (Fake Attribution via Multilevel Embeddings), a lightweight and efficient spatio-temporal framework designed to capture subtle generative artifacts specific to different face-swap models. FAME integrates spatial and temporal attention mechanisms to improve attribution accuracy while remaining computationally efficient. We evaluate our model on three challenging and diverse datasets: Deepfake Detection and Manipulation (DFDM), FaceForensics++, and FakeAVCeleb. Results show that FAME consistently outperforms existing methods in both accuracy and runtime, highlighting its potential for deployment in real-world forensic and information security applications.
15,500 Seconds: Lean UAV Classification Leveraging PEFT and Pre-Trained Networks
Authors: Andrew P. Berg, Qian Zhang, Mia Y. Wang
Date: 2025-05-21 00:00:00
Tasks: Audio Classification, Data Augmentation, parameter-efficient fine-tuning

Unmanned Aerial Vehicles (UAVs) pose an escalating security concerns as the market for consumer and military UAVs grows. This paper address the critical data scarcity challenges in deep UAV audio classification. We build upon our previous work expanding novel approaches such as: parameter efficient fine-tuning, data augmentation, and pre-trained networks. We achieve performance upwards of 95\% validation accuracy with EfficientNet-B0.
A Framework for Privacy-Preserving in IoV Using Federated Learning With Differential Privacy
Authors: MUHAMMADADNAN, Semeen Rehman, MADIHAHAIDER SYED, ADEEL ANJUM
Date: 2024-12-09 00:00:00
Proceeding: IEEE Access 2024 12
Tasks: Federated Learning, Privacy Preserving

Vehiclesbecomemoreadvancedandsmarterduetoadvancementsintechnologyinthemodern
 world. Every person now a days, demand a smart vehicle due to their automobility and smart controls. This
 is all possible through advancements in VANET (Vehicular Adhoc Network) and the Internet of Vehicles
 (IoV). Vehicles in the VANET are highly connected to each other and this thing can cause security, safety,
 and privacy risks for the asset itself and driver also. It can become a reason of major threat. And these
 threats can occur due to tracing the location of the vehicle. Existing techniques like group-based shadowing
 schemes, obfuscation, silent periods, and mix-zone have preserved privacy of location somehow, but don’t
 have a good QoS and optimized efficient security. To overcome these issues, we introduced a new privacy
 framework, which is an improvement of the existing shadowing scheme. We proposed a computationally
 efficient group leader selection process based on centeredness, rule obeyed, and OBU resources, reducing
 overhead by 20%, integrating FL with DP to preserve data privacy without sacrificing utility, and achieving
 a 15%improvement in location accuracy under privacy constraints, validating the scalability and robustness
 of the framework through extensive simulations involving up to 300 vehicles. Group Leader is used as an
 optimization of the overall framework including efficiency and implementation of the scheme. This scheme
 increases privacy if the number of vehicles also increases, and this thing makes our scheme more scalable.
 This scheme overcomes the many drawbacks of existing techniques like a higher tracing ratio in shadowing
 schemes, totally depending on the group leader, and reduced utility of all schemes based on distances. The
 most important thing, the single point of failure in the group leader base shadowing scheme is overcome by
 using local federated learning with differential privacy. Validation results of our proposed scheme showed
 that it outperformed the current schemes mainly based on group leader.
Simple Radiology VLLM Test-time Scaling with Thought Graph Traversal
Authors: Yue Yao, Zelin Wen, Yan Tong, Xinyu Tian, Xuqing Li, Xiao Ma, Dongliang Xu, Tom Gedeon
Date: 2025-06-13 00:00:00

Test-time scaling offers a promising way to improve the reasoning performance of vision-language large models (VLLMs) without additional training. In this paper, we explore a simple but effective approach for applying test-time scaling to radiology report generation. Specifically, we introduce a lightweight Thought Graph Traversal (TGT) framework that guides the model to reason through organ-specific findings in a medically coherent order. This framework integrates structured medical priors into the prompt, enabling deeper and more logical analysis with no changes to the underlying model. To further enhance reasoning depth, we apply a reasoning budget forcing strategy that adjusts the model's inference depth at test time by dynamically extending its generation process. This simple yet powerful combination allows a frozen radiology VLLM to self-correct and generate more accurate, consistent chest X-ray reports. Our method outperforms baseline prompting approaches on standard benchmarks, and also reveals dataset biases through traceable reasoning paths. Code and prompts are open-sourced for reproducibility at https://github.com/glerium/Thought-Graph-Traversal.
Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task
Authors: Wuzhenghong Wen, Su Pan, Yuwei Sun
Date: 2025-06-13 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Text to SQL, Text-To-SQL

Schema linking is a critical step in Text-to-SQL task, aiming to accurately predict the table names and column names required for the SQL query based on the given question. However, current fine-tuning approaches for schema linking models employ a rote-learning paradigm, excessively optimizing for ground truth schema linking outcomes while compromising reasoning ability. This limitation arises because of the difficulty in acquiring a high-quality reasoning sample for downstream tasks. To address this, we propose Schema-R1, a reasoning schema linking model trained using reinforcement learning. Specifically, Schema-R1 consists of three key steps: constructing small batches of high-quality reasoning samples, supervised fine-tuning for cold-start initialization, and rule-based reinforcement learning training. The final results demonstrate that our method effectively enhances the reasoning ability of the schema linking model, achieving a 10\% improvement in filter accuracy compared to the existing method. Our code is available at https://github.com/hongWin/Schema-R1/.
Noisemaker 3D: Comprehensive Framework for Mesh Noise Generation
Authors: Vladimir Mashurov, Vasilii Latonov, Anastasia Martynova, Natalia Semenova
Date: 2025-05-16 00:00:00
Proceeding: 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2025) 2025 5
Tasks: 3D Mesh Denoising, 3D Noise Generation, Dataset Generation, Denoising

In this article, we present a comprehensive library for generating node and topological noise in meshes. The library provides a versatile tool for creating corrupted mesh datasets, which are essential for learning-based denoising algorithms. Our main contributions include cluster and patch noise generation techniques for mesh topology corruption. Cluster generation supports two modes: separated and merged clusters. We also compare the node noise generated by the library to real noise from a scanned object dataset. Finally, we create a noisy object dataset using the library and test it with filter-based and machine learning-based denoising methods.
Testing Prompt Engineering Methods for Knowledge Extraction from Text
Authors: Fina Polat, Ilaria Tiddi, Paul Groth
Date: 2025-02-18 00:00:00
Proceeding: Semantic Web 2025 2
Tasks: Open Information Extraction, Prompt Engineering, Relation Extraction, Retrieval

The capabilities of Large Language Models (LLMs,) such as Mistral 7B, Llama 3, GPT-4, present a significant opportunity for knowledge extraction (KE) from text. However, LLMs’ context-sensitivity can hinder obtaining precise and task-aligned outcomes, thereby requiring prompt engineering. This study explores the efficacy of five prompt methods with different task demonstration strategies across 17 different prompt templates, utilizing a relation extraction dataset (RED-FM) with the aforementioned LLMs. To facilitate evaluation, we introduce a novel framework grounded in Wikidata’s ontology. The findings demonstrate that LLMs are capable of extracting a diverse array of facts from text. Notably, incorporating a simple instruction accompanied by a task demonstration – comprising three examples selected via a retrieval mechanism – significantly enhances performance across Mistral 7B, Llama 3, and GPT-4. The effectiveness of reasoning-oriented prompting methods such as Chain-of-Thought, Reasoning and Acting, while improved with task demonstrations, does not surpass alternative methods. This suggests that framing extraction as a reasoning task may not be necessary for KE. Notably, task demonstrations leveraging examples selected via retrieval mechanisms facilitate effective knowledge extraction across all tested prompting strategies and LLMs.
SLRNet: A Real-Time LSTM-Based Sign Language Recognition System
Authors: Sharvari Kamble
Date: 2025-06-11 00:00:00
Tasks: Gesture Recognition, Sign Language Recognition

Sign Language Recognition (SLR) plays a crucial role in bridging the communication gap between the hearing-impaired community and society. This paper introduces SLRNet, a real-time webcam-based ASL recognition system using MediaPipe Holistic and Long Short-Term Memory (LSTM) networks. The model processes video streams to recognize both ASL alphabet letters and functional words. With a validation accuracy of 86.7%, SLRNet demonstrates the feasibility of inclusive, hardware-independent gesture recognition.
Segment This Thing: Foveated Tokenization for Efficient Point-Prompted Segmentation
Authors: Tanner Schmidt, Richard Newcombe
Date: 2025-06-10 00:00:00
Proceeding: CVPR 2025 1
Tasks: Foveation, Image Segmentation, Inductive Bias, Segmentation, Semantic Segmentation

This paper presents Segment This Thing (STT), a new efficient image segmentation model designed to produce a single segment given a single point prompt. Instead of following prior work and increasing efficiency by decreasing model size, we gain efficiency by foveating input images. Given an image and a point prompt, we extract a crop centered on the prompt and apply a novel variable-resolution patch tokenization in which patches are downsampled at a rate that increases with increased distance from the prompt. This approach yields far fewer image tokens than uniform patch tokenization. As a result we can drastically reduce the computational cost of segmentation without reducing model size. Furthermore, the foveation focuses the model on the region of interest, a potentially useful inductive bias. We show that our Segment This Thing model is more efficient than prior work while remaining competitive on segmentation benchmarks. It can easily run at interactive frame rates on consumer hardware and is thus a promising tool for augmented reality or robotics applications.
Fine-grained spatial-temporal perception for gas leak segmentation
Authors: Xinlong Zhao, Shan Du
Date: 2025-05-01 00:00:00
Tasks: Decoder, Segmentation

Gas leaks pose significant risks to human health and the environment. Despite long-standing concerns, there are limited methods that can efficiently and accurately detect and segment leaks due to their concealed appearance and random shapes. In this paper, we propose a Fine-grained Spatial-Temporal Perception (FGSTP) algorithm for gas leak segmentation. FGSTP captures critical motion clues across frames and integrates them with refined object features in an end-to-end network. Specifically, we first construct a correlation volume to capture motion information between consecutive frames. Then, the fine-grained perception progressively refines the object-level features using previous outputs. Finally, a decoder is employed to optimize boundary segmentation. Because there is no highly precise labeled dataset for gas leak segmentation, we manually label a gas leak video dataset, GasVid. Experimental results on GasVid demonstrate that our model excels in segmenting non-rigid objects such as gas leaks, generating the most accurate mask compared to other state-of-the-art (SOTA) models.
MatchPlant: An Open-Source Pipeline for UAV-Based Single-Plant Detection and Data Extraction
Authors: Worasit Sangjan, Piyush Pandey, Norman B. Best, Jacob D. Washburn
Date: 2025-06-14 00:00:00
Tasks: object-detection, Object Detection

Accurate identification of individual plants from unmanned aerial vehicle (UAV) images is essential for advancing high-throughput phenotyping and supporting data-driven decision-making in plant breeding. This study presents MatchPlant, a modular, graphical user interface-supported, open-source Python pipeline for UAV-based single-plant detection and geospatial trait extraction. MatchPlant enables end-to-end workflows by integrating UAV image processing, user-guided annotation, Convolutional Neural Network model training for object detection, forward projection of bounding boxes onto an orthomosaic, and shapefile generation for spatial phenotypic analysis. In an early-season maize case study, MatchPlant achieved reliable detection performance (validation AP: 89.6%, test AP: 85.9%) and effectively projected bounding boxes, covering 89.8% of manually annotated boxes with 87.5% of projections achieving an Intersection over Union (IoU) greater than 0.5. Trait values extracted from predicted bounding instances showed high agreement with manual annotations (r = 0.87-0.97, IoU >= 0.4). Detection outputs were reused across time points to extract plant height and Normalized Difference Vegetation Index with minimal additional annotation, facilitating efficient temporal phenotyping. By combining modular design, reproducibility, and geospatial precision, MatchPlant offers a scalable framework for UAV-based plant-level analysis with broad applicability in agricultural and environmental monitoring.
Organizational Adaptation to Generative AI in Cybersecurity: A Systematic Review
Authors: Christopher Nott
Date: 2025-05-31 00:00:00
Tasks: Large Language Model

Cybersecurity organizations are adapting to GenAI integration through modified frameworks and hybrid operational processes, with success influenced by existing security maturity, regulatory requirements, and investments in human capital and infrastructure. This qualitative research employs systematic document analysis and comparative case study methodology to examine how cybersecurity organizations adapt their threat modeling frameworks and operational processes to address generative artificial intelligence integration. Through examination of 25 studies from 2022 to 2025, the research documents substantial transformation in organizational approaches to threat modeling, moving from traditional signature-based systems toward frameworks incorporating artificial intelligence capabilities. The research identifies three primary adaptation patterns: Large Language Model integration for security applications, GenAI frameworks for risk detection and response automation, and AI/ML integration for threat hunting. Organizations with mature security infrastructures, particularly in finance and critical infrastructure sectors, demonstrate higher readiness through structured governance approaches, dedicated AI teams, and robust incident response processes. Organizations achieve successful GenAI integration when they maintain appropriate human oversight of automated systems, address data quality concerns and explainability requirements, and establish governance frameworks tailored to their specific sectors. Organizations encounter ongoing difficulties with privacy protection, bias reduction, personnel training, and defending against adversarial attacks. This work advances understanding of how organizations adopt innovative technologies in high-stakes environments and offers actionable insights for cybersecurity professionals implementing GenAI systems.
EgoPrivacy: What Your First-Person Camera Says About You?
Authors: Yijiang Li, Genpei Zhang, Jiacheng Cheng, Yi Li, Xiaojun Shan, Dashan Gao, Jiancheng Lyu, Yuan Li, Ning Bi, Nuno Vasconcelos
Date: 2025-06-13 00:00:00
Tasks: Retrieval

While the rapid proliferation of wearable cameras has raised significant concerns about egocentric video privacy, prior work has largely overlooked the unique privacy threats posed to the camera wearer. This work investigates the core question: How much privacy information about the camera wearer can be inferred from their first-person view videos? We introduce EgoPrivacy, the first large-scale benchmark for the comprehensive evaluation of privacy risks in egocentric vision. EgoPrivacy covers three types of privacy (demographic, individual, and situational), defining seven tasks that aim to recover private information ranging from fine-grained (e.g., wearer's identity) to coarse-grained (e.g., age group). To further emphasize the privacy threats inherent to egocentric vision, we propose Retrieval-Augmented Attack, a novel attack strategy that leverages ego-to-exo retrieval from an external pool of exocentric videos to boost the effectiveness of demographic privacy attacks. An extensive comparison of the different attacks possible under all threat models is presented, showing that private information of the wearer is highly susceptible to leakage. For instance, our findings indicate that foundation models can effectively compromise wearer privacy even in zero-shot settings by recovering attributes such as identity, scene, gender, and race with 70-80% accuracy. Our code and data are available at https://github.com/williamium3000/ego-privacy.
From Emergence to Control: Probing and Modulating Self-Reflection in Language Models
Authors: XUDONG ZHU, Jiachen Jiang, Mohammad Mahdi Khalili, Zhihui Zhu
Date: 2025-06-13 00:00:00
Tasks: Large Language Model, Navigate

Self-reflection -- the ability of a large language model (LLM) to revisit, evaluate, and revise its own reasoning -- has recently emerged as a powerful behavior enabled by reinforcement learning with verifiable rewards (RLVR). While self-reflection correlates with improved reasoning accuracy, its origin and underlying mechanisms remain poorly understood. In this work, {\it we first show that self-reflection is not exclusive to RLVR fine-tuned models: it already emerges, albeit rarely, in pretrained models}. To probe this latent ability, we introduce Reflection-Inducing Probing, a method that injects reflection-triggering reasoning traces from fine-tuned models into pretrained models. This intervention raises self-reflection frequency of Qwen2.5 from 0.6\% to 18.6\%, revealing a hidden capacity for reflection. Moreover, our analysis of internal representations shows that both pretrained and fine-tuned models maintain hidden states that distinctly separate self-reflective from non-reflective contexts. Leveraging this observation, {\it we then construct a self-reflection vector, a direction in activation space associated with self-reflective reasoning}. By manipulating this vector, we enable bidirectional control over the self-reflective behavior for both pretrained and fine-tuned models. Experiments across multiple reasoning benchmarks show that enhancing these vectors improves reasoning performance by up to 12\%, while suppressing them reduces computational cost, providing a flexible mechanism to navigate the trade-off between reasoning quality and efficiency without requiring additional training. Our findings further our understanding of self-reflection and support a growing body of work showing that understanding model internals can enable precise behavioral control.
EDformer: Transformer-Based Event Denoising Across Varied Noise Levels
Authors: Bin Jiang1⋆, Bo Xiong2⋆, Bohan Qu1, M. Salman Asif3, You Zhou4†, and Zhan Ma1†
Date: 2024-11-03 00:00:00
Proceeding: Computer Vision – ECCV 2024 2024 11
Tasks: Denoising

Currently, there is relatively limited research on the background activity noise of event cameras in different brightness conditions, and the relevant real-world datasets are extremely scarce. This limitation contributes to the lack of robustness in existing event denoising algorithms when applied in practical scenarios. This paper addresses this gap by collecting and analyzing background activity noise from the DAVIS346 event camera under different illumination conditions. We introduce the first real-world event denoising dataset, ED24, encompassing 21 noise levels and noise annotations. Furthermore, we propose EDformer, an innovative event-by-event denoising model based on transformer. This model excels in event denoising by learning the spatiotemporal correlations among events across varied noise levels. In comparison to existing denoising algorithms, the proposed EDformer achieves state-of-the-art performance in denoising accuracy, including open-source datasets and datasets captured in practical scenarios with low-light intensity requirements such as zebrafish blood vessels imaging.