Data Detection
Data detection research focuses on reliably identifying patterns and anomalies within diverse data types, aiming to improve accuracy and efficiency across various applications. Current efforts concentrate on enhancing existing models like YOLO and convolutional neural networks, incorporating techniques such as few-shot learning, ensemble methods, and vision-language models to address challenges like imbalanced datasets, adversarial attacks, and low-light conditions. These advancements have significant implications for fields ranging from autonomous driving and healthcare diagnostics to combating misinformation and securing AI models.
Papers
Excretion Detection in Pigsties Using Convolutional and Transformerbased Deep Neural Networks
Simon Mielke, Anthony Stein
To Ensemble or Not: Assessing Majority Voting Strategies for Phishing Detection with Large Language Models
Fouad Trad, Ali Chehab
Real-Time Anomaly Detection in Video Streams
Fabien Poirier
Forensics Adapter: Adapting CLIP for Generalizable Face Forgery Detection
Xinjie Cui, Yuezun Li, Ao Luo, Jiaran Zhou, Junyu Dong
Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook
Florinel-Alin Croitoru, Andrei-Iulian Hiji, Vlad Hondru, Nicolae Catalin Ristea, Paul Irofti, Marius Popescu, Cristian Rusu, Radu Tudor Ionescu, Fahad Shahbaz Khan, Mubarak Shah
Multi-task CNN Behavioral Embedding Model For Transaction Fraud Detection
Bo Qu, Zhurong Wang, Minghao Gu, Daisuke Yagi, Yang Zhao, Yinan Shan, Frank Zahradnik
Knowledge-Augmented Explainable and Interpretable Learning for Anomaly Detection and Diagnosis
Martin Atzmueller, Tim Bohne, Patricia Windler
Beyond Logit Lens: Contextual Embeddings for Robust Hallucination Detection & Grounding in VLMs
Anirudh Phukan, Divyansh, Harshit Kumar Morj, Vaishnavi, Apoorv Saxena, Koustava Goswami
Dynamic Attention and Bi-directional Fusion for Safety Helmet Wearing Detection
Junwei Feng, Xueyan Fan, Yuyang Chen, Yi Li
GloFinder: AI-empowered QuPath Plugin for WSI-level Glomerular Detection, Visualization, and Curation
Jialin Yue, Tianyuan Yao, Ruining Deng, Siqi Lu, Junlin Guo, Quan Liu, Mengmeng Yin, Juming Xiong, Haichun Yang, Yuankai Huo
Living off the Analyst: Harvesting Features from Yara Rules for Malware Detection
Siddhant Gupta, Fred Lu, Andrew Barlow, Edward Raff, Francis Ferraro, Cynthia Matuszek, Charles Nicholas, James Holt
HDI-Former: Hybrid Dynamic Interaction ANN-SNN Transformer for Object Detection Using Frames and Events
Dianze Li, Jianing Li, Xu Liu, Zhaokun Zhou, Xiaopeng Fan, Yonghong Tian
Anomaly Detection and RFI Classification with Unsupervised Learning in Narrowband Radio Technosignature Searches
Ben Jacobson-Bell, Steve Croft, Carmen Choza, Alex Andersson, Daniel Bautista, Vishal Gajjar, Matthew Lebofsky, David H. E. MacMahon, Caleb Painter, Andrew P. V. Siemion
No Identity, no problem: Motion through detection for people tracking
Martin Engilberge, F. Wilke Grosche, Pascal Fua
Twin Trigger Generative Networks for Backdoor Attacks against Object Detection
Zhiying Li, Zhi Liu, Guanggang Geng, Shreyank N Gowda, Shuyuan Lin, Jian Weng, Xiaobo Jin
Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lens
Zhangqi Jiang, Junkai Chen, Beier Zhu, Tingjin Luo, Yankun Shen, Xu Yang