Late Fusion

Latest papers

9 papers1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

MAC-Net: A Multi-Task Deep Learning Framework for Modeling Cognitive Function From Task-Based fMRI
Sep 27, 2026Md. Tanvir Rahman, Nabil Anan Orka, Asaduzzaman Khan +1Late Fusion Multi-Task Learning
School of Health and Rehabilitation Sciences, The University of Queensland, QLD 4072, Australia · Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail 1902, Bangladesh
Less Is More: Graph-free Multimodal RAG via Multi-signal Late Fusion
Sep 16, 2026Tithi Rakshit, Hongkuan Zhou, Lavdim Halilaj +1Late Fusion Multimodal RAG
University of Tübingen, Tübingen, Germany · Corporate Research, Robert Bosch GmbH, Renningen, Germany · University of Stuttgart, Stuttgart, Germany
LLM-Enhanced Dual-Branch Learning for Large-Scale Multi-Label Text Classification
Sep 11, 2026Hui Ye, Jing Zhang, Xiulong Yang +1Late Fusion Multi-Label Classification
Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA · Amazon, San Diego, CA, USA · School of Computer Science, Central China Normal University, Wuhan 430079, China
How Much Reconstruction Does Quantum Machine Learning Need? Late Fusion of Independently Trained Quantum Subcircuits
Aug 6, 2026Prabhjot Singh, Adel N. Toosi, Rajkumar BuyyaLate Fusion Quantum Machine Learning
Quantum Cloud Computing and Distributed Systems (qCLOUDS) Lab, School of Computing and Information Systems, The University of Melbourne, Melbourne, VIC, 3010, Australia
Speech Signals Complement LLMs for Predicting Interpersonal Attraction in Speed Dating
Jul 25, 2026Yuriko Kikuchi, Takato Hayashi, Ryusei Kimura +3Late Fusion Speech Processing
Japan Advanced Institute of Science and Technology · Nomi, Ishikawa, Japan · NTT, Inc. +1
From Text Metrics to Model Internals: A Study of Whisper ASR Hallucination Detection
Jun 22, 2026Jan Jasiński, Mateusz Barański, Julitta Bartolewska +2Late Fusion Audio-Language Model Hallucination Detection
Signal Processing Group, Institute of Electronics, AGH University of Krakow, Poland
Collaborative Trajectory Prediction via Late Fusion
Apr 24, 2026Nadya Abdel Madjid, Murad Mebrahtu, Zakhar Yagudin +5Late Fusion Multi-Agent Trajectory Prediction
Khalifa University, Computer Science, Abu Dhabi, UAE · Khalifa University, KUCARS-KU Center for Autonomous Robotic Systems, Department of Computer Science, Abu Dhabi, UAE · Khalifa University, Computer and Information Engineering, Abu Dhabi, UAE +2
HyDRA: Hybrid Domain-Aware Robust Architecture for Heterogeneous Collaborative Perception
Mar 25, 2026Minwoo Song, Minhee Kang, Heejin AhnLate Fusion Heterogeneous Collaborative Perception
School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
Prompt Amplification and Zero-Shot Late Fusion in Audio-Language Models for Speech Emotion Recognition
Mar 24, 2026Saurabh Kataria, Xiao HuLate Fusion Speech Emotion Recognition
School of Nursing, Emory University, Atlanta, USA

Sep 27, 2026J/K move · Enter open · S save

MAC-Net: A Multi-Task Deep Learning Framework for Modeling Cognitive Function From Task-Based fMRI

Md. Tanvir Rahman, Nabil Anan Orka, Asaduzzaman Khan, Mohammad Ali Moni

School of Health and Rehabilitation Sciences, The University of Queensland, QLD 4072, Australia · Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail 1902, Bangladesh

Late Fusion Multi-Task Learning Neuroimaging

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Objective cognitive assessment from neural signals supports neurorehabilitation, but individual-level prediction from task-based fMRI (tfMRI) remains difficult because neural features coexist with substantial demographic and scanner-related variation. We present the Multi-task Activation and Contrast Network (MAC-Net), a covariate-aware deep learning framework for modeling individual cognitive function from regional tfMRI. By isolating tfMRI features into a dedicated neural pathway and restricting participant variables to a terminal late-fusion pathway, MAC-Net prevents dominant covariates from suppressing high-dimensional clinical representations during feature learning. Evaluating baseline data from 6,500 Adolescent Brain Cognitive Development Study participants under family-aware cross-validation, MAC-Net was benchmarked against linear models, random forests, and alternative deep architectures. The N-back plus Monetary Incentive Delay configuration achieved

R^{2}

values of 0.174, 0.238, and 0.277 for fluid, crystallized, and total cognition, outperforming covariate-only baselines (0.178) and alternative deep models (0.217). N-back was the most informative paradigm, whereas incorporating the Stop Signal Task marginally degraded performance. Feature attributions via Integrated Gradients, DeepLIFT, and Input Gradient were highly concordant, localizing working-memory-related frontal, parietal, and cingulate regions. These findings demonstrate that covariate-aware multi-task modeling yields reproducible cognitive-function estimations, establishing a robust neural engineering framework for clinical translation.