Papers Multi-Task Learning
“Multi-Task Learning” 태그가 달린 논문 4,109편 · 필터 해제
Robust Multi-Source Covid-19 Detection in CT Images
Deep learning models for COVID-19 detection from chest CT scans generally perform well when the training and test data originate from the same institution, but they often struggle when scans are drawn from multiple centr…
Multi-Task LearningCOVID-19 DiagnosisMTLSI-Net: A Linear Semantic Interaction Network for Parameter-Efficient Multi-Task Dense Prediction
Multi-task dense prediction aims to perform multiple pixel-level tasks simultaneously. However, capturing global cross-task interactions remains non-trivial due to the quadratic complexity of standard self-attention on h…
Multi-Task LearningUniversal Hypernetworks for Arbitrary Models
Conventional hypernetworks are typically engineered around a specific base-model parameterization, so changing the target architecture often entails redesigning the hypernetwork and retraining it from scratch. We introdu…
Multi-Task LearningKG-CMI: Knowledge graph enhanced cross-Mamba interaction for medical visual question answering
Medical visual question answering (Med-VQA) is a crucial multimodal task in clinical decision support and telemedicine. Recent methods fail to fully leverage domain-specific medical knowledge, making it difficult to accu…
Knowledge Graph EmbeddingVisual Question AnsweringMulti-Task LearningReal-time Appearance-based Gaze Estimation for Open Domains
Appearance-based gaze estimation (AGE) has achieved remarkable performance in constrained settings, yet we reveal a significant generalization gap where existing AGE models often fail in practical, unconstrained scenario…
Multi-Task LearningGaze EstimationShared Representation for 3D Pose Estimation, Action Classification, and Progress Prediction from Tactile Signals
Estimating human pose, classifying actions, and predicting movement progress are essential for human-robot interaction. While vision-based methods suffer from occlusion and privacy concerns in realistic environments, tac…
3D Human Pose EstimationAction ClassificationMulti-Task Learning3D Pose EstimationPoseDriver: A Unified Approach to Multi-Category Skeleton Detection for Autonomous Driving
Object skeletons offer a concise representation of structural information, capturing essential aspects of posture and orientation that are crucial for autonomous driving applications. However, a unified architecture that…
Multi-Task LearningAutonomous DrivingLane DetectionFrequency Switching Mechanism for Parameter-E!cient Multi-Task Learning
Multi-task learning (MTL) aims to enable a single model to solve multiple tasks efficiently; however, current parameter-efficient fine-tuning (PEFT) methods remain largely limited to single-task adaptation. We introduce …
parameter-efficient fine-tuningMulti-Task LearningFAAR: Efficient Frequency-Aware Multi-Task Fine-Tuning via Automatic Rank Selection
Adapting models pre-trained on large-scale datasets is a proven way to reach strong performance quickly for down-stream tasks. However, the growth of state-of-the-art mod-els makes traditional full fine-tuning unsuitable…
parameter-efficient fine-tuningMulti-Task LearningEnhancing Multi-Corpus Training in SSL-Based Anti-Spoofing Models: Domain-Invariant Feature Extraction
The performance of speech spoofing detection often varies across different training and evaluation corpora. Leveraging multiple corpora typically enhances robustness and performance in fields like speaker recognition and…
Speaker RecognitionMulti-Task LearningSpeech RecognitionShared Representation Learning for Reference-Guided Targeted Sound Detection
Human listeners exhibit the remarkable ability to segregate a desired sound from complex acoustic scenes through selective auditory attention, motivating the study of Targeted Sound Detection (TSD). The task requires det…
Representation LearningMulti-Task LearningA Hybrid Modeling Framework for Crop Prediction Tasks via Dynamic Parameter Calibration and Multi-Task Learning
Accurate prediction of crop states (e.g., phenology stages and cold hardiness) is essential for timely farm management decisions such as irrigation, fertilization, and canopy management to optimize crop yield and quality…
Multi-Task LearningRoCo Challenge at AAAI 2026: Benchmarking Robotic Collaborative Manipulation for Assembly Towards Industrial Automation
Embodied Artificial Intelligence (EAI) is rapidly developing, gradually subverting previous autonomous systems' paradigms from isolated perception to integrated, continuous action. This transition is highly significant f…
Multi-Task LearningCMHL: Contrastive Multi-Head Learning for Emotionally Consistent Text Classification
Textual Emotion Classification (TEC) is one of the most difficult NLP tasks. State of the art approaches rely on Large language models (LLMs) and multi-model ensembles. In this study, we challenge the assumption that lar…
Emotion ClassificationDomain GeneralizationMulti-Task LearningText ClassificationSemantic Aware Feature Extraction for Enhanced 3D Reconstruction
Feature matching is a fundamental problem in computer vision with wide-ranging applications, including simultaneous localization and mapping (SLAM), image stitching, and 3D reconstruction. While recent advances in deep l…
Semantic SegmentationMulti-Task LearningKeypoint Detection3D ReconstructionSharpness-Aware Minimization for Generalized Embedding Learning in Federated Recommendation
Federated recommender systems enable collaborative model training while keeping user interaction data local and sharing only essential model parameters, thereby mitigating privacy risks. However, existing methods overloo…
Multi-Task LearningUniPINN: A Unified PINN Framework for Multi-task Learning of Diverse Navier-Stokes Equations
Physics-Informed Neural Networks (PINNs) have shown promise in solving incompressible Navier-Stokes equations, yet existing approaches are predominantly designed for single-flow settings. When extended to multi-flow scen…
Multi-Task LearningRiemannian MeanFlow for One-Step Generation on Manifolds
Flow Matching enables simulation-free training of generative models on Riemannian manifolds, yet sampling typically still relies on numerically integrating a probability-flow ODE. We propose Riemannian MeanFlow (RMF), ex…
Multi-Task LearningCORAL: Scalable Multi-Task Robot Learning via LoRA Experts
Deploying Vision-Language-Action (VLA) models in real-world robotics exposes a core multi-task learning challenge: reconciling task interference in multi-task robotic learning. When multiple tasks are jointly fine-tuned …
Multi-Task LearningA Unified Hierarchical Multi-Task Multi-Fidelity Framework for Data-Efficient Surrogate Modeling in Manufacturing
Surrogate modeling is an essential data-driven technique for quantifying relationships between input variables and system responses in manufacturing and engineering systems. Two major challenges limit its effectiveness: …
Multi-Task Learning