Papers Multi-Task Learning
“Multi-Task Learning” 태그가 달린 논문 4,109편 · 필터 해제
Parameter-efficient Quantum Multi-task Learning
Multi-task learning (MTL) improves generalization and data efficiency by jointly learning related tasks through shared representations. In the widely used hard-parameter-sharing setting, a shared backbone is combined wit…
Quantum Machine LearningMulti-Task LearningSarcasm DetectionAutomatically Inferring Teachers' Geometric Content Knowledge: A Skills Based Approach
Assessing teachers' geometric content knowledge is essential for geometry instructional quality and student learning, but difficult to scale. The Van Hiele model characterizes geometric reasoning through five hierarchica…
Multi-Task LearningHeuristic Style Transfer for Real-Time, Efficient Weather Attribute Detection
We present lightweight and efficient architectures to detect weather conditions from RGB images, predicting the weather type (sunny, rain, snow, fog) and 11 complementary attributes such as intensity, visibility, and gro…
Multi-Task LearningStyle TransferCycloneMAE: A Scalable Multi-Task Learning Model for Global Tropical Cyclone Probabilistic Forecasting
Tropical cyclones (TCs) rank among the most destructive natural hazards, yet their forecasting faces fundamental trade-offs: numerical weather prediction (NWP) models are computationally prohibitive and struggle to lever…
Multi-Task LearningTCL: Enabling Fast and Efficient Cross-Hardware Tensor Program Optimization via Continual Learning
Deep learning (DL) compilers rely on cost models and auto-tuning to optimize tensor programs for target hardware. However, existing approaches depend on large offline datasets, incurring high collection costs and offerin…
Knowledge DistillationMulti-Task LearningContinual LearningActive LearningRadar-Camera BEV Multi-Task Learning with Cross-Task Attention Bridge for Joint 3D Detection and Segmentation
Bird's-eye-view (BEV) representations are the dominant paradigm for 3D perception in autonomous driving, providing a unified spatial canvas where detection and segmentation features are geometrically registered to the sa…
Multi-Task LearningAutonomous DrivingAttention to task structure for cognitive flexibility
Humans and artificial agents must often learn and switch between multiple tasks in dynamic environments. Success in such settings requires cognitive flexibility: the ability to retain prior knowledge (cognitive stability…
Multi-Task LearningTeaching Robots to Interpret Social Interactions through Lexically-guided Dynamic Graph Learning
For a robot to be called socially intelligent, it must be able to infer users internal states from their current behaviour, predict the users future behaviour, and if required, respond appropriately. In this work, we inv…
Multi-Task LearningDecision MakingGraph LearningNeuVolEx: Implicit Neural Features for Volume Exploration
Direct volume rendering (DVR) aims to help users identify and examine regions of interest (ROIs) within volumetric data, and feature representations that support effective ROI classification and clustering play a fundame…
Multi-Task LearningDelve into the Applicability of Advanced Optimizers for Multi-Task Learning
Multi-Task Learning (MTL) is a foundational machine learning problem that has seen extensive development over the past decade. Recently, various optimization-based MTL approaches have been proposed to learn multiple task…
Multi-Task LearningDomain-generalizable Face Anti-Spoofing with Patch-based Multi-tasking and Artifact Pattern Conversion
Face Anti-Spoofing (FAS) algorithms, designed to secure face recognition systems against spoofing, struggle with limited dataset diversity, impairing their ability to handle unseen visual domains and spoofing methods. We…
Domain GeneralizationMulti-Task LearningFace Anti-SpoofingFace RecognitionCAD 100K: A Comprehensive Multi-Task Dataset for Car Related Visual Anomaly Detection
Multi-task visual anomaly detection is critical for car-related manufacturing quality assessment. However, existing methods remain task-specific, hindered by the absence of a unified benchmark for multi-task evaluation. …
Multi-Task LearningData AugmentationAnomaly DetectionInformation-Theoretic Requirements for Gradient-Based Task Affinity Estimation in Multi-Task Learning
Multi-task learning shows strikingly inconsistent results -- sometimes joint training helps substantially, sometimes it actively harms performance -- yet the field lacks a principled framework for predicting these outcom…
Multi-Task LearningJoint Interference Detection and Identification via Adversarial Multi-task Learning
Precise interference detection and identification are crucial for enhancing the survivability of communication systems in non-cooperative wireless environments. While deep learning (DL) has advanced this field, existing …
Multi-Task LearningCognitive-Causal Multi-Task Learning with Psychological State Conditioning for Assistive Driving Perception
Multi-task learning for advanced driver assistance systems requires modeling the complex interplay between driver internal states and external traffic environments. However, existing methods treat recognition tasks as fl…
Emotion RecognitionMulti-Task LearningOmniCamera: A Unified Framework for Multi-task Video Generation with Arbitrary Camera Control
Video fundamentally intertwines two crucial axes: the dynamic content of a scene and the camera motion through which it is observed. However, existing generation models often entangle these factors, limiting independent …
Multi-Task LearningVideo GenerationEAGLE: Edge-Aware Graph Learning for Proactive Delivery Delay Prediction in Smart Logistics Networks
Modern logistics networks generate rich operational data streams at every warehouse node and transportation lane -- from order timestamps and routing records to shipping manifests -- yet predicting delivery delays remain…
Multi-Task LearningAnomaly DetectionGraph LearningLAA-X: Unified Localized Artifact Attention for Quality-Agnostic and Generalizable Face Forgery Detection
In this paper, we propose Localized Artifact Attention X (LAA-X), a novel deepfake detection framework that is both robust to high-quality forgeries and capable of generalizing to unseen manipulations. Existing approache…
Multi-Task LearningDeepFake DetectionA Family of Open Time-Series Foundation Models for the Radio Access Network
The Radio Access Network (RAN) is evolving into a programmable and disaggregated infrastructure that increasingly relies on AI-native algorithms for optimization and closed-loop control. However, current RAN intelligence…
Multi-Task LearningAnomaly DetectionHyperCT: Low-Rank Hypernet for Unified Chest CT Analysis
Non-contrast chest CTs offer a rich opportunity for both conventional pulmonary and opportunistic extra-pulmonary screening. While Multi-Task Learning (MTL) can unify these diverse tasks, standard hard-parameter sharing …
Computational EfficiencyMulti-Task Learning