Papers Multi-Task Learning
“Multi-Task Learning” 태그가 달린 논문 4,109편 · 필터 해제
FrameONE: Hierarchical Motion Modeling for Universal Multi-View Echocardiographic Keyframe Detection
Accurate detection of end-systole (ES) and end-diastole (ED) frames is fundamental to echocardiographic assessment. Existing methods are typically developed in a view-specific manner, depend on auxiliary annotations or i…
Representation LearningMulti-Task LearningPrivacy-Preserving Industrial Ergonomics: mmWave-Based Automated REBA Scoring and Pose Estimation
Work-related Musculoskeletal Disorders (WMSDs) require continuous ergonomic assessments. While Rapid Entire Body Assessment (REBA) is a gold-standard observation tool, manual monitoring is labor-intensive, and vision-bas…
Multi-Task LearningPose EstimationPoint CloudsArgus: Metric Panoramic 3D Reconstruction for Indoor Scenes
Metric feed-forward 3D reconstruction for panoramic data remains under-explored due to the lack of large-scale panoramic RGB-D training data. We present Realsee3D, a hybrid dataset of 10K indoor scenes (1K real, 9K synth…
Camera Pose EstimationMulti-Task Learning3D ReconstructionDepth EstimationLEIQ-Assessor: Multi-dimensional Quality Assessment of Low-light Enhanced Images via Multi-task Learning
Low-light image enhancement algorithms (LIEAs) aim to improve the visibility of images captured under poor illumination. However, the enhancement process often introduces artifacts such as noise amplification, color shif…
Low-Light Image EnhancementImage Quality AssessmentMulti-Task LearningMUSE: Unlocking Timestep as Native Task Steering for One-Step Dense Prediction
Monocular dense prediction has recently seen remarkable success by repurposing pre-trained diffusion models. This opens a promising yet challenging avenue for more efficient multi-task learning paradigm. However, existin…
Multi-Task LearningTemporally Consistent Label Interpolation for Robust Surgical Multi-Task Learning under Challenging Conditions
Effective multi-task learning for surgical scene understanding is fundamentally hindered by annotation granularity mismatch; temporal workflow tasks such as phase recognition, step recognition and anticipation benefit fr…
Surgical phase recognitionOptical Flow EstimationRepresentation LearningMulti-Task LearningIntracranial Aneurysm Classification and Segmentation via Tri-Axial ROI and Multi-Task Learning
Intracranial aneurysms are often asymptomatic until rupture, which carries high mortality. Rupture risk assessment and treatment planning depend on both aneurysm morphology and anatomical location, yet existing automated…
Multi-Label ClassificationMulti-Task LearningTowards Explainable Adjudicative Variance: Quantifying Judicial Discretion via Gated Multi-Task Learning
Legal outcome prediction must disentangle objective case facts from adjudicative context. Merit-based rulings rely on factual evidence while technical disposals may hinge on judicial discretion. We propose a Judge-Aware …
Multi-Task LearningBlasto-Net: An Explainable Multi-Task Learning for Blastocyst Segmentation, Grading, and Implantation Prediction
This study introduces Blasto-Net, a multi-task deep learning model for comprehensive blastocyst analysis. The proposed model performs three tasks simultaneously in a single forward pass: segmentation of the ZP, TE, and I…
Multi-Task LearningdVLA-RL: Reinforcement Learning over Denoising Trajectories for Discrete Diffusion Vision-Language-Action Models
Vision-Language-Action (VLA) models have established a powerful paradigm for generalist robotic manipulation by grounding control into the semantic reasoning of VLMs. Prevailing architectures typically model actions cont…
Reinforcement LearningMulti-Task LearningAgroSense 2.0: Cross-Modal Transformer Fusion with Geospatial Raster Integration and Interpretable Multi-Task Learning for Precision Crop Recommendation
Crop recommendation systems in precision agriculture have long suffered from a fundamental modality gap: visual soil characterization and chemical nutrient profiling are typically treated as independent inference problem…
Recommendation SystemsMulti-Task LearningStructure-Aware Graph Multi-Task Learning for Dynamic Sparse OD Demand Prediction
Origin-Destination (OD) demand prediction is fundamental to intelligent transportation systems, yet real-world OD flows are often dynamically sparse, long-tailed, and characterized by heterogeneous zero-flow patterns. Th…
Multi-Task LearningVQActFlow: Vector-Quantized Action Mode Steering for Multi-Task Robot Manipulation
Multi-task robot manipulation policies are challenging to learn from demonstration because traditionally a single network must select among qualitatively different action modes from a multimodal demonstration distributio…
Multi-Task LearningRobot ManipulationPACT: Preserving Anchored Cores in Task-vectors for Model Merging
Model merging has emerged as a training-free alternative to multi-task learning, aiming to combine multiple task-specific fine-tuned models into a single multi-task model. Most existing model merging approaches follow th…
Multi-Task LearningEssential Subspace Merging for Multi-Task Learning
Model merging aims to enable multi-task learning by integrating the capabilities of multiple models fine-tuned from the same pre-trained checkpoint into a single model. Its core challenge is inter-task interference among…
Multi-Task LearningInteractive Pareto navigation for deep multi-task learning
In multi-task learning, handling an increasing number of objectives can quickly become challenging, both in terms of the computational resources and the decision maker's capacity to choose appropriate trade-offs. A widel…
Multi-Task LearningUoU: A Universal Fingerprint Foundation Model Based on Large-Scale Unsupervised Learning
Fingerprint recognition is still dominated by task-specific pipelines, where enhancement, structural parsing, alignment, and matching are optimized in isolation. Although effective in narrow settings, this design limits …
Multi-Task LearningImage RestorationLearning to Orchestrate Vision Foundation Models for Multi-Task Dense Prediction
Vision foundation models (VFMs) exhibit complementary strengths shaped by their pretraining objectives. Yet prevailing methods for multi-task dense prediction still train an entire backbone, either by fine-tuning it unde…
Multi-Task LearningTowards Global AI-Driven Cervical Cancer Screening
The global elimination of cervical cancer is a key public health goal set by the World Health Organization (WHO), with screening programs reducing mortality by up to 80%. However, access to experts and biopsy services is…
Lesion SegmentationMulti-Task LearningData AugmentationIs Fairness Truly Fair? Towards Reliable Lipschitz Fairness in Multi-Task Learning via Fixed-\texorpdfstring{$δ$}{delta} Alignment
Lipschitz-style individual fairness formalizes the idea that semantically similar examples should receive similar predictions, but its evaluation in multi-task learning (MTL) can be confounded by method-induced represent…
Multi-Task Learning