Papers Multi-Task Learning
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UniH^3: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration
All-in-One medical image restoration (MedIR) aims to address diverse tasks across modalities and degradation types using a single universal model. Existing methods typically prioritize modeling inter-task heterogeneity (…
Multi-Task LearningImage RestorationMulti-Functional Embedding Models for Funder Name Disambiguation in Scientific Publication Records
Understanding the historical allocation and distribution of research funding advances our knowledge of how scientific research is supported across fields, institutions, and regions. However, large-scale analyses are hind…
Multi-Task LearningFederated Multi-Task Learning for Bladder Tumor Segmentation and MIBC Classification Using a Hybrid CNN-Transformer Architecture
Accurate bladder tumor segmentation and assessment of mus- cle invasion from T2-weighted MRI are important for treatment plan- ning, but developing robust models across institutions is challenging be- cause patient data …
Multi-Task LearningTumor SegmentationMonroe: A Molecular Foundation Model for In-Context Probabilistic Inference
Bioassay activity prediction is often data-limited because drug-discovery datasets rely on time-consuming and expensive wet-lab experiments for data generation and evaluation. This challenge has inspired recent research …
Activity PredictionMulti-Task LearningLearning Unified Video and Image Representation for Video Face Forgery Detection
Face forgery detection is crucial for preserving the security and integrity of facial data given the rapid developments in face manipulation techniques and deep generative models. Existing methods for video face forgery …
Multi-Task LearningEnhancing Scientific Named Entity Recognition via Large Language Models: A Type-driven Multi-task Learning Approach
Scientific named entity recognition (SciNER) plays a crucial role in information extraction and knowledge discovery from scientific texts. Recently, large language models (LLMs) have demonstrated the capacity to achieve …
Information ExtractionMulti-Task LearningEntity TypingOneEmo: A Unified Multimodal Reasoning Model for Emotion Perception, Understanding, and Interaction
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in emotional intelligence. However, prevailing research predominantly focuses on task-specific specialization, often neglecting inter-tas…
Reinforcement LearningEmotional IntelligenceMultimodal ReasoningMulti-Task LearningSFT Conflicts, RL Coexists: A Theoretical and Empirical Analysis of Multi-Task Learning for LLMs
Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) exhibit fundamentally different behaviors in enhancing multi-task reasoning for large language models (LLMs). Our preliminary experiments revealed a phenomenon…
Reinforcement LearningMulti-Task LearningMulti-Task Learning for Heterogeneous Prediction from Video Game State with Transfer Learning
Multi-task learning (MTL) is a promising approach for prediction tasks derived from video game state data, as modern game telemetry provides multiple related supervision signals from the same structured observations. We …
Multi-Task LearningTransfer LearningMAGE-Vein: Multi-Instance Age and Gender Estimation from Finger Vein Images
Age estimation from finger vein images has been widely considered impractical due to severe demographic biases in public datasets and physiological confounding factors like gender. To overcome these limitations, we propo…
Age and Gender EstimationMulti-Task LearningAge EstimationDPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction
Multi-Task Learning (MTL) in robotics perception systems supports comprehensive 3D spatial scene understanding by integrating semantic segmentation and depth estimation. While Vision Foundation Models (VFMs) are increasi…
Semantic SegmentationMulti-Task LearningScene UnderstandingDepth EstimationSeerGuard: A Safety Framework for Mobile GUI Agents via World Model Prediction
Mobile graphical user interface (GUI) agents have demonstrated remarkable capabilities in automating complex tasks, yet they introduce critical safety risks where a single erroneous action can lead to irreversible conseq…
Multi-Task LearningHSEmotion Team at the 11th ABAW Challenge: Multi-Task Learning and Ambivalence/Hesitancy Video Recognition
This article presents our results for the 11th Affective Behavior Analysis in-the-Wild (ABAW) competition. For multi-task learning with simultaneous prediction of valence, arousal, facial expressions, and action units on…
Multi-Task LearningAffectFlow-DINO: Uncertainty-Aware Multi-Task Affect Estimation via Conditional Rectified Flow
We present AffectFlow-DINO, a multi-task learning system for the 11th ABAW challenge that extends a standard deterministic architecture with a conditional rectified-flow head to model the inherent ambiguity of in-the-wil…
Multi-Task LearningCausal Supervision of Attention for Affective Behaviour Analysis
The \textit{11th Affective Behaviour Analysis in-the-wild Competition} includes the Multi-Task Learning Challenge, where participants develop a unified framework for Valence-Arousal Estimation, Expression Recognition, an…
Action Unit DetectionMulti-Task LearningCompositional Context Fine-Tuning Vision-Language Model for Complex Assembly Action Understanding from Videos
Assembly action understanding is a key enabler for effective human-robot collaborative assembly, yet it remains challenging due to subtle motions and fine-grained hand-object interactions. We adapt vision-language models…
Hyperparameter OptimizationAction UnderstandingMulti-Task LearningAction Recognition4DR360: State Reasoning for Joint 3D Detection and Occupancy Prediction in 4D Radar-Camera Full-Scene Perception
Reliable autonomous driving requires full-scene perception that couples foreground objects with dense semantic layout. Recently, 4D millimeter-wave radar has emerged as a robust and affordable sensor, yet its sparse retu…
Multi-Task LearningScene UnderstandingAutonomous DrivingVideo-Text Temporal Localization via Multi-Scale Convolution and Dynamic Routing
Video-text temporal localization requires precise alignment between natural language queries and corresponding video segments, a fundamental challenge in multimodal understanding. We present a novel framework that addres…
Computational EfficiencyNatural Language QueriesSemantic CommunicationMulti-Task LearningCo-Adaptive Multi-Task LoRA: Transfer-Aware, Label-Free Control of Domain Participation
Fine-tuning a single low-rank adapter on many domains at once is multi-task learning: the domains must be co-learned, and how they share the adapter decides whether they help or hurt one another. Most efficient fine-tuni…
Multi-Task LearningContaminated Multi-task Learning with Heterogeneity: Fundamental Limits and Optimal Algorithms
Integrating information across related tasks can improve estimation and prediction in transfer, multi-task, and federated learning, but contamination and heterogeneity make robust borrowing challenging. We study a contam…
Multi-Task LearningFederated LearningOutlier Detection