Papers Auxiliary Learning
“Auxiliary Learning” 태그가 달린 논문 100편 · 필터 해제
SDF-TopoNet: A Two-Stage Framework for Tubular Structure Segmentation via SDF Pre-training and Topology-Aware Fine-Tuning
Accurate segmentation of tubular and curvilinear structures, such as blood vessels, neurons, and road networks, is crucial in various applications. A key challenge is ensuring topological correctness while maintaining co…
Auxiliary LearningComputational EfficiencySegmentationBehavior Importance-Aware Graph Neural Architecture Search for Cross-Domain Recommendation
Cross-domain recommendation (CDR) mitigates data sparsity and cold-start issues in recommendation systems. While recent CDR approaches using graph neural networks (GNNs) capture complex user-item interactions, they rely …
Auxiliary LearningNeural Architecture SearchRecommendation SystemsDocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning
Despite recent significant advancements in Handwritten Document Recognition (HDR), the efficient and accurate recognition of text against complex backgrounds, diverse handwriting styles, and varying document layouts rema…
Auxiliary LearningHandwritten Document RecognitionMeta-LearningLAL: Enhancing 3D Human Motion Prediction with Latency-aware Auxiliary Learning
Making accurate prediction of human motions based on the historical observation is a crucial technology for robots to collaborate with humans. Existing human motion prediction methods are all built under an ideal ass…
Auxiliary LearningHuman motion predictionmotion predictionPredictionSGTC: Semantic-Guided Triplet Co-training for Sparsely Annotated Semi-Supervised Medical Image Segmentation
Although semi-supervised learning has made significant advances in the field of medical image segmentation, fully annotating a volumetric sample slice by slice remains a costly and time-consuming task. Even worse, most o…
Auxiliary LearningImage SegmentationMedical Image SegmentationSegmentation+3Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions
In multimedia broadcasting, no-reference image quality assessment (NR-IQA) is used to indicate the user-perceived quality of experience (QoE) and to support intelligent data transmission while optimizing user experience.…
Auxiliary LearningBenchmarkingImage Quality AssessmentNo-Reference Image Quality Assessment+1Self-supervised Auxiliary Learning for Texture and Model-based Hybrid Robust and Fair Featuring in Face Analysis
In this work, we explore Self-supervised Learning (SSL) as an auxiliary task to blend the texture-based local descriptors into feature modelling for efficient face analysis. Combining a primary task and a self-supervised…
AttributeAuxiliary LearningDeepFake DetectionEmotion Recognition+2Learning Representation for Multitask learning through Self Supervised Auxiliary learning
Multi-task learning is a popular machine learning approach that enables simultaneous learning of multiple related tasks, improving algorithmic efficiency and effectiveness. In the hard parameter sharing approach, an enco…
Auxiliary LearningComputational EfficiencyMulti-Task LearningWhen does Self-Prediction help? Understanding Auxiliary Tasks in Reinforcement Learning
We investigate the impact of auxiliary learning tasks such as observation reconstruction and latent self-prediction on the representation learning problem in reinforcement learning. We also study how they interact with d…
Auxiliary LearningPredictionreinforcement-learningRepresentation LearningRepresentation Learning For Efficient Deep Multi-Agent Reinforcement Learning
Sample efficiency remains a key challenge in multi-agent reinforcement learning (MARL). A promising approach is to learn a meaningful latent representation space through auxiliary learning objectives alongside the MARL o…
Auxiliary LearningMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning+1Aux-NAS: Exploiting Auxiliary Labels with Negligibly Extra Inference Cost
We aim at exploiting additional auxiliary labels from an independent (auxiliary) task to boost the primary task performance which we focus on, while preserving a single task inference cost of the primary task. While most…
Auxiliary LearningNeural Architecture SearchMeta-Auxiliary Learning for Micro-Expression Recognition
Micro-expressions (MEs) are involuntary movements revealing people's hidden feelings, which has attracted numerous interests for its objectivity in emotion detection. However, despite its wide applications in various sce…
Auxiliary LearningMicro Expression RecognitionMicro-Expression RecognitionHiMAL: A Multimodal Hierarchical Multi-task Auxiliary Learning framework for predicting and explaining Alzheimer disease progression
Objective: We aimed to develop and validate a novel multimodal framework HiMAL (Hierarchical, Multi-task Auxiliary Learning) framework, for predicting cognitive composite functions as auxiliary tasks that estimate the lo…
Auxiliary LearningMulti-Task LearningGeoAuxNet: Towards Universal 3D Representation Learning for Multi-sensor Point Clouds
Point clouds captured by different sensors such as RGB-D cameras and LiDAR possess non-negligible domain gaps. Most existing methods design different network architectures and train separately on point clouds from variou…
Auxiliary LearningRepresentation LearningLearning Multiple Representations with Inconsistency-Guided Detail Regularization for Mask-Guided Matting
Mask-guided matting networks have achieved significant improvements and have shown great potential in practical applications in recent years. However, simply learning matting representation from synthetic and lack-of-rea…
Auxiliary LearningEdge DetectionImage MattingLine Detection+2Auxiliary Tasks Enhanced Dual-affinity Learning for Weakly Supervised Semantic Segmentation
Most existing weakly supervised semantic segmentation (WSSS) methods rely on Class Activation Mapping (CAM) to extract coarse class-specific localization maps using image-level labels. Prior works have commonly used an o…
Auxiliary Learningimage-classificationImage ClassificationMulti-Label Image Classification+6Two-Stage Multi-task Self-Supervised Learning for Medical Image Segmentation
Medical image segmentation has been significantly advanced by deep learning (DL) techniques, though the data scarcity inherent in medical applications poses a great challenge to DL-based segmentation methods. Self-superv…
Auxiliary LearningImage SegmentationKnowledge DistillationMedical Image Segmentation+3Enhancing Molecular Property Prediction with Auxiliary Learning and Task-Specific Adaptation
Pretrained Graph Neural Networks have been widely adopted for various molecular property prediction tasks. Despite their ability to encode structural and relational features of molecules, traditional fine-tuning of such …
Auxiliary LearningMolecular Property PredictionProperty PredictionA Survey on Cross-Domain Sequential Recommendation
Cross-domain sequential recommendation (CDSR) shifts the modeling of user preferences from flat to stereoscopic by integrating and learning interaction information from multiple domains at different granularities (rangin…
Auxiliary LearningSequential RecommendationSurveyFast Adaptation for Human Pose Estimation via Meta-Optimization
Domain shift is a challenge for supervised human pose estimation where the source data and target data come from different distributions. This is why pose estimation methods generally perform worse on the test set th…
Auxiliary LearningImage InpaintingMeta-LearningPose Estimation+2