Auxiliary Learning
25 papers with code • 0 benchmarks • 0 datasets
Auxiliary learning aims to find or design auxiliary tasks which can improve the performance on one or some primary tasks.
( Image credit: Self-Supervised Generalisation with Meta Auxiliary Learning )
Benchmarks
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Latest papers with no code
Meta-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.
HiMAL: A Multimodal Hierarchical Multi-task Auxiliary Learning framework for predicting and explaining Alzheimer disease progression
Discussion: Clinically informative model explanations anticipate cognitive decline 6 months in advance, aiding clinicians in future disease progression assessment.
Learning Multiple Representations with Inconsistency-Guided Detail Regularization for Mask-Guided Matting
Our framework and model introduce the following key aspects: (1) to learn real-world adaptive semantic representation for objects with diverse and complex structures under real-world scenes, we introduce extra semantic segmentation and edge detection tasks on more diverse real-world data with segmentation annotations; (2) to avoid overfitting on low-level details, we propose a module to utilize the inconsistency between learned segmentation and matting representations to regularize detail refinement; (3) we propose a novel background line detection task into our auxiliary learning framework, to suppress interference of background lines or textures.
Auxiliary Tasks Enhanced Dual-affinity Learning for Weakly Supervised Semantic Segmentation
We propose AuxSegNet+, a weakly supervised auxiliary learning framework to explore the rich information from these saliency maps and the significant inter-task correlation between saliency detection and semantic segmentation.
Two-Stage Multi-task Self-Supervised Learning for Medical Image Segmentation
Self-supervised learning offers a solution by creating auxiliary learning tasks from the available dataset and then leveraging the knowledge acquired from solving auxiliary tasks to help better solve the target segmentation task.
A 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 (ranging from inter-sequence to intra-sequence and from single-domain to cross-domain).
Mitigate Domain Shift by Primary-Auxiliary Objectives Association for Generalizing Person ReID
While deep learning has significantly improved ReID model accuracy under the independent and identical distribution (IID) assumption, it has also become clear that such models degrade notably when applied to an unseen novel domain due to unpredictable/unknown domain shift.
Perception Reinforcement Using Auxiliary Learning Feature Fusion: A Modified Yolov8 for Head Detection
Head detection provides distribution information of pedestrian, which is crucial for scene statistical analysis, traffic management, and risk assessment and early warning.
Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning
In this paper, we propose a novel framework, dubbed Detaux, whereby a weakly supervised disentanglement procedure is used to discover new unrelated classification tasks and the associated labels that can be exploited with the principal task in any Multi-Task Learning (MTL) model.
Semantic-aware Temporal Channel-wise Attention for Cardiac Function Assessment
Cardiac function assessment aims at predicting left ventricular ejection fraction (LVEF) given an echocardiogram video, which requests models to focus on the changes in the left ventricle during the cardiac cycle.
