Multi-task Adaptation Research Articles

VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Scene Understanding

Large-scale pre-trained models have achieved remarkable success in various computer vision tasks. A standard approach to leverage these models is to fine-tune all model parameters for downstream tasks, which poses challenges in terms of computational and storage costs. Recently, inspired by Natural Language Processing (NLP), parameter-efficient transfer learning has been successfully applied to vision tasks. However, most existing techniques primarily focus on single-task adaptation, and despite limited research on multi-task adaptation, these methods often exhibit suboptimal training/inference efficiency. In this paper, we first propose an once-for-all Vision Multi-Task Adapter (VMT-Adapter), which strikes approximately O(1) training and inference efficiency w.r.t task number. Concretely, VMT-Adapter shares the knowledge from multiple tasks to enhance cross-task interaction while preserves task-specific knowledge via independent knowledge extraction modules. Notably, since task-specific modules require few parameters, VMT-Adapter can handle an arbitrary number of tasks with a negligible increase of trainable parameters. We also propose VMT-Adapter-Lite, which further reduces the trainable parameters by learning shared parameters between down- and up-projections. Extensive experiments on four dense scene understanding tasks demonstrate the superiority of VMT-Adapter(-Lite), achieving a 3.96% (1.34%) relative improvement compared to single-task full fine-tuning, while utilizing merely ～1% (0.36%) trainable parameters of the pre-trained model.

Configuration Design and Analysis of Reconfigurable Supernumerary Robotic Legs for Multitask Adaptation: SuperLegs

Supernumerary robotic limbs (SRLs) are a new type of wearable robot that add artificial limbs to the human body to perform collaborative tasks. In contrast with exoskeletons, SRLs are kinematically independent of human limbs, allowing the wearer to overcome the limitations of human physiological ability, such as realizing the expansion of space in the human body, rather than enhancing existing limbs. In this study, a lightweight and compact hexagonal reconfigurable lower‐limb SRL system is proposed to assist human locomotion in daily activities, including walking, crouching, and stair climbing. To adapt to multiple scenarios, the hexagonal mechanism can be adjusted to different configurations including convex hexagonal configuration, pentagonal configuration, and concave hexagonal configuration. To achieve more optimized performance of different configurations, the optimization for identifying the optimal dimensions of each link was carried out. Subsequently, the detailed design methodology and specifics are presented. Finally, the load and wearing performance experiments were evaluated. The experiment results demonstrated that the tested maximum load in different configurations exceeded 90% of the simulated value and the entire equipment has a good wearing adaptability. This study may inspire the design of other lower‐limb SRLs and provide efficient solutions for stable support assistance in various scenarios.

A contextual multi-task neural approach to medication and adverse events identification from clinical text

Effective wide-scale pharmacovigilance calls for accurate named entity recognition (NER) of medication entities such as drugs, dosages, reasons, and adverse drug events (ADE) from clinical text. The scarcity of adverse event annotations and underlying semantic ambiguities make accurate scope identification challenging. The current research explores integrating contextualized language models and multi-task learning from diverse clinical NER datasets to mitigate this challenge. We propose a novel multi-task adaptation method to refine the embeddings generated by the Bidirectional Encoder Representations from Transformers (BERT) language model to improve inter-task knowledge sharing. We integrated the adapted BERT model into a unique hierarchical multi-task neural network comprised of the medication and auxiliary clinical NER tasks. We validated the model using two different versions of BERT on diverse well-studied clinical tasks: Medication and ADE (n2c2 2018/n2c2 2009), Clinical Concepts (n2c2 2010/n2c2 2012), Disorders (ShAReCLEF 2013). Overall medication extraction performance enhanced by up to +1.19 F1 (n2c2 2018) while generalization enhanced by +5.38 F1 (n2c2 2009) as compared to standalone BERT baselines. ADE recognition enhanced significantly (McNemar’s test), out-performing prior baselines. Similar benefits were observed on the auxiliary clinical and disorder tasks. We demonstrate that combining multi-dataset BERT adaptation and multi-task learning out-performs prior medication extraction methods without requiring additional features, newer training data, or ensembling. Taken together, the study contributes an initial case study towards integrating diverse clinical datasets in an end-to-end NER model for clinical decision support.

Multitask Diffusion Adaptation Over Networks With Common Latent Representations

Online learning with streaming data in a distributed and collaborative manner can be useful in a wide range of applications. This topic has been receiving considerable attention in recent years with emphasis on both single-task and multitask scenarios. In single-task adaptation, agents cooperate to track an objective of common interest, while in multitask adaptation agents track multiple objectives simultaneously. Regularization is one useful technique to promote and exploit similarity among tasks in the latter scenario. This work examines an alternative way to model relations among tasks by assuming that they all share a common latent feature representation. As a result, a new multitask learning formulation is presented and algorithms are developed for its solution in a distributed online manner. We present a unified framework to analyze the mean-square-error performance of the adaptive strategies, and conduct simulations to illustrate the theoretical findings and potential applications.