Learning to Transfer: Transferring Latent Task Structures and Its Application to Person-Specific Facial Action Unit Detection

By Timur R. Almaev, Brais Martínez, Michel François Valstar. 2015

42 citations recorded by the source corpus.

In this article we explore the problem of constructing person-specific models for the detection of facial Action Units (AUs), addressing the problem from the point of view of Transfer Learning and Multi-Task Learning. Our starting point is the fact that some expressions, such as smiles, are very easily elicited, annotated, and automatically detected, while others are much harder to elicit and to annotate. We thus consider a novel problem: all AU models for the target subject are to be learnt using person-specific annotated data for a reference AU (AU12 in our case), and no data or little data regarding the target AU. In order to design such a model, we propose a novel Multi-Task Learning and the associated Transfer Learning framework, in which we consider both relations across subjects and AUs. That is to say, we consider a tensor structure among the tasks. Our approach hinges on learning the latent relations among tasks using one single reference AU, and then transferring these latent relations to other AUs. We show that we are able to effectively make use of the annotated data for AU12 when learning other person-specific AU models, even in the absence of data for the target task. Finally, we show the excellent performance of our method when small amounts of annotated data for the target tasks are made available.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Signal Processing

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Citations in the corpus

A Survey on Multi-Task Learning (2017, methodology): “In [162], the Go-MTL method [58] is generalized to the multilinear multi-task setting [107], [108] for person-specific facial action unit prediction, where each facial action unit is treated as a task and in each task, a function is learned for a person.”
Attend to You: Personalized Image Captioning with Context Sequence Memory Networks (2017, methodology): “Via max-pooling, each cim,t is reduced from (300 × [47, 46, 45]) to (300 × [1, 1, 1]).”
Automatic Analysis of Facial Actions: A Survey (2019, background): “Alternatively, [3] built upon a MTL algorithm capable of estimating tasks relatedness.”