Deep Weakly Supervised Domain Adaptation for Pain Localization in Videos
| Autor: | Eric Granger, R. Gnana Praveen, Patrick Cardinal |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Facial expression
Contextual image classification Computer science business.industry Deep learning Frame (networking) 020206 networking & telecommunications Pattern recognition 02 engineering and technology Facial recognition system Domain (software engineering) Pain assessment 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Hidden Markov model business |
| Zdroj: | FG |
| DOI: | 10.1109/fg47880.2020.00139 |
| Popis: | Automatic pain assessment has an important potential diagnostic value for populations that are incapable of articulating their pain experiences. As one of the dominating nonverbal channels for eliciting pain expression events, facial expressions has been widely investigated for estimating the pain intensity of individual. However, using state-of-the-art deep learning (DL) models in real-world pain estimation applications poses several challenges related to the subjective variations of facial expressions, operational capture conditions, and lack of representative training videos with labels. Given the cost of annotating intensity levels for every video frame, we propose a weakly-supervised domain adaptation (WSDA) technique that allows for training 3D CNNs for spatiotemporal pain intensity estimation using weakly labeled videos, where labels are provided on a periodic basis. In particular, WSDA integrates multiple instance learning into an adversarial deep domain adaptation framework to train an Inflated 3D-CNN (I3D) model such that it can accurately estimate pain intensities in the target operational domain. The training process relies on weak target loss, along with domain loss and source loss for domain adaptation of the I3D model. Experimental results obtained using labeled source domain RECOLA videos and weakly-labeled target domain UNBC-McMaster videos indicate that the proposed deep WSDA approach can achieve significantly higher level of sequence (bag)-level and frame (instance)-level pain localization accuracy than related state-of-the-art approaches. |
| Databáze: | OpenAIRE |
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