Feature Pyramid Network for Multi-task Affective Analysis

Abstract

Affective Analysis is not a single task, and the valencearousal value, expression class, and action unit can be predicted at the same time. Previous researches did not pay enough attention to the entanglement and hierarchical relation of these three facial attributes. We propose a novel model named feature pyramid networks for multi-task affect analysis. The hierarchical features are extracted to predict three labels and we apply a teacher-student training strategy to learn from pretrained single-task models. Extensive experiment results demonstrate the proposed model outperforms other models. This is a submission to The 2nd Workshop and Competition on Affective Behavior Analysis in the wild (ABAW). The code and model are available for research purposes at this link.

Affect Expression Behaviour Analysis in the Wild using Consensual Collaborative Training

“…Since, we have trained model for single task and not used any of audio and video features, so performance is not as good as teams using multi-task learning with video features. [27] 0.29 0.6491 0.4082 NTUA-CVSP [28] 0.3367 0.6418 0.4374 Morphoboid [29] 0.3511 0.668 0.4556 FLAB2021 [30] 0.4079 0.6729 0.4953 STAR [31] 0.4759 0.7321 0.5604 Maybe Next Time [32] 0.6046 0.7289 0.6456 CPIC-DIR2021 [33] 0.6834 0.7709 0.7123 Netease Fuxi Virtual Human [34] 0.763 0.8059 0.7777 Ours [18] 0.361 0.675 0.4646 Table 3 shows the influence of number of networks that are collaboratively trained in CCT. It can be observed that model with 3 networks performs the best in the presence of noise.…”

Face Identification Based on Active Facial Patches Using Multi-Task Cascaded Convolutional Networks

“…As a recent development, face recognition has become an integral part of social cognition and has been used in various requests, pedestrian tracking, and surveillance systems. There has been significant progress in facial recognition using DCNN [10].…”