Learning Social Relation Traits from Face Images

Social relation defines the association, e.g, warm, friendliness, and dominance, between two or more people. Motivated by psychological studies, we investigate if such fine-grained and high-level relation traits can be characterised and quantified from face images in the wild. To address this challenging problem we propose a deep model that learns a rich face representation to capture gender, expression, head pose, and age-related attributes, and then performs pairwise-face reasoning for relation prediction. To learn from heterogeneous attribute sources, we formulate a new network architecture with a bridging layer to leverage the inherent correspondences among these datasets. It can also cope with missing target attribute labels. Extensive experiments show that our approach is effective for fine-grained social relation learning in images and videos.Comment: To appear in International Conference on Computer Vision (ICCV) 201

Quantifying Facial Age by Posterior of Age Comparisons

We introduce a novel approach for annotating large quantity of in-the-wild facial images with high-quality posterior age distribution as labels. Each posterior provides a probability distribution of estimated ages for a face. Our approach is motivated by observations that it is easier to distinguish who is the older of two people than to determine the person's actual age. Given a reference database with samples of known ages and a dataset to label, we can transfer reliable annotations from the former to the latter via human-in-the-loop comparisons. We show an effective way to transform such comparisons to posterior via fully-connected and SoftMax layers, so as to permit end-to-end training in a deep network. Thanks to the efficient and effective annotation approach, we collect a new large-scale facial age dataset, dubbed `MegaAge', which consists of 41,941 images. Data can be downloaded from our project page mmlab.ie.cuhk.edu.hk/projects/MegaAge and github.com/zyx2012/Age_estimation_BMVC2017. With the dataset, we train a network that jointly performs ordinal hyperplane classification and posterior distribution learning. Our approach achieves state-of-the-art results on popular benchmarks such as MORPH2, Adience, and the newly proposed MegaAge.Comment: To appear on BMVC 2017 (oral) revised versio

On-Device Domain Generalization

We present a systematic study of domain generalization (DG) for tiny neural networks. This problem is critical to on-device machine learning applications but has been overlooked in the literature where research has been merely focused on large models. Tiny neural networks have much fewer parameters and lower complexity and therefore should not be trained the same way as their large counterparts for DG applications. By conducting extensive experiments, we find that knowledge distillation (KD), a well-known technique for model compression, is much better for tackling the on-device DG problem than conventional DG methods. Another interesting observation is that the teacher-student gap on out-of-distribution data is bigger than that on in-distribution data, which highlights the capacity mismatch issue as well as the shortcoming of KD. We further propose a method called out-of-distribution knowledge distillation (OKD) where the idea is to teach the student how the teacher handles out-of-distribution data synthesized via disruptive data augmentation. Without adding any extra parameter to the model -- hence keeping the deployment cost unchanged -- OKD significantly improves DG performance for tiny neural networks in a variety of on-device DG scenarios for image and speech applications. We also contribute a scalable approach for synthesizing visual domain shifts, along with a new suite of DG datasets to complement existing testbeds.Comment: Preprin

Consistent alleviation of abiotic stress with silicon addition: a meta-analysis

1. Hundreds of single species studies have demonstrated the facility of silicon (Si) to alleviate diverse abiotic stresses in plants. Understanding of the mechanisms of Si-mediated stress alleviation is progressing, and several reviews have brought information together. A quantitative assessment of the alleviative capacity of Si, however, which could elucidate plant Si function more broadly, was lacking. 2. We combined the results of 145 experiments, predominantly on agricultural species, in a meta-analysis to statistically assess the responses of stressed plants to Si supply across multiple plant families and abiotic stresses. We interrogated our database to determine whether stressed plants increased in dry mass and net assimilation rate, oxidative stress markers were reduced, antioxidant responses were increased and whether element uptake showed consistent changes when supplied with Si. 3. We demonstrated that across plant families and stress types, Si increases dry weight, assimilation rate and chlorophyll biosynthesis and alleviates oxidative damage in stressed plants. In general, results indicated that plant family (as a proxy for accumulator type) and stress type had significant explanatory power for variation in responses. The consistent reduction in oxidative damage was not mirrored by consistent increases in antioxidant production, indicative of the several different stress alleviation mechanisms in which Si is involved. Silicon addition increased K in shoots, decreased As and Cd in roots and Na and Cd in shoots. Silicon addition did not affect Al, Ca or Mn concentration in shoots and roots of stressed plants. Plants had significantly lower concentrations of Si accumulated in shoots but not in roots when stressed. 4. Meta-analyses showed consistent alleviation by Si of oxidative damage caused by a range of abiotic stresses across diverse species. Our findings indicate that Si is likely to be a useful fertilizer for many crops facing a spectrum of abiotic stresses. Similarities in responses across families provide strong support for a role of Si in the alleviation of abiotic stress in natural systems, where it has barely been explored. We suggest this role may become more important under a changing climate and more experiments using non-agricultural species are now needed