Recently, body part as an intuitive movement unit has received increasing attention in skeleton-based action. However, the part-level embedding is hard to be fully exploited, especially for fine-grained actions, as the body joints are aggregated into parts. To address this problem, we propose a novel transformer-based network (IIP-Transformer). Different from previous models that rely on specially designed partition method, our proposed IIPA mechanism which incorporates joint-level (intra-part) and part-level (inter-part) interactions simultaneously is the keypoint for IIP-Transformer to fully exploit part-level data, making considerable improvements in both coarse-grained and fine-grained action recognition. Ablation studies on three typical partition methods show that IIP-Transformer is a relatively general solution for part-level data and thus we could choose the simplest hand-craft partition embedding to significantly reduce computational complexity and model size. Besides, The proposed IIP-Transformer exceeds the state-of-the-art methods with much less computational complexity on NTU RGB+D, NTU RGB+D120 and NW-UCLA datasets.

Active learning is an algorithm that aims to minimize the labeling effort in model training by wisely choosing the key data for labeling in an iterative and alternately querying labeled data and training procedure. While most of studies focus on the query strategy such as selecting the informative instances for labeling in each run, some others consider the query on features, or the query on both of the features and instances simultaneously to build effective model more efficiently than before. A typical strategy of querying features can take care only the categorical features. In this work, we extend the possibility of querying numerical features as well. We consider a Vector Quantization (VQ) technique to transform the numerical features to categorical ones and then all the features, as well as instances can all be evaluated using a unified criterion in the feature/instance selection step. Moreover, we study multivariate VQ as well as univariate VQ to find the best approach for the qunatization before sending the numerical features to categorical ones. After all, we can have active learning that deals with the selection on informative instances and features at the same time. We need to point out that not all numerical features could be appropriate to become categorical ones in the aforementioned transformation. As in our evaluation, some data from medical analysis and others are perfect to be one of such active learning applications. Along this line, we study the difference between applying the proposed feature/instance combined active learning to various data sets. A set of different vector quantization methods shall also be examined to find the best setting for effective active learning experience.