Recently, action recognition has achieved impressive performance, mainly due to the aid of deep convolutional neural networks and large datasets. Traditionally, most efforts in action recognition have focused on capturing motion information by dense optical flow, but optical flow extraction is very time-consuming. Moreover, prior arts seek to improve accuracy but neglect the part-whole relationship between objects in videos, which may be self-defeating and even deteriorate the performance of methods. To circumvent the above challenges, we present a novel collaborative multipath capsule network (CMCN) for action recognition. In particular, we propose a plug-and-play collaborative multipath block containing spatiotemporal, channel, and motion units, which are complementary and crucial information for action recognition. We exploit the interaction of these three units and selectively emphasize informative spatial-temporal motion to reduce the expensive computational costs. Subsequently, we explore a new capsule voting procedure to reduce the computation used in the capsule dynamic routing mechanism. The critical insight is that the same type of capsules simulates the same entity in different positions, and their voting results should be consistent. This strategy lessens the number of learning parameters that backward pass in the training process, and thus strengthens part-whole relationships in a video. Extensive experiments on multiple real-world datasets for action recognition demonstrate that our model significantly outperforms state-of-the-art models.

Weakly Supervised Semantic Segmentation with image-level annotation uses localization maps from the classifier to generate pseudo labels. However, such localization maps focus only on sparse salient object regions, it is difficult to generate high-quality segmentation labels, which deviates from the requirement of semantic segmentation. To address this issue, we propose a dual-aware domain mining and cross-aware supervision (DDMCAS) method for weakly-supervised semantic segmentation. Specifically, we propose a dual-aware domain mining (DDM) module consisting of graph-based global reasoning unit and salient-region extension controller, which produces dense localization maps by exploring object features in salient regions and adjacent non-salient regions simultaneously. In order to further bridge the gap between salient regions and adjacent non-salient regions to generate more refined localization maps, we propose a cross-aware supervision (CAS) strategy to recover missing parts of the target objects and enhance weak attention in adjacent non-salient regions, leading to pseudo labels of higher quality for training the segmentation network. Based on the generated pseudo-labels, extensive experiments on PASCAL VOC 2012 dataset demonstrate that our method outperforms state-of-the-art methods using image-level labels for weakly supervised semantic segmentation.

Recently, with the development of temporal knowledge graph technology, more and more Temporal Knowledge Graph Embedded (TKGE) models have been developed. The effectiveness of TKGE largely depends on the ability to model intrinsic relation patterns and capture specific information about entities and relations. However, existing approaches can capture only some of them with insufficient modeling capacity, and none has a “deep” architecture for modeling the entries in a quadruple at the same dimension. In this article, we propose a more powerful KGE framework named DuCape , which combines a dual quaternion and capsule network in modeling for the first time to make up for the defects of existing TKGE models. In dual quaternion vector space, the head entity learns a k -dimensional rigid transformation parametrized by relation and time, falling near its corresponding tail entity. Further, we employ the embeddings of entities, relations, and time trained from dual quaternion vector space as the input to capsule networks. Experimental results on several basic datasets show that the DuCape model constructed in this article is superior to existing state-of-the-art models.

While Graph Convolutional Networks (GCNs) have been extended to various fields of artificial intelligence with their powerful representation capabilities, recent studies have revealed that their ability to capture the part-whole structure of the graph is limited. Furthermore, though many GCNs variants have been proposed and obtained state-of-the-art results, they face the situation that much early information may be lost during the graph convolution step. To this end, we innovatively present an Graph Capsule Network with a Dual Adaptive Mechanism (DA-GCN) to tackle the above challenges. Specifically, this powerful mechanism is a dual-adaptive mechanism to capture the part-whole structure of the graph. One is an adaptive node interaction module to explore the potential relationship between interactive nodes. The other is an adaptive attention-based graph dynamic routing to select appropriate graph capsules, so that only favorable graph capsules are gathered and redundant graph capsules are restrained for better capturing the whole structure between graphs. Experiments demonstrate that our proposed algorithm has achieved the most advanced or competitive results on all datasets.