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Video captioning is a joint task of computer vision and natural language processing, which aims to describe the video content using several natural language sentences. Nowadays, most methods cast this task as a mapping problem, which learns a mapping from visual features to natural language and generates captions directly from videos. However, the underlying challenge of video captioning, i.e., sequence to sequence mapping across the different domains, is still not well handled. To address these problems, we introduce the polishing mechanism in an attempt to mimic human polishing process and propose a generate-and-polish framework for video captioning. In this paper, we propose a two-step transformer based polishing network (TSTPN) consisting of two sub-modules: the generation-module is to generate the caption candidate and the polishing-module is to gradually refine the generated candidate. Specifically, the candidate provides a global information of the visual contents in a semantically-meaningful order, where it is firstly considered as a semantic intersnubber to bridge the semantic gap between the text and video, with the cross-modal attention mechanism for better cross-modal modeling; and it secondly provides a global planning ability to maintain the semantic consistency and fluency of the whole sentence for better sequence mapping. In experiments, we present adequate evaluations to show that the proposed TSTPN achieves the comparable and even better performance than the state-of-the-art methods on the benchmark datasets. [ABSTRACT FROM AUTHOR]

Labanotation is a symbolic recording system for human movements, and also a powerful tool for protecting and spreading folk dances and other performing arts. State-of-the-art automatic Labanotation uses end-to-end methods with sequence-based skeleton representation, which cannot capture the relationship between joints and bones in the skeleton for accurate descriptions of continuous lower limb movements such as dance steps. In this paper, we propose a novel double-stream fusion method of directed graph neural networks (DGNN), combined with connectionist temporal classification (CTC), namely DFGNN-CTC, for sequential fine-grained motion recognition, such as the Labanotation generation of unsegmented dance movement. First, we extract double-stream directed graph feature, employing an orientation-normalized directed acyclic graph (ON-DAG) and an orientation-normalized temporal directed acyclic graph (ON-TDAG), to jointly model spatiotemporal properties of movement recorded in motion capture data. Then, we design a CTC-based fusion-pooling module to fuse the spatial and temporal streams encoded by two DGNNs. It concatenates and fuses the two streams to generate discriminative descriptions of each time step, and concentrates them to make per-time-step predictions of Laban gesture type, from which the CTC searches the optimal Laban symbol sequence, corresponding to elemental motions composing the movement. In this way, the new method enables much finer discrimination for similar Laban gestures with subtle differences in spatial and temporal properties through joint contextual spatiotemporal modeling so that it achieves much superior performance in continuous Labanotation generation to existing methods, which only have single-stream analysis either spatially or temporally. The experiments on two Labanotation-labelled motion capture datasets demonstrate the effectiveness of the components in the proposed method and its superiority comparing with the state-of-the-art methods, especially for lower limb movements. [ABSTRACT FROM AUTHOR]

Human activity localization aims at recognizing contents and detecting locations of activities in video sequences. With an increasing number of untrimmed video data, traditional activity localization methods always suffer from two major limitations. First, detailed annotations are needed in most existing methods, i.e., bounding-box annotations in every frame, which are both expensive and time consuming. Second, the search space is too large for 3D activity localization, which requires generating a large number of proposals. In this paper, we propose a unified deep Q-network with weak reward and weak loss (DWRLQN) to address the two problems. Certain weak knowledge and weak constraints involving the temporal dynamics of human activity are incorporated into a deep reinforcement learning framework under sparse spatial supervision, where we assume that only a portion of frames are annotated in each video sequence. Experiments on UCF-Sports, UCF-101 and sub-JHMDB demonstrate that our proposed model achieves promising performance by only utilizing a very small number of proposals. More importantly, our DWRLQN trained with partial annotations and weak information even outperforms fully supervised methods. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) scene classification plays an important role in the field of earth observation. With the rapid development of the RS techniques, a large number of RS scene images are available. As manually labeling large-scale RS scene images is both labor and time consuming, when a new unlabeled data set is obtained, how to use the existing labeled data sets to classify the new unlabeled images is an important research direction. Different RS scene image data sets may be taken from different type of sensors, and the images may vary from imaging modalities, spatial resolutions, and image scales, so the distribution discrepancy exists among different image data sets. As a result, simply applying convolutional neural networks (CNN) trained on source domain cannot accurately classify the images on target domain. Domain adaptation (DA) can be helpful to solve this problem. In this letter, we design a subspace alignment (SA) and CNN-based framework to solve the DA problem in RS scene image classification. A new SA layer is proposed and added into CNN models for DA, which could align the source and target domains in some feature subspace. Fine-tuning the modified CNN model with the added SA layer makes the CNN model adapt to the aligned feature subspace and helps to relieve the domain distribution discrepancy. The experiments conducted on two public data sets show that adding the SA layer into CNN improves the scene classification on the target domain. [ABSTRACT FROM AUTHOR]

Real-time object tracking has wide applications in time-critical multimedia processing areas such as motion analysis and human–computer interaction. It remains a hard problem to balance between accuracy and speed. In this paper, we present a fast real-time context-based visual tracking algorithm with a new saliency prior context (SPC) model. Based on the probability formulation, the tracking problem is solved by sequentially maximizing the computed confidence map of target location in each video frame. To handle the various cases of feature distributions generated from different targets and their contexts, we exploit low-level features as well as fast spectral analysis for saliency to build a new prior context model. Then, based on this model and a spatial context model learned online, a confidence map is computed and the target location is estimated. In addition, under this framework, the tracking procedure can be accelerated by the fast Fourier transform. Therefore, the new method generally achieves a real-time running speed. Extensive experiments show that our tracking algorithm based on the proposed SPC model achieves real-time computation efficiency with overall best performance comparing with other state-of-the-art methods. [ABSTRACT FROM PUBLISHER]