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Action prediction is to recognize the class label of an ongoing activity when only a part of it is observed. In this paper, we focus on online action prediction in streaming 3D skeleton sequences. A dilated convolutional network is introduced to model the motion dynamics in temporal dimension via a sliding window over the temporal axis. Since there are significant temporal scale variations in the observed part of the ongoing action at different time steps, a novel window scale selection method is proposed to make our network focus on the performed part of the ongoing action and try to suppress the possible incoming interference from the previous actions at each step. An activation sharing scheme is also proposed to handle the overlapping computations among the adjacent time steps, which enables our framework to run more efficiently. Moreover, to enhance the performance of our framework for action prediction with the skeletal input data, a hierarchy of dilated tree convolutions are also designed to learn the multi-level structured semantic representations over the skeleton joints at each frame. Our proposed approach is evaluated on four challenging datasets. The extensive experiments demonstrate the effectiveness of our method for skeleton-based online action prediction. [ABSTRACT FROM AUTHOR]

In this paper, a feature boosting network is proposed for estimating 3D hand pose and 3D body pose from a single RGB image. In this method, the features learned by the convolutional layers are boosted with a new long short-term dependence-aware (LSTD) module, which enables the intermediate convolutional feature maps to perceive the graphical long short-term dependency among different hand (or body) parts using the designed Graphical ConvLSTM. Learning a set of features that are reliable and discriminatively representative of the pose of a hand (or body) part is difficult due to the ambiguities, texture and illumination variation, and self-occlusion in the real application of 3D pose estimation. To improve the reliability of the features for representing each body part and enhance the LSTD module, we further introduce a context consistency gate (CCG) in this paper, with which the convolutional feature maps are modulated according to their consistency with the context representations. We evaluate the proposed method on challenging benchmark datasets for 3D hand pose estimation and 3D full body pose estimation. Experimental results show the effectiveness of our method that achieves state-of-the-art performance on both of the tasks. [ABSTRACT FROM AUTHOR]

Recognizing actions in videos is not a trivial task because video is an information-intensive media and includes multiple modalities. Moreover, on each modality, an action may only appear at some spatial regions, or only part of the temporal video segments may contain the action. A valid question is how to locate the attended spatial areas and selective video segments for action recognition. In this paper, we devise a general attention neural cell, called AttCell, that estimates the attention probability not only at each spatial location but also for each video segment in a temporal sequence. With AttCell, a unified Spatio-Temporal Attention Networks (STAN) is proposed in the context of multiple modalities. Specifically, STAN extracts the feature map of one convolutional layer as the local descriptors on each modality and pools the extracted descriptors with the spatial attention measured by AttCell as a representation of each segment. Then, we concatenate the representation on each modality to seek a consensus on the temporal attention, a priori, to holistically fuse the combined representation of video segments to the video representation for recognition. Our model differs from conventional deep networks, which focus on the attention mechanism, because our temporal attention provides a principled and global guidance across different modalities and video segments. Extensive experiments are conducted on four public datasets; UCF101, CCV, THUMOS14, and Sports-1M; our STAN consistently achieves superior results over several state-of-the-art techniques. More remarkably, we validate and demonstrate the effectiveness of our proposal when capitalizing on the different number of modalities. [ABSTRACT FROM AUTHOR]

Human action recognition in 3D skeleton sequences has attracted a lot of research attention. Recently, long short-term memory (LSTM) networks have shown promising performance in this task due to their strengths in modeling the dependencies and dynamics in sequential data. As not all skeletal joints are informative for action recognition, and the irrelevant joints often bring noise which can degrade the performance, we need to pay more attention to the informative ones. However, the original LSTM network does not have explicit attention ability. In this paper, we propose a new class of LSTM network, global context-aware attention LSTM, for skeleton-based action recognition, which is capable of selectively focusing on the informative joints in each frame by using a global context memory cell. To further improve the attention capability, we also introduce a recurrent attention mechanism, with which the attention performance of our network can be enhanced progressively. Besides, a two-stream framework, which leverages coarse-grained attention and fine-grained attention, is also introduced. The proposed method achieves state-of-the-art performance on five challenging datasets for skeleton-based action recognition. [ABSTRACT FROM PUBLISHER]

Visual patterns, i.e., high-order combinations of visual words, contributes to a discriminative abstraction of the high-dimensional bag-of-words image representation. However, the existing visual patterns are built upon the 2D photographic concurrences of visual words, which is ill-posed comparing with their real-world 3D concurrences, since the words from different objects or different depth might be incorrectly bound into an identical pattern. On the other hand, designing compact descriptors from the mined patterns is left open. To address both issues, in this paper, we propose a novel compact bag-of-patterns (CBoPs) descriptor with an application to low bit rate mobile landmark search. First, to overcome the ill-posed 2D photographic configuration, we build up a 3D point cloud from the reference images of each landmark, therefore more accurate pattern candidates can be extracted from the 3D concurrences of visual words. A novel gravity distance metric is then proposed to mine discriminative visual patterns. Second, we come up with compact image description by introducing a CBoPs descriptor. CBoP is figured out by sparse coding over the mined visual patterns, which maximally reconstructs the original bag-of-words histogram with a minimum coding length. We developed a low bit rate mobile landmark search prototype, in which CBoP descriptor is directly extracted and sent from the mobile end to reduce the query delivery latency. The CBoP performance is quantized in several large-scale benchmarks with comparisons to the state-of-the-art compact descriptors, topic features, and hashing descriptors. We have reported comparable accuracy to the million-scale bag-of-words histogram over the million scale visual words, with high descriptor compression rate (approximately 100-bits) than the state-of-the-art bag-of-words compression scheme. [ABSTRACT FROM AUTHOR]