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Removing the undesired reflections from images taken through the glass is of broad application to various computer vision tasks. Non-learning based methods utilize different handcrafted priors such as the separable sparse gradients caused by different levels of blurs, which often fail due to their limited description capability to the properties of real-world reflections. In this paper, we propose a network with the feature-sharing strategy to tackle this problem in a cooperative and unified framework, by integrating image context information and the multi-scale gradient information. To remove the strong reflections existed in some local regions, we propose a statistic loss by considering the gradient level statistics between the background and reflections. Our network is trained on a new dataset with 3250 reflection images taken under diverse real-world scenes. Experiments on a public benchmark dataset show that the proposed method performs favorably against state-of-the-art methods. [ABSTRACT FROM AUTHOR]

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]

The high similarities of different real-world vehicles and great diversities of the acquisition views pose grand challenges to vehicle re-identification (ReID), which traditionally maps the vehicle images into a high-dimensional embedding space for distance optimization, vehicle discrimination, and identification. To improve the discriminative capability and robustness of the ReID algorithm, we propose a novel end-to-end embedding adversarial learning network (EALN) that is capable of generating samples localized in the embedding space. Instead of selecting abundant hard negatives from the training set, which is extremely difficult if not impossible, with our embedding adversarial learning scheme, the automatically generated hard negative samples in the specified embedding space can greatly improve the capability of the network for discriminating similar vehicles. Moreover, the more challenging cross-view vehicle ReID problem, which requires the ReID algorithm to be robust with different query views, can also benefit from such a scheme based on the artificially generated cross-view samples. We demonstrate the promise of EALN through extensive experiments and show the effectiveness of hard negative and cross-view generation in facilitating vehicle ReID based on the comparisons with the state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

The advent of artificial intelligence and Internet of Things has led to the seamless transition turning the big data into the big knowledge. The deep learning models, which assimilate knowledge from large-scale data, can be regarded as an alternative but promising modality of knowledge for artificial intelligence services. Yet, the compression, storage, and communication of the deep learning models towards better knowledge services, especially over networks, pose a set of challenging problems on both industrial and academic realms. This paper presents the deep learning model communication paradigm based on multiple model compression, which greatly exploits the redundancy among multiple deep learning models in different application scenarios. We analyze the potential and demonstrate the promise of the compression strategy for deep learning model communication through a set of experiments. Moreover, the interoperability in deep learning model communication, which is enabled based on the standardization of compact deep learning model representation, is also discussed and envisioned. [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]