Recent works using artificial neural networks based on word distributed representation greatly boost the performance of various natural language learning tasks, especially question answering. Though, they also carry along with some attendant problems, such as corpus selection for embedding learning, dictionary transformation for different learning tasks, etc. In this paper, we propose to straightforwardly model sentences by means of character sequences, and then utilize convolutional neural networks to integrate character embedding learning together with point-wise answer selection training. Compared with deep models pre-trained on word embedding (WE) strategy, our character-sequential representation (CSR) based method shows a much simpler procedure and more stable performance across different benchmarks. Extensive experiments on two benchmark answer selection datasets exhibit the competitive performance compared with the state-of-the-art methods.

Still-to-video (S2V) face recognition has recently attracted attention from researchers because of its great applications in real-world scenarios. In S2V FR, still images are usually of high quality, captured from cooperative users under controlled environment, such as mugshots, while video clips may be acquired with low resolutions and low quality, from non-cooperative users under uncontrolled environment. Because of those significant differences, we interpret the S2V FR as a heterogeneous matching problem, and propose an approach aiming at building multiple “bridges” between those two heterogeneous face modalities. Considering the unbalanced distributions and large diversities between two modalities, we propose to exploit a Grassmann manifold learning method to construct subspaces in between to find connections (or transitions) between the still images and video clips. Multiple geodesic flows are generated connecting the subspace of still images and the clustered subspace centers of videos, which are representative and robust to characterize the relationship between still images and video frames. Extensive experiments are conducted on two large scale benchmark databases, COX-S2V and PaSC, with different recognition tasks: face identification and verification. The experimental results show that the proposed approach outperforms the state-of-the-art methods under the same experimental settings.

We address the problem of face video retrieval in TV-series, which searches video clips based on the presence of specific character, given one face track of his/her. This is tremendously challenging because on one hand, faces in TV-series are captured in largely uncontrolled conditions with complex appearance variations, and on the other hand, retrieval task typically needs efficient representation with low time and space complexity. To handle this problem, we propose a compact and discriminative representation for the huge body of video data, named compact video code (CVC). Our method first models the face track by its sample (i.e., frame) covariance matrix to capture the video data variations in a statistical manner. To incorporate discriminative information and obtain more compact video signature suitable for retrieval, the high-dimensional covariance representation is further encoded as a much lower dimensional binary vector, which finally yields the proposed CVC. Specifically, each bit of the code, i.e., each dimension of the binary vector, is produced via supervised learning in a max margin framework, which aims to make a balance between the discriminability and stability of the code. Besides, we further extend the descriptive granularity of covariance matrix from traditional pixel-level to more general patch-level, and proceed to propose a novel hierarchical video representation named spatial pyramid covariance along with a fast calculation method. Face retrieval experiments on two challenging TV-series video databases, i.e., the Big Bang Theory and Prison Break, demonstrate the competitiveness of the proposed CVC over the state-of-the-art retrieval methods. In addition, as a general video matching algorithm, CVC is also evaluated in traditional video face recognition task on a standard Internet database, i.e., YouTube Celebrities, showing its quite promising performance by using an extremely compact code with only 128 bits.

Retrieving videos of a specific person given his/her face image as query becomes more and more appealing for applications like smart movie fast-forwards and suspect searching. It also forms an interesting but challenging computer vision task, as the visual data to match, i.e., still image and video clip are usually represented quite differently. Typically, face image is represented as point (i.e., vector) in Euclidean space, while video clip is seemingly modeled as a point (e.g., covariance matrix) on some particular Riemannian manifold in the light of its recent promising success. It thus incurs a new hashing-based retrieval problem of matching two heterogeneous representations, respectively in Euclidean space and Riemannian manifold. This work makes the first attempt to embed the two heterogeneous spaces into a common discriminant Hamming space. Specifically, we propose Hashing across Euclidean space and Riemannian manifold (HER) by deriving a unified framework to firstly embed the two spaces into corresponding reproducing kernel Hilbert spaces, and then iteratively optimize the intra- and inter-space Hamming distances in a max-margin framework to learn the hash functions for the two spaces. Extensive experiments demonstrate the impressive superiority of our method over the state-of-the-art competitive hash learning methods.

We address the challenging large-scale content-based face image retrieval problem, intended as searching images based on the presence of specific subject, given one face image of him/her. To this end, one natural demand is a supervised binary code learning method. While the learned codes might be discriminating, people often have a further expectation that whether some semantic message (e.g., visual attributes) can be read from the human-incomprehensible codes. For this purpose, we propose a novel binary code learning framework by jointly encoding identity discriminability and a number of facial attributes into unified binary code. In this way, the learned binary codes can be applied to not only fine-grained face image retrieval, but also facial attributes prediction, which is the very innovation of this work, just like killing two birds with one stone. To evaluate the effectiveness of the proposed method, extensive experiments are conducted on a new purified large-scale web celebrity database, named CFW 60K, with abundant manual identity and attributes annotation, and experimental results exhibit the superiority of our method over state-of-the-art.