Generative adversarial networks (GANs) present a way to learn deep representations
without extensively annotated training data. These networks achieve learning through …

Anomaly detection of machine tools plays a vital role in the machinery industry to sustain
efficient operation and avoid catastrophic failures. Compared to traditional machine learning …

The binary neural network, largely saving the storage and computation, serves as a
promising technique for deploying deep models on resource-limited devices. However, the …

Person re-identification (re-ID) and attribute recognition share a common target at learning
pedestrian descriptions. Their difference consists in the granularity. Most existing re-ID …

Cross-modality person re-identification between the thermal and visible domains is
extremely important for night-time surveillance applications. Existing works in this filed …

Multiview data clustering attracts more attention than their single-view counterparts due to
the fact that leveraging multiple independent and complementary information from multiview …

The proliferation of social media in communication and information dissemination has made
it an ideal platform for spreading rumors. Automatically debunking rumors at their stage of …

Video-based person re-identification (re-id) is a central application in surveillance systems
with a significant concern in security. Matching persons across disjoint camera views in their …

In this paper, we propose a novel deep generative approach to cross-modal retrieval to
learn hash functions in the absence of paired training samples through the cycle consistency …

Fine-grained visual recognition is an important problem in pattern recognition applications.
However, it is a challenging task due to the subtle interclass difference and large intraclass …