Facial expression recognition in the wild is challenging due to various unconstrained
conditions. Although existing facial expression classifiers have been almost perfect on …

The ability to reason about temporal and causal events from videos lies at the core of human
intelligence. Most video reasoning benchmarks, however, focus on pattern recognition from …

We marry two powerful ideas: deep representation learning for visual recognition and
language understanding, and symbolic program execution for reasoning. Our neural …

Multimodal representation learning, which aims to narrow the heterogeneity gap among
different modalities, plays an indispensable role in the utilization of ubiquitous multimodal …

Text-image cross-modal retrieval is a challenging task in the field of language and vision.
Most previous approaches independently embed images and sentences into a joint …

In order to answer semantically-complicated questions about an image, a Visual Question
Answering (VQA) model needs to fully understand the visual scene in the image, especially …

Vehicle re-identification is one of the core technologies of intelligent transportation systems,
and it is crucial for the construction of smart cities. With the rapid development of deep …

Learning effective fusion of multi-modality features is at the heart of visual question
answering. We propose a novel method of dynamically fuse multi-modal features with intra …

Vehicle re-identification (re-ID) has the huge potential to contribute to the intelligent video
surveillance. However, it suffers from challenges that different vehicle identities with a similar …

Dramatic progress has been witnessed in basic vision tasks involving low-level perception,
such as object recognition, detection, and tracking. Unfortunately, there is still enormous …