A semantic understanding of road traffic can help people understand road traffic flow
situations and emergencies more accurately and provide a more accurate basis for anomaly …

Abstract We propose Anticipative Video Transformer (AVT), an end-to-end attention-based
video modeling architecture that attends to the previously observed video in order to …

Occlusion and pose variations, which can change facial appearance significantly, are two
major obstacles for automatic Facial Expression Recognition (FER). Though automatic FER …

The explosive growth in video streaming gives rise to challenges on performing video
understanding at high accuracy and low computation cost. Conventional 2D CNNs are …

Abstract Unsupervised Domain Adaptive (UDA) person re-identification (ReID) aims at
adapting the model trained on a labeled source-domain dataset to a target-domain dataset …

Abstract We introduce the Action Transformer model for recognizing and localizing human
actions in video clips. We repurpose a Transformer-style architecture to aggregate features …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

We focus on multi-modal fusion for egocentric action recognition, and propose a novel
architecture for multi-modal temporal-binding, ie the combination of modalities within a …

In the face of the video data deluge, today's expensive clip-level classifiers are increasingly
impractical. We propose a framework for efficient action recognition in untrimmed video that …

We present Audiovisual SlowFast Networks, an architecture for integrated audiovisual
perception. AVSlowFast has Slow and Fast visual pathways that are deeply integrated with a …