In this paper, we introduce a novel visual representation learning which relies on a handful
of adaptively learned tokens, and which is applicable to both image and video …

We present a simple approach which can turn a ViT encoder into an efficient video model,
which can seamlessly work with both image and video inputs. By sparsely sampling the …

Video action recognition is one of the representative tasks for video understanding. Over the
last decade, we have witnessed great advancements in video action recognition thanks to …

In this paper, we introduce a novel visual representation learning which relies on a handful
of adaptively learned tokens, and which is applicable to both image and video …

Human action recognition (HAR) in RGB-D videos has been widely investigated since the
release of affordable depth sensors. Currently, unimodal approaches (eg, skeleton-based …

In vision-based action recognition, spatio-temporal features from different modalities are
used for recognizing activities. Temporal modeling is a long challenge of action recognition …

We present a cross-modal Transformer-based framework, which jointly encodes video data
and text labels for zero-shot action recognition (ZSAR). Our model employs a conceptually …

We present 4D-Net, a 3D object detection approach, which utilizes 3D Point Cloud and RGB
sensing information, both in time. We are able to incorporate the 4D information by …

Gesture recognition has attracted considerable attention owing to its great potential in
applications. Although the great progress has been made recently in multi-modal learning …

Many attempts have been made towards combining RGB and 3D poses for the recognition
of Activities of Daily Living (ADL). ADL may look very similar and often necessitate to model …