Many believe that the successes of deep learning on image understanding problems can be
replicated in the realm of video understanding. However, due to the scale and temporal …

Attentive video modeling is essential for action recognition in unconstrained videos due to
their rich yet redundant information over space and time. However, introducing attention in a …

Addressing the dual challenges of local redundancy and global dependencies in video
understanding, this work innovatively adapts the Mamba to the video domain. The proposed …

Although neural networks excel in video action recognition tasks their" black-box" nature
makes it challenging to understand the rationale behind their decisions. Recent approaches …

Despite the recent success of end-to-end learned representations, hand-crafted optical flow
features are still widely used in video analysis tasks. To fill this gap, we propose TVNet, a …

Motion plays a crucial role in understanding videos and most state-of-the-art neural models
for video classification incorporate motion information typically using optical flows extracted …

Accurate video understanding involves reasoning about the relationships between actors,
objects and their environment, often over long temporal intervals. In this paper, we propose …

In this paper we study 3D convolutional networks for video understanding tasks. Our starting
point is the stateof-the-art I3D model of [3], which “inflates” all the 2D filters of the Inception …

Video understanding is a research hotspot of computer vision and significant progress has
been made on video action recognition recently. However, the semantics information …

Spatial convolutions are widely used in numerous deep video models. It fundamentally
assumes spatio-temporal invariance, ie, using shared weights for every location in different …