The re-kindled fascination in machine learning (ML), observed over the last few decades,
has also percolated into natural sciences and engineering. ML algorithms are now used in …

To understand the world, we humans constantly need to relate the present to the past, and
put events in context. In this paper, we enable existing video models to do the same. We …

How do humans recognize the action" opening a book"? We argue that there are two
important cues: modeling temporal shape dynamics and modeling functional relationships …

Video question answering (VideoQA) is challenging as it requires modeling capacity to distill
dynamic visual artifacts and distant relations and to associate them with linguistic concepts …

Current methods for video analysis often extract frame-level features using pre-trained
convolutional neural networks (CNNs). Such features are then aggregated over time eg, by …

Video captioning, in essential, is a complex natural process, which is affected by various
uncertainties stemming from video content, subjective judgment, and so on. In this paper, we …

Future prediction, especially in long-range videos, requires reasoning from current and past
observations. In this work, we address questions of temporal extent, scaling, and level of …

Nowadays, big data systems are being widely adopted by many domains for offering
effective data solutions, such as manufacturing, healthcare, education, and media. Big data …

Our objective in this work is fine-grained classification of actions in untrimmed videos, where
the actions may be temporally extended or may span only a few frames of the video. We cast …

Recently, there has been a lot of interest in building compact models for video classification
which have a small memory footprint (< 1 GB). While these models are compact, they …