Two-Stream Transformer Architecture for Long Video Understanding

Pure vision transformer architectures are highly effective for short video classification and action recognition tasks. However, due to the quadratic complexity of self attention and lack of inductive bias, transformers are resource intensive and suffer from data inefficiencies. Long form video understanding tasks amplify data and memory efficiency problems in transformers making current approaches unfeasible to implement on data or memory restricted domains. This paper introduces an efficient Spatio-Temporal Attention Network (STAN) which uses a two-stream transformer architecture to model dependencies between static image features and temporal contextual features. Our proposed approach can classify videos up to two minutes in length on a single GPU, is data efficient, and achieves SOTA performance on several long video understanding tasks

New Screen Media : Cinema / Art / Narrative

Editors Rieser and Zapp argue that the new media has brought with it innovations in screen narrative form that raise issues about the body, identity, authorship, and temporal and spatial construction. Texts by cultural theorists are juxtaposed with artists’ analyses of their own work. Providing an overview of the history and theory of narrative and the media, the book documents the unique forms new media narrative practices have taken. Includes an interactive DVD-ROM featuring works by 36 artists. Biographical notes on contributors. Glossary (5 p.), bibliography (6 p.) and index (8 p.). Circa 250 bibl. ref