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We propose a novel benchmark for cross-view knowledge transfer of dense videocaptioning, adapting models from web instructional videos with exocentric viewsto an egocentric view. While dense video captioning (predicting time segmentsand their captions) is primarily studied with exocentric videos (e.g.,YouCook2), benchmarks with egocentric videos are restricted due to datascarcity. To overcome the limited video availability, transferring knowledgefrom abundant exocentric web videos is demanded as a practical approach.However, learning the correspondence between exocentric and egocentric views isdifficult due to their dynamic view changes. The web videos contain mixed viewsfocusing on either human body actions or close-up hand-object interactions,while the egocentric view is constantly shifting as the camera wearer moves.This necessitates the in-depth study of cross-view transfer under complex viewchanges. In this work, we first create a real-life egocentric dataset (EgoYC2)whose captions are shared with YouCook2, enabling transfer learning betweenthese datasets assuming their ground-truth is accessible. To bridge the viewgaps, we propose a view-invariant learning method using adversarial training inboth the pre-training and fine-tuning stages. While the pre-training isdesigned to learn invariant features against the mixed views in the web videos,the view-invariant fine-tuning further mitigates the view gaps between bothdatasets. We validate our proposed method by studying how effectively itovercomes the view change problem and efficiently transfers the knowledge tothe egocentric domain. Our benchmark pushes the study of the cross-viewtransfer into a new task domain of dense video captioning and will envisionmethodologies to describe egocentric videos in natural language.

Exo2EgoDVC: Dense Video Captioning of Egocentric Procedural Activities Using Web Instructional Videos