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Found 5 papers, 5 papers with code
GenHowTo: Learning to Generate Actions and State Transformations from Instructional Videos
We address the task of generating temporally consistent and physically plausible images of actions and object state transformations.
Multi-Task Learning of Object State Changes from Uncurated Videos
We aim to learn to temporally localize object state changes and the corresponding state-modifying actions by observing people interacting with objects in long uncurated web videos.
Look for the Change: Learning Object States and State-Modifying Actions from Untrimmed Web Videos
In this paper, we seek to temporally localize object states (e. g. "empty" and "full" cup) together with the corresponding state-modifying actions ("pouring coffee") in long uncurated videos with minimal supervision.
TransNet V2: An effective deep network architecture for fast shot transition detection
Although automatic shot transition detection approaches are already investigated for more than two decades, an effective universal human-level model was not proposed yet.
Ranked #2 on
Camera shot boundary detection
on ClipShots
(using extra training data)
TransNet: A deep network for fast detection of common shot transitions
Shot boundary detection (SBD) is an important first step in many video processing applications.
