Search Results for author:
Found 4 papers, 2 papers with code
Leveraging Next-Active Objects for Context-Aware Anticipation in Egocentric Videos
Compared to existing video modeling architectures for action anticipation, NAOGAT captures the relationship between objects and the global scene context in order to predict detections for the next active object and anticipate relevant future actions given these detections, leveraging the objects' dynamics to improve accuracy.
Guided Attention for Next Active Object @ EGO4D STA Challenge
In this technical report, we describe the Guided-Attention mechanism based solution for the short-term anticipation (STA) challenge for the EGO4D challenge.
Ranked #1 on
Short-term Object Interaction Anticipation
on Ego4D
Enhancing Next Active Object-based Egocentric Action Anticipation with Guided Attention
To this end, we propose a novel approach that applies a guided attention mechanism between the objects, and the spatiotemporal features extracted from video clips, enhancing the motion and contextual information, and further decoding the object-centric and motion-centric information to address the problem of STA in egocentric videos.
Anticipating Next Active Objects for Egocentric Videos
This paper addresses the problem of anticipating the next-active-object location in the future, for a given egocentric video clip where the contact might happen, before any action takes place.
