James M. Rehg works at Georgia Institute of Technology.

Posts

Tripping through time: Efficient Localization of Activities in Videos

Tripping through time: Efficient Localization of Activities in Videos Localizing moments in untrimmed videos via language queries is a new and interesting task that requires the ability to accurately ground language into video. Previous works have approached this task by processing the entire video, often more than once, to localize relevant activities. In the real world applications of this approach, such as video surveillance, efficiency is a key system requirement. In this paper, we present TripNet, an end-to-end system that uses a gated attention architecture to model fine-grained textual and visual representations in order to align text and video content. Furthermore, TripNet uses reinforcement learning to efficiently localize relevant activity clips in long videos, by learning how to intelligently skip around the video. It extracts visual features for few frames to perform activity classification. In our evaluation over Charades-STA [14], ActivityNet Captions [26] and the TACoS dataset [36], we find that TripNet achieves high accuracy and saves processing time by only looking at 32-41% of the entire video.

Tripping through time: Efficient Temporal Localization of Activities in Videos

Tripping through time: Efficient Temporal Localization of Activities in Videos Localizing moments in untrimmed videos using language queries is a new task that requires ability to accurately ground language into video. Existing approaches process the video, often more than once to localize the activities and are inefficient. In this paper, we present TripNet, an end-to-end system which uses a gated attention architecture to model fine grained textual and visual representations in order to align text and video content. Furthermore, TripNet uses reinforcement learning to efficiently localize relevant activity clips in long videos, by learning how to skip around the video saving feature extraction and processing time. In our evaluation over Charades-STA and ActivityNet Captions dataset, we find that TripNet achieves high accuracy and only processes 32-41% of the entire video.