Action Localization refers to the task of identifying and precisely localizing specific actions or activities within a video sequence or a series of frames. In the context of computer vision and video analysis, action localization goes beyond action recognition, which only identifies the actions present in a video. Instead, it aims to pinpoint the exact time intervals or spatial regions within the video where particular actions are happening. This task is important for various applications, including video surveillance, human-computer interaction, and content analysis.

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Learning Higher-order Object Interactions for Keypoint-based Video Understanding

Learning Higher-order Object Interactions for Keypoint-based Video Understanding Action recognition is an important problem that requires identifying actions in video by learning complex interactions across scene actors and objects. However, modern deep-learning based networks often require significant computation and may capture scene context using various modalities that further increases compute costs. Efficient methods such as those used for AR/VR often only use human-keypoint information but suffer from a loss of scene context that hurts accuracy. In this paper, we describe an action-localization method, KeyNet, that uses only the keypoint data for tracking and action recognition. Specifically, KeyNet introduces the use of object based keypoint information to capture context in the scene. Our method illustrates how to build a structured intermediate representation that allows modeling higher-order interactions in the scene from object and human keypoints without using any RGB information. We find that KeyNet is able to track and classify human actions at just 5 FPS. More importantly, we demonstrate that object keypoints can be modeled to recover any loss in context from using keypoint information over AVA action and Kinetics datasets.

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.