We are stumbling across a video tsunami flooding our communication channels. The ubiquity of digital cameras and social networks has increased the amount of visual media content generated and shared by people, in particular videos. Cisco reports that 82% of the internet traffic would be in the form of videos by 2022. The computer vision community has embraced this challenge by offering the first building blocks to translate the visual data in segmented video clips into semantic tags. However, users usually require to go beyond tagging at the video level. For example, someone may want to retrieve important moments such as the “first steps of her child” from a large collection of untrimmed videos; or retrieving all the instances of a home-run from an unsegmented video of baseball. In the face of this data deluge, it becomes crucial to develop efficient and scalable algorithms that can intelligently localize semantic visual content in untrimmed videos. In this work, I address three different challenges on the localization of actions in videos. First, I develop deep-based action proposals and detection models that take a video and generate action-agnostic and class-specific temporal segments, respectively. These models retrieve temporal locations with high accuracy in an efficient manner, faster than real-time. Second, I propose the new task to retrieve and localize temporal moments from a collection of videos given a natural language query. To tackle this challenge, I introduce an efficient and effective model that aligns the text query to individual clips of fixed length while still retrieves moments spanning multiple clips. This approach not only allows smooth interactions with users via natural languagequeries but also reduce the index size and search time for retrieving the moments. Lastly, I introduce the concept of actor-supervision that exploits the inherent compo sitionality of actions, in terms of transformations of actors, to achieve spatiotemporal localization of actions without the need of action box annotations. By designing ef ficient models to scan a single video in real-time; retrieve and localizing moments of interest from multiple videos; and an effective strategy to localize actions without resorting in action box annotations, this thesis provides insights that put us closer to the goal of general video understanding.