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We introduce the task of spotting temporally precise, fine-grained events in video (detecting the precise moment in time events occur). Precise spotting requires models to reason globally about the full-time scale of actions and locally to identify subtle frame-to-frame appearance and motion differences that identify events during these actions. Surprisingly, we find that top performing solutions to prior video understanding tasks such as action detection and segmentation do not simultaneously meet both requirements. In response, we propose E2E-Spot, a compact, end-to-end model that performs well on the precise spotting task and can be trained quickly on a single GPU. We demonstrate that E2E-Spot significantly outperforms recent baselines adapted from the video action detection, segmentation, and spotting literature to the precise spotting task. Finally, we contribute new annotations and splits to several fine-grained sports action datasets to make these datasets suitable for future work on precise spotting. 

We describe the results of a randomized controlled trial of video-streaming algorithms for bitrate selection and network prediction. Over the last year, we have streamed 38.6 years of video to 63,508 users across the Internet. Sessions are randomized in blinded fashion among algorithms. We found that in this real-world setting, it is difficult for sophisticated or machine-learned control schemes to outperform a "simple" scheme (buffer-based control), notwithstanding good performance in network emulators or simulators. We performed a statistical analysis and found that the heavy-tailed nature of network and user behavior, as well as the challenges of emulating diverse Internet paths during training, present obstacles for learned algorithms in this setting. We then developed an ABR algorithm that robustly outperformed other schemes, by leveraging data from its deployment and limiting the scope of machine learning only to making predictions that can be checked soon after. The system uses supervised learning in situ, with data from the real deployment environment, to train a probabilistic predictor of upcoming chunk transmission times. This module then informs a classical control policy (model predictive control). To support further investigation, we are publishing an archive of data and results each week, and will open our ongoing study to the community. We welcome other researchers to use this platform to develop and validate new algorithms for bitrate selection, network prediction, and congestion control.