In neuroscience, understanding animal behaviors is key to studying their memory patterns. Meanwhile, this is also the most time-consuming and difficult process because it relies heavily on humans to manually annotate the videos recording the animals. In this paper, we present a visual recognition system to automatically annotate animal behaviors to save human annotation costs. By treating the annotation task as a per-frame action classification problem, we can fine-tune a powerful pre-trained convolutional neural network (CNN) for this task. Through extensive experiments, we demonstrate our model not only provides more accurate annotations than alternate automatic methods, but also provides reliable annotations that can replace human annotations for neuroscience experiments.
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