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Obtaining annotations for large training sets is expen- sive, especially in settings where domain knowledge is re- quired, such as behavior analysis. Weak supervision has been studied to reduce annotation costs by using weak la- bels from task-specific labeling functions (LFs) to augment ground truth labels. However, domain experts still need to hand-craft different LFs for different tasks, limiting scal- ability. To reduce expert effort, we present AutoSWAP: a framework for automatically synthesizing data-efficient task-level LFs. The key to our approach is to efficiently represent expert knowledge in a reusable domain-specific language and more general domain-level LFs, with which we use state-of-the-art program synthesis techniques and a small labeled dataset to generate task-level LFs. Addition- ally, we propose a novel structural diversity cost that allows for efficient synthesis of diverse sets of LFs, further improv- ing AutoSWAP’s performance. We evaluate AutoSWAP in three behavior analysis domains and demonstrate that Au- toSWAP outperforms existing approaches using only a frac- tion of the data. Our results suggest that AutoSWAP is an effective way to automatically generate LFs that can signif- icantly reduce expert effort for behavior analysis. 

We propose a method for learning the posture and struc- ture of agents from unlabelled behavioral videos. Start- ing from the observation that behaving agents are gener- ally the main sources of movement in behavioral videos, our method, Behavioral Keypoint Discovery (B-KinD), uses an encoder-decoder architecture with a geometric bottle- neck to reconstruct the spatiotemporal difference between video frames. By focusing only on regions of movement, our approach works directly on input videos without requir- ing manual annotations. Experiments on a variety of agent types (mouse, fly, human, jellyfish, and trees) demonstrate the generality of our approach and reveal that our dis- covered keypoints represent semantically meaningful body parts, which achieve state-of-the-art performance on key- point regression among self-supervised methods. Addition- ally, B-KinD achieve comparable performance to supervised keypoints on downstream tasks, such as behavior clas- sification, suggesting that our method can dramatically re- duce model training costs vis-a-vis supervised methods. 

Specialized domain knowledge is often necessary to ac- curately annotate training sets for in-depth analysis, but can be burdensome and time-consuming to acquire from do- main experts. This issue arises prominently in automated behavior analysis, in which agent movements or actions of interest are detected from video tracking data. To reduce annotation effort, we present TREBA: a method to learn annotation-sample efficient trajectory embedding for be- havior analysis, based on multi-task self-supervised learn- ing. The tasks in our method can be efficiently engineered by domain experts through a process we call “task program- ming”, which uses programs to explicitly encode structured knowledge from domain experts. Total domain expert effort can be reduced by exchanging data annotation time for the construction of a small number of programmed tasks. We evaluate this trade-off using data from behavioral neuro- science, in which specialized domain knowledge is used to identify behaviors. We present experimental results in three datasets across two domains: mice and fruit flies. Using embeddings from TREBA, we reduce annotation burden by up to a factor of 10 without compromising accuracy com- pared to state-of-the-art features. Our results thus suggest that task programming and self-supervision can be an ef- fective way to reduce annotation effort for domain experts. 

Hand-annotated data can vary due to factors such as subjective differences, intra-rater variability, and differing annotator expertise. We study annotations from different ex- perts who labelled the same behavior classes on a set of an- imal behavior videos, and observe a variation in annotation styles. We propose a new method using program synthesis to help interpret annotation differences for behavior analysis. Our model selects relevant trajectory features and learns a temporal filter as part of a program, which corresponds to estimated importance an annotator places on that feature at each timestamp. Our experiments on a dataset from behav- ioral neuroscience demonstrate that compared to baseline approaches, our method is more accurate at capturing an- notator labels and learns interpretable temporal filters. We believe that our method can lead to greater reproducibility of behavior annotations used in scientific studies. We plan to release our code.