Camera traps, unmanned observation devices, and deep learning-based image recognition systems have greatly reduced human effort in collecting and analyzing wildlife images. However, data collected via above apparatus exhibits 1) long-tailed and 2) open-ended distribution problems. To tackle the open-set long-tailed recognition problem, we propose the Temporal Flow Mask Attention Network that comprises three key building blocks: 1) an optical flow module, 2) an attention residual module, and 3) a meta-embedding classifier. We extract temporal features of sequential frames using the optical flow module and learn informative representation using attention residual blocks. Moreover, we show that applying the meta-embedding technique boosts the performance of the method in open-set long-tailed recognition. We apply this method on a Korean Demilitarized Zone (DMZ) dataset. We conduct extensive experiments, and quantitative and qualitative analyses to prove that our method effectively tackles the open-set long-tailed recognition problem while being robust to unknown classes.

Fairness Indicators is designed to support teams in evaluating and improving models for fairness concerns in partnership with the broader Tensorflow toolkit. The tool is currently actively used internally by many of our products, and is now available in BETA to try for your own use cases. Fairness Indicators enables easy computation of commonly-identified fairness metrics for binary and multiclass classifiers. At Google, it is important for us to have tools that can work on billion-user systems. Fairness Indicators will allow you to evaluate across any size of use case.