Temporal Flow Mask Attention for Open-Set Long-Tailed Recognition of Wild Animals in Camera-Trap Images

• URL: http://arxiv.org/abs/2208.14625v1
• Date: Wed, 31 Aug 2022 04:15:17 GMT
• Title: Temporal Flow Mask Attention for Open-Set Long-Tailed Recognition of Wild Animals in Camera-Trap Images
• Authors: Jeongsoo Kim, Sangmin Woo, Byeongjun Park, Changick Kim
• Abstract summary: We propose the Temporal Flow Mask Attention Network to tackle the open-set long-tailed recognition problem. We extract temporal features of sequential frames using the optical flow module and learn informative representation using attention residual blocks. We show that applying the meta-embedding technique boosts the performance of the method in open-set long-tailed recognition.
• Score: 21.473296246163443
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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