TY - JOUR AU - Le, Trung-Nghia AU - Nguyen, Vuong AU - Le, Cong AU - Nguyen, Tan-Cong AU - Tran, Minh-Triet AU - Nguyen, Tam V. PY - 2021/05/18 Y2 - 2024/03/28 TI - CamouFinder: Finding Camouflaged Instances in Images JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 35 IS - 18 SE - AAAI Demonstration Track DO - 10.1609/aaai.v35i18.18015 UR - https://ojs.aaai.org/index.php/AAAI/article/view/18015 SP - 16071-16074 AB - In this paper, we investigate the interesting yet challenging problem of camouflaged instance segmentation. To this end, we first annotate the available CAMO dataset at the instance level. We also embed the data augmentation in order to increase the number of training samples. Then, we train different state-of-the-art instance segmentation on the CAMO-instance data. Last but not least, we develop an interactive user interface which demonstrates the performance of different state-of-the-art instance segmentation methods on the task of camouflaged instance segmentation. The users are able to compare the results of different methods on the given input images. Our work is expected to push the envelope of the camouflage analysis problem. ER -