Sfic: Sparsity-Driven Facial Image Compression Network

Moving object detection is a core task in computer vision. However, existing deep learning-based moving object detection methods require a large number of labeled frames to achieve good generalization and performance. This paper proposes a novel deep learning network called FeSh-Net. This network can learn to extract an exemplar-based attention map using a few labeled frames, which guides the network to know which object is foreground and which is a background in the current frame. FeSh-Net is trained using a novel meta-learning technique to be able to segment moving objects from new unseen videos. The proposed network is evaluated using the benchmark CDNet. The results of the proposed FeSh-Net are compared with current state-of-the-art methods, and the results show that FeSh-Net outperforms the best reported state-of-the-art method by 4.4% on average. Additionally, FeSh-Net performs better than other methods when tested using new unseen videos.