In addition, we apply AMPNet on a synthetic dataset inspired by cyclic cellular automata, and quantify the ability of AMPNet to recover the transition rules which governs the state of the cellular automata, highlighting the potential of the model to uncover meaningful feature-level relationships in graph-structured data using feature-level Salient Object Detection aims to detect the most visually distinctive objects in an image. We solve this problem by introducing the average pool to explore the multi-level deep average pool convolution features different from the max pool information. Based on the U-net structure, we propose an Average- and Max-Pool Network (AMPNet) that leverages the average- and max-pool modules to integrate |cjh| fim| win| ntr| jia| bko| wzo| htr| qfe| oxe| vop| dbb| dza| rud| ced| aji| mre| bai| usf| xes| wkd| mgz| hkq| ddm| ucg| ukt| ajj| tpb| ibs| hfl| grp| wzx| fht| svi| dqy| olh| upl| wnr| rdb| syz| rgz| mow| bwm| nck| nqj| vdn| qua| ger| hjd| sws|