Fusion Attention Mechanism for Foreground Detection Based on Multiscale U-Net Architecture

<table class="table-group" id="tab5"><tr><td><table class="table"><tr><td class="thead-hr" colspan="5"><hr/></td></tr><tr class="thead"><td class="align_left">Methods</td><td class="align_center">Number of models</td><td class="align_center">Network parameters</td><td class="align_center">Training time</td><td class="align_center">GPU</td></tr><tr><td class="thead-hr" colspan="5"><hr/></td></tr><tr><td class="align_left">FgSegNet_v2 [<a href="/journals/cin/2022/7432615/#B31" target="_blank">31</a>]</td><td class="align_center">53</td><td class="align_center">489 M <svg height="11.5564pt" id="M96" style="vertical-align:-2.26807pt" version="1.1" viewbox="-0.0498162 -9.28833 89.2789 11.5564" width="89.2789pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 70 271V270C70 89 136 -72 282 -170L300 -147Z"></path></g><g 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123 -12 169 -12C221 -11 288 15 342 59C403 109 431 165 431 225C431 308 366 395 238 395C212 395 165 379 127 364L153 550Z"></path></g><g transform="matrix(.013,0,0,-0.013,60.525,0)"><path d="M95 130C70 130 46 113 46 88C46 72 54 64 59 64C93 55 121 33 121 -3C121 -41 93 -68 44 -88L55 -117C117 -98 186 -56 186 22C186 91 131 130 95 130Z"></path></g><g transform="matrix(.013,0,0,-0.013,65.668,0)"><path d="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z"></path></g><g transform="matrix(.013,0,0,-0.013,71.908,0)"><path d="M137 343C167 482 260 545 321 574C357 591 397 603 429 609L423 641C382 634 335 622 295 608C189 570 37 457 37 238C37 84 125 -12 242 -12C362 -12 447 89 447 209C447 311 374 393 267 393C247 393 226 386 204 376L137 343ZM227 337C318 337 361 256 361 173C361 105 336 22 258 22C176 22 126 120 126 240C126 266 127 291 132 310C155 323 189 337 227 337Z"></path></g><g transform="matrix(.013,0,0,-0.013,78.148,0)"><path d="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z"></path></g><g transform="matrix(.013,0,0,-0.013,84.388,0)"><path d="M275 270C275 450 212 609 64 710L45 686C145 604 203 442 203 270S147 -63 45 -147L64 -170C213 -68 275 89 275 270Z"></path></g></svg></td><td class="align_center">29 days [<a href="/journals/cin/2022/7432615/#B46" target="_blank">46</a>]</td><td class="align_center">1080 Ti</td></tr><tr><td class="align_left">AMU-Net</td><td class="align_center">1</td><td class="align_center">24.9 M (24, 915, 969)</td><td class="align_center">12 hours</td><td class="align_center">2080 Ti</td></tr><tr class="table-tr"><td colspan="5"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>The differences between AMU-Net and FgSegNet_v2.</div>

Computational Intelligence and Neuroscience

tab5

Table 5

Table 5: Fusion Attention Mechanism for Foreground Detection Based on Multiscale U-Net Architecture 