Transformers only look once with nonlinear combination for real-time object detection

Xia, Ruiyang; Li, Guoquan; Huang, Zhengwen; Pang, Yu; Qi, Man

doi:10.1007/s00521-022-07333-y

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…Therefore, foreground enhancement highlights the objects for further correlation analysis. TOLO [69] is one of the recent works aiming to bring inductive bias (using CNN) to the transformer architecture through a simple neck module. This module combines features from different layers to incorporate high-resolution and high-semantic properties.…”

Section: Architecture and Block Modificationsmentioning

confidence: 99%

“…Using this dataset for pretraining and subsequently fine-tuning on MS COCO significantly enhances performance outcomes. Model Backbone GFLOPS↓/FPS ↑ #params↓ mAP @[0.5,0.95] ↑ Epochs↓ URL Faster RCNN-DC5 (NeurIPS2015) [24] ResNet50 320/16 166M 21.4 37 Link Faster RCNN-FPN (NeurIPS2015) [24] ResNet50 180/26 42M 24.2 37 Link Faster RCNN-FPN (NeurIPS2015) [24] ResNet101 246/20 60M 25.2 -Link RepPoints v2-DCN-MS (NeurIPS2020) [119] ResNeXt101 -/--34.5 * 24 Link FCOS (ICCV2019) [56] ResNet50 177/17 -26.2 36 Link CBNet V2-DCN(ATSS [120]) (TIP2022) [70] Res2Net101 -/-107M 35.7 * 20 Link CBNet V2-DCN(Cascade RCNN) (TIP2022) [70] Res2Net101 -/-146M 37.4 * 32 Link DETR (ECCV2020) [31] ResNet50 86/28 41M 20.5 500 Link DETR-DC5 (ECCV2020) [31] ResNet50 187/12 41M 22.5 500 Link DETR (ECCV2020) [31] ResNet101 52/20 60M 21.9 -Link DETR-DC5 (ECCV2020) [31] ResNet101 253/10 60M 23.7 -Link ViT-FRCNN (arXiv2020) [32] --/--17.8 --RelationNet++ (NeurIPS2020) [35] ResNeXt101 -/--32.8 * -Link RelationNet++-MS (NeurIPS2020) [35] ResNeXt101 [41] ResNeXt101 -/--34.4 * -Link Dynamic DETR (ICCV2021) [44] ResNet50 -/--28.6 * --Dynamic DETR-DCN (ICCV2021) [44] ResNeXt101 -/--30.3 * --TSP-FCOS (ICCV2021) [55] ResNet101 255/12 -27.7 36 Link TSP-RCNN (ICCV2021) [55] ResNet101 254/9 -29.9 96 Link Mask R-CNN (ICCV2021) [57] Conformer-S/16 457.7/-56.9M 28.7 12 Link Conditional DETR-DC5 (ICCV2021) [65] ResNet101 262/-63M 27.2 108 Link SOF-DETR (2022JVCIR) [95] ResNet50 -/--21.7 -Link DETR++ (arXiv2022) [91] ResNet50 -/--22.1 --TOLO-MS (NCA2022) [69] --/57 -24.1 --Anchor DETR-DC5 (AAAI2022) [46] ResNet101 -/--25.8 50 Link DESTR-DC5 (CVPR2022) [68] ResNet101 299/-88M 28.2 50 -Conditional DETR v2-DC5 (arXiv2022) [66] ResNet101 228/-65M 26.3 50 -Conditional DETR v2 (arXiv2022) [66] Hourglass48 521/-90M 32.1 50 -FP-DETR-IN (ICLR2022) [80] --/-36M 26.5 50 Link DAB-DETR-DC5 (arXiv2...…”

Section: Uav123 [109]: This Dataset Contains 123 Videos Acquired Withmentioning

confidence: 99%

See 1 more Smart Citation

Extended Expectation Maximization for Under-Fitted Models

Rekavandi¹,

SEGHOUANE²,

Boussaid³

et al. 2022

Preprint

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<p>In this paper, we generalize the well-known Expectation Maximization (EM) algorithm using the α− divergence for Gaussian Mixture Model (GMM). This approach is used in robust subspace detection when the number of parameters is kept small to avoid overfitting and large estimation variances. The level of robustness can be tuned by the parameterα. When α→1, our method is equivalent to the standard EM approach and for α <1 the method is robust against potential outliers. Simulation results show that the method outperforms the standard EM when it comes to mismatches between noise models and their realizations. In addition, we use the proposed method to detect active brain areas using collected functional Magnetic Resonance Imaging (fMRI) data during task-related experiments.</p>

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Section: Architecture and Block Modificationsmentioning

confidence: 99%