Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Led to

• ResNet2015 · cited 2×
  “We adopt Faster R-CNN Ren2015 as the detection method.”

  From ResNet · §Experiments
• Visual Genome2016 · cited 2×
  “Much progress has been made in recent years towards this goal, including image classification Deng et al., 2009; Perronnin et al., 2010; Simonyan and Zisserman, 2014; Krizhevsky et al., 2012; Szegedy et al., 2014 and obj…”

  From Visual Genome · §Introduction
• Goyal large-batch SGD2017 · cited 4×
  “Additionally, we show that the linear scaling rule and warmup generalize to more complex tasks including object detection and instance segmentation Girshick2015; Ren2015; He2017; Long2015, which we demonstrate via the re…”

  From Goyal large-batch SGD · §Introduction
• JFT-300M (unreasonable effectiveness)2017 · cited 4×, 1 in Method
  “We use the Faster RCNN framework [33] for its state-of-the-art performance.”

  From JFT-300M (unreasonable effectiveness) · §Training and Evaluation Framework
• NASNet2017 · cited 2×
  “In our experiments, the features learned by NASNets from ImageNet classification can be combined with the Faster-RCNN framework faster_rcnn to achieve state-of-the-art on COCO object detection task for both the largest a…”

  From NASNet · §Introduction
• Bottom-Up Top-Down attention2017 · cited 3×, 1 in Method
  “Practically, we implement bottom-up attention using Faster R-CNN faster_rcnn, which represents a natural expression of a bottom-up attention mechanism.”

  From Bottom-Up Top-Down attention · §Introduction
• Neural Baby Talk2018 · cited 4×, 3 in Method
  “Given an image 𝑰\bm{I}, and the corresponding caption 𝒚\bm{y}, the candidate grounding regions are obtained by using a pre-trained Faster-RCNN network ren2015faster.”

  From Neural Baby Talk · §Method
• Bilinear Attention Networks2018 · cited 2×
  “Note that both Query-Adaptive RCNN [10] and our off-the-shelf object detector [2] are based on Faster RCNN [25] and pre-trained on Visual Genome [17].”

  From Bilinear Attention Networks · §Flickr30k entities results and discussions
• nocaps2018 · cited 3×, 2 in Method
  “Hence, we use a Faster-RCNN [36] model pre-trained using Open Images V4 [20] (referred as OI detector henceforth), to obtain candidate region proposals as described in Section 4 of the main paper.”

  From nocaps · §Additional Implementation Details for Baseline Models
• CPC v22019 · cited 2×, 1 in Method
  “As such 𝔻l{\mathbb{D}}_{l} is the entire PASCAL VOC 2007 dataset (comprised of 5011 labeled images); hψh_{\psi} and ℒSupL_{\textrm{Sup}} are the Faster-RCNN architecture and loss (Ren et al. 2015).”

  From CPC v2 · §Experimental Setup
• LXMERT2019 · cited 2×, 2 in Method
  “For these reasons, we take detected labels output by Faster R-CNN Ren et al. 2015.”

  From LXMERT · §Pre-Training Strategies
• Decoupled box proposals captioning2019 · cited 1×, 1 in Method
  “In this paper, we select Faster R-CNN Ren et al. 2015b, a widely-used object detector in image captioning and VQA.”

  From Decoupled box proposals captioning · §Features and Experimental Setup
• MoCo2019 · cited 2×
  “The detector is Faster R-CNN Ren2015 with a backbone of R50-dilated-C5 or R50-C4 He2017 (details in appendix), with BN tuned, implemented in Wu2019.”

  From MoCo · §Experiments
• Grid features for VQA2020 · cited 6×
  “Unlike normal attention that uses ‘top-down’ linguistic inputs to focus on specific parts of the visual input, bottom-up attention uses pre-trained object detectors ren2015faster to identify salient regions based solely…”

  From Grid features for VQA · §Introduction
• VL-BERT meta-analysis2020 · cited 1×, 1 in Method
  “V&L BERTs typically extract image features using a Faster R-CNN (Ren et al. 2015) trained on the Visual Genome dataset (VG; Krishna et al. 2017), either with a ResNet-101101 (He et al. 2016) or a ResNeXT-152152 backbone…”

  From VL-BERT meta-analysis · §Experimental Setup
• VL-T52021 · cited 1×, 1 in Method
  “We represent an input image vv with n=36n{=}36 object regions from a Faster R-CNN (Ren et al. 2015) trained on Visual Genome (Krishna et al. 2016) for object and attribute classification (Anderson et al. 2018).”

  From VL-T5 · §Model
• ViLT2021 · cited 1×, 1 in Method
  “They are obtained from an off-the-shelf object detector like Faster R-CNN (Ren et al. 2016).”

  From ViLT · §Background
• Conceptual 12M2021 · cited 1×, 1 in Method
  “We train a Faster-RCNN [68] on Visual Genome [43], with a ResNet101 [29] backbone trained on JFT [30] and fine-tuned on ImageNet [69].”

  From Conceptual 12M · §Evaluating Vision-and-Language Pre-Training Data
• Dynamic Head2021 · cited 4×, 1 in Method
  “Two-stage detectors utilize region proposal and ROI-pooling [23] layers to extract intermediate representations from feature pyramid of a backbone network.”

  From Dynamic Head · §Our Approach