Contextual Priming and Feedback for Faster R-CNN

By Abhinav Shrivastava, Abhinav Kumar Gupta. 2016

156 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection (2019, methodology): “After that, lots of algorithms are proposed to improve its performance, including architecture redesign and reform [4, 9, 5, 28, 30], context and attention mechanism [2, 51, 38, 7, 44], multiscale training and testing [54, 41], training strategy and loss function [40, 52, 61, 17], feature fusion and”
Beyond Skip Connections: Top-Down Modulation for Object Detection (2016, background): “[43] trainval testdev VGG16++ VGG16++ 27.”
Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors (2016, background): “Since appearing in 2015, Faster R-CNN has been particularly influential, and has led to a number of follow-up works [2, 34, 33, 45, 13, 5, 19, 44, 23, 46] (including SSD and R-FCN).”
Residual Attention Network for Image Classification (2017, methodology): “Region proposal [26, 4, 8, 38] has been successfully adopted in image detection task.”
Single-Shot Refinement Neural Network for Object Detection (2017, methodology): “After that, numerous effective techniques are proposed to further improve the performance, such as architecture diagram [5, 25, 55], training strategy [31, 41, 48, 50], contextual reasoning [1, 14, 40, 51] and multiple layers exploiting [3, 24, 26, 42].”
CenterNet: Keypoint Triplets for Object Detection (2019, background): “, a [52, 22] focus on the architecture design, a [1, 10, 37, 47] focus on the contextual relationship, and a [23, 3] focus on the multi-scale unification.”
A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection (2017, methodology): “For example, [32] use segmentation as a way to contextually prime object detectors and provide feedback to initial layers.”
Single-Shot Object Detection with Enriched Semantics (2017, background): “We are not the first one to show segmentation information can be leveraged to help object detection [6, 10, 23].”
Deep Back-Projection Networks for Super-Resolution (2018, methodology): “Feedback procedure has been implemented in various computing tasks [3, 34, 46, 28, 48, 38, 31].”
MTL-NAS: Task-Agnostic Neural Architecture Search Towards General-Purpose Multi-Task Learning (2020, background): “A great success has been witnessed in various areas such as detection [15, 16, 23, 50, 54, 55], depth prediction and semantic segmentation (also surface normal prediction) [10, 65], human-related tasks [21,48,60,62,68], etc .”
Attentional Local Contrast Networks for Infrared Small Target Detection (2020, unknown): “Besides carefully designing the anchor matching strategy [23] or training the network in a scaleaware scheme [24], encoding context that offers more evidence beyond the object extent is also highly explored as a solution to mitigate the problems arising from small objects [25], [26], which is genera”
NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction (2018, background): “Benefiting from that, significant improvements have been witnessed in fundamental computer vision tasks, such as image classification [23, 47, 16, 19, 18], object detection [9, 8, 38, 15, 37, 28, 46, 50, 44, 45], semantic segmentation [4, 29, 39, 1, 35, 5, 2, 51, 3], etc.”
Visual Semantic Navigation using Scene Priors (2018, background): “…has been studied extensively in the computer vision literature (Torralba et al., 2003; Hoiem et al., 2005; Rabinovich et al., 2005; Divvala et al., 2009; Desai et al., 2009; Marszalek et al., 2009; Malisiewicz & Efros, 2009; Mottaghi et al., 2014; Zhu et al., 2015; Shrivastava & Gupta, 2016).”
Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey (2018, background): “In addition, some recent works have also been developed to further boost the performance of CNN-based approaches for COD, such as hard negative mining [61], feature enhancement [41], [62], contextual information fusion [63]–[65], and so on.”
Video Object Detection with an Aligned Spatial-Temporal Memory (2017, background): “Recent work that adopt deep neural networks have significantly advanced the state-of-the-art in static image object detection [16,39,17,38,37,31,9,40,7].”
Deep Learning for Generic Object Detection: A Survey (2018, unknown): “IEEE TPAMI 39(12):2525–2538 27 [245] Shrivastava A.”
Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection (2018, methodology): “While previous work in the non-medical domain has pursued this concept [23, 19, 20, 3], the extra supervision signal is either used sub-optimally or combined with approaches of arguably signiﬁcant model complexity.”