International Journal of Computer Vision

2018

Citations in the corpus

Visual interpretability for deep learning: a survey (2018, background): “This idea is related to the disentanglement of the local, bottom-up, and top-down information components for prediction [Wu et l., 2007; Yang et al., 2009; Wu and Zhu, 2011].”
Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering (2016, background): “Recently, deep learning has achieved widespread success in numerous machine learning tasks [Krizhevsky et al., 2012; Zheng et al., 2014b; Szegedy et al., 2015; Zheng et al., 2014a; He et al., 2016; Zheng et al., 2015; 2016], where learning good representations by deep neural networks (DNN) lies in P”
Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization (2017, unknown): “, 2013b], human action recognition [Jones and Shao, 2014; Shao et al., 2016; Wu et al., 2017b; Wang et al., 2016b; Wang et al., 2014b; Wu et al., 2017a; Wu and Wang, 2017; Wang et al., 2012] etc.”
Understanding How Feature Structure Transfers in Transfer Learning (2017, background): “The last decade has witnessed the success of various transfer learning algorithms [Shao et al., 2014; Long et al., 2015; Li et al., 2015; Liu et al., 2016; Shao et al., 2016] in exploiting the transfer of knowledge across domains.”
Collaborative Learning for Weakly Supervised Object Detection (2018, methodology): “Therefore, it is a common practice to validate the model’s CorLoc on training set [Deselaers et al., 2012].”
An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents (2018, background): “, 2015] that show an agent’s activations as it interacts with a game, and also enables creating synthetic inputs via activation maximization [Erhan et al., 2009; Zeiler and Fergus, 2014; Olah et al., 2018; Nguyen et al., 2017; Simonyan et al., 2013; Yosinski et al., 2015; Mahendran and Vedaldi, 2016”
Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review (2020, methodology): “, 2014] uses Selective Search (SS) [Uijlings et al., 2013] methods to generate region proposals, uses CNN to extract features and trains classifiers using SVM.”