European conference on computer vision (ECCV)

By Andrew William Fitzgibbon, Marc Pollefeys, Luc Van Gool, Andrew Zisserman. 2006

739 citations recorded by the source corpus.

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Citations in the corpus

Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning (2019, background): “Counterfactuals are also used in generative adversarial networks (GANs) to improve training [Neal et al., 2018].”
Leveraging Human Guidance for Deep Reinforcement Learning Tasks (2019, methodology): “Existing works have proposed to use a learned gaze prediction model to guide the process of learning a human policy [Li et al. , 2018; Zhang et al. , 2018; Kim et al. , 2018].”
Recurrent Convolutional Neural Networks for Scene Parsing (2013, background): “However, in scene parsing (especially in datasets with large number of classes), classes which are much more frequent than others (e.g. the “sky” class is much more frequent than “moon”) have more impact on this measure.”
Convolutional Networks with Adaptive Inference Graphs (2017, background): “Lastly, due to surface resemblance, we also compare our model to stochastic depth (Huang et al. 2016).”
Multiview RGB-D Dataset for Object Instance Detection (2016, methodology): “Recently, methods such as YOLO [22], SSD [19], and Faster RCNN [23] drop the use of unsupervised proposal generation techniques and train their networks end-to-end to predict bounding boxes in addition to the classification score for each object category.”
Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images (2018, background): “In all three cases, KAZE/VGG outperforms other feature combinations with an AUC of 88.51 % on all modules, followed by KAZE/SIFT with an AUC of 87.22 %.”
Dense Semantic Correspondence Where Every Pixel is a Classifier (2015, background): “Liu et al . proposed to circumvent these problems by assuming the dense geometric dependencies in an image can be adequately governed by a variational regularizer, and that accurate local detections between semantically similar images can be attained through the L1 distance between SIFT descriptors.”
Depth-Based Hand Pose Estimation: Methods, Data, and Challenges (2015, unknown): “Some authors responded to our request for their code (Rogez et al. 2014).”
The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM (2016, background): “, 2013) and 3D (Kim et al., 2016; Kueng et al., 2016; Rebecq et al., 2016b), optical flow (Bardow et al.”
Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks (2016, unknown): “…et al. [4] derive CA-SVR with a cumulative attribute space and SVR, Chang et al. [1] rank hyperplanes for age estimation (OHRank), Huerta et al. [29] fuse texture and local appearance descriptors, Luu et al. [38] use AAM and SVR, while Guo and Mu [22] use CCA and PLS. Recently, Yi et al. [59]…”
Large-scale Online Kernel Learning with Random Feature Reparameterization (2017, methodology): “To our knowledge, this question has not been properly addressed in the existing work including [Băzăvan et al., 2012; Moeller et al., 2016].”
SPeCiaL: Self-Supervised Pretraining for Continual Learning (2021, background): “, 2019] convolutions with Group Normalization [Wu and He, 2018], a normalization approach with does not use batch-level statistics and has shown strong performance in small batch settings.”
Going deeper into action recognition: A survey (2016, methodology): “The dominant motion is assumed to be caused by the camera and is compensated from original trajectories by subtraction in Jiang et al. (2012) and through afﬁne transformations in Jain et al. (2013).”