Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation

By Zhuowen Tu, Xiang Bai. 2010

625 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Learning Non-maximum Suppression (2017, methodology): “Some methods improve object detection by jointly rescoring detections locally [30, 4] or globally [31] using image information.”
Convolutional Pose Machines (2016, methodology): “In contrast, methods based on a sequential prediction framework [29] learn an implicit spatial model with potentially complex interactions between variables by directly training an inference procedure, as in [22, 25, 31, 41].”
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs (2016, background): “Most of the successful semantic segmentation systems developed in the previous decade relied on hand-crafted features combined with flat classifiers, such as Boosting [24], [42], Random Forests [43], or Support Vector Machines [44].”
Designing deep networks for surface normal estimation (2014, background): “Additionally, both coarse and local networks treat every pixel independently; our fusion network can also be thought of applying a form of learned reasoning akin to [26, 35] on our outputs.”
Iterative Instance Segmentation (2015, methodology): “Auto-context [32] and inference machines [26] take advantage of the observation that performing accurate inference does not necessarily require modelling the posterior distribution explicitly.”
Learning message-passing inference machines for structured prediction (2011, methodology): “The technique of [21] can be understood as using forward training on a synchronous message passing using only marginals, similar to mean-field inference.”
Recurrent Human Pose Estimation (2016, methodology): “In other related work, the iterated implicit configuration module of our model bears similarities to autocontext [40] and the message-passing inference machines of [32].”
Action Genome: Actions As Compositions of Spatio-Temporal Scene Graphs (2019, background): “Given its versatile utility, the task of scene graph prediction has resulted in a series of publications [14, 30, 43, 46, 48, 49, 59, 77, 78, 85] that have explored reinforcement learning [49], structured prediction [16, 40, 70], utilizing object attributes [20, 61], sequential prediction [59], few-”
Pose Machines: Articulated Pose Estimation via Inference Machines (2014, background): “While [25] explored the use of context from neighboring pixel classifiers for computer vision tasks.”
Weakly supervised histopathology cancer image segmentation and classification (2014, background): “From a different angle, there is a rich body of literature on supervised approaches for image detection and segmentation (Viola and Jones, 2004; Shotton et al., 2008; Felzenszwalb et al., 2010; Tu and Bai, 2010).”
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, background): “Usually, a separate context model is learned to handle occlusions, see Hoiem et al. (2008); Tu & Bai (2010); Desai et al. (2011); Yang et al. (2012).”
Spatial Memory for Context Reasoning in Object Detection (2017, background): “…strong psychological evidence [4, 37, 64, 65] that context is vital for humans to recognize objects, many empirical studies in the computer vision community [10, 11, 16, 22, 23, 43, 60, 62, 73, 82, 83] have also suggested that recognition algorithms can be improved by proper modeling of context.”
VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images (2017, positive): “, volume data in our case) together with the context information by learning a series of classifiers such as random forests (Tu and Bai, 2010).”
Learning to Filter with Predictive State Inference Machines (2015, methodology): “Toward this end, we generalize the supervised message-passing Inference Machine approach ar X iv :1 51 2.”
Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation (2017, background): “Similarly, autocontext models [45] have made use of label and image priors in segmentation, which require a cascade of models.”
Iterative Visual Reasoning Beyond Convolutions (2018, background): “While various previous work ( e.g . scene-level reasoning [46], attributes [13, 36], structured prediction [24, 9, 47], relationship graph [21, 31, 52]) has approached this problem from different angles, the break-through comes from the idea of feature learning with Con-vNets [16].”