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Image Segmentation by Iterative Inference from Conditional Score Estimation [article]

Adriana Romero, Michal Drozdzal, Akram Erraqabi, Simon Jégou, Yoshua Bengio
2017 arXiv   pre-print
We experimentally find that the proposed iterative inference from conditional score estimation by conditional denoising autoencoders performs better than comparable models based on CRFs or those not using  ...  This approach is applied to image pixel-wise segmentation, with the estimated conditional score used to perform gradient ascent towards a mode of the estimated conditional distribution.  ...  Can a conditional DAE be used successfully as the building block of iterative inference for image segmentation? 2.  ... 
arXiv:1705.07450v2 fatcat:ridct2njlzfilbs7b2n5amllem

CaRTS: Causality-driven Robot Tool Segmentation from Vision and Kinematics Data [article]

Hao Ding, Jintan Zhang, Peter Kazanzides, Jie Ying Wu, Mathias Unberath
2022 arXiv   pre-print
Rather than directly inferring segmentation masks from observed images, CaRTS iteratively aligns tool models with image observations by updating the initially incorrect robot kinematic parameters through  ...  With the introduction of deep learning, many methods were presented to solve instrument segmentation directly and solely from images.  ...  Acknowledgement: This research is supported by a collaborative research agreement with the MultiScale Medical Robotics Center at The Chinese University of Hong Kong.  ... 
arXiv:2203.09475v3 fatcat:qyoonmour5aizlxnxuqgxuc6ge

What is Healthy? Generative Counterfactual Diffusion for Lesion Localization [article]

Pedro Sanchez, Antanas Kascenas, Xiao Liu, Alison Q. O'Neil, Sotirios A. Tsaftaris
2022 arXiv   pre-print
In this paper, we consider the problem of inferring pixel-level predictions of brain lesions by only using image-level labels for training.  ...  Reducing the requirement for densely annotated masks in medical image segmentation is important due to cost constraints.  ...  This work was partially supported by the Alan Turing Institute under the EPSRC grant EP N510129\1.  ... 
arXiv:2207.12268v1 fatcat:tnczybopufh77iehbspzcbagme

Combining Bottom-Up, Top-Down, and Smoothness Cues for Weakly Supervised Image Segmentation

Anirban Roy, Sinisa Todorovic
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
This paper addresses the problem of weakly supervised semantic image segmentation.  ...  Our problem statement differs from common semantic segmentation, where pixelwise annotations are typically assumed available in training.  ...  Acknowledgment This work was supported in part by DARPA XAI and NSF RI1302700.  ... 
doi:10.1109/cvpr.2017.770 dblp:conf/cvpr/RoyT17 fatcat:ths4xwlamjgldloxtfechm4juy

Co-segmentation for Space-Time Co-located Collections [article]

Hadar Averbuch-Elor, Johannes Kopf, Tamir Hazan, Daniel Cohen-Or
2017 arXiv   pre-print
We present a co-segmentation technique for space-time co-located image collections.  ...  Thus, to disambiguate what the common foreground object is, we introduce a weakly-supervised technique, where we assume only a small seed, given in the form of a single segmented image.  ...  The final results are obtained by averaging all the likelihood estimates follows by a graph-cut segmentation.  ... 
arXiv:1701.08931v1 fatcat:mtm5ybbg5zdthex3bpercw56yi

SegDiff: Image Segmentation with Diffusion Probabilistic Models [article]

Tomer Amit, Eliya Nachmani, Tal Shaharbany, Lior Wolf
2021 arXiv   pre-print
The information in the input image and in the current estimation of the segmentation map is merged by summing the output of two encoders.  ...  Additional encoding layers and a decoder are then used to iteratively refine the segmentation map using a diffusion model.  ...  conditioning the step estimation function θ on an input tensor that combines information derived from both the current estimate x t and the input image I.  ... 
arXiv:2112.00390v1 fatcat:nwtzip66lvc2posas7ar6zbswq

Streaming Multiscale Deep Equilibrium Models [article]

Can Ufuk Ertenli, Emre Akbas, Ramazan Gokberk Cinbis
2022 arXiv   pre-print
For this purpose, we leverage the recently emerging implicit layer model which infers the representation of an image by solving a fixed-point problem.  ...  Our main insight is to leverage the slowly changing nature of videos and use the previous frame representation as an initial condition on each frame.  ...  Similarly, on the Cityscapes semantic segmentation task, using StreamDEQ instead of the standard DEQ inference scheme improves the converged streaming mIoU score from 42.3 to 71.5 when 4 inference iterations  ... 
arXiv:2204.13492v1 fatcat:vaiyarxqgrbqrpc6jfr6hv2c74

Fast Semantic Image Segmentation with High Order Context and Guided Filtering [article]

Falong Shen, Gang Zeng
2016 arXiv   pre-print
This paper describes a fast and accurate semantic image segmentation approach that encodes not only the discriminative features from deep neural networks, but also the high-order context compatibility  ...  segmentation.  ...  In each iteration, the classifier is trained both on local image feature and estimated label context output by the previous classifier.  ... 
arXiv:1605.04068v1 fatcat:aiqmocow2ng2dntzcwbozgrecy

Recovering Occlusion Boundaries from an Image

Derek Hoiem, Alexei A. Efros, Martial Hebert
2010 International Journal of Computer Vision  
Rather than viewing the problem as one of pure image processing, our approach employs cues from an estimated surface layout and applies Gestalt grouping principles using a conditional random field (CRF  ...  We propose a hierarchical segmentation process, based on agglomerative merging, that re-estimates boundary strength as the segmentation progresses.  ...  This material is based upon work supported by the NSF under award IIS-0904209 (DH) and CAREER award IIS-0546547 (AE), as well as a Microsoft Graduate Fellowship (DH).  ... 
doi:10.1007/s11263-010-0400-4 fatcat:wjz5ewjkebfqlotitd6btoaotm

Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets

Aurelien Lucchi, Yunpeng Li, Pascal Fua
2013 2013 IEEE Conference on Computer Vision and Pattern Recognition  
We focus on the setting of general graphical models, such as loopy MRFs and CRFs commonly used in image segmentation, where exact inference is intractable and the most violated constraints can only be  ...  We propose a working set based approximate subgradient descent algorithm to minimize the margin-sensitive hinge loss arising from the soft constraints in max-margin learning frameworks, such as the structured  ...  We extract feature vectors by first over-segmenting images using SLIC superpixels [1] . We then extract SIFT descriptors and color histograms from image patches surrounding each superpixel centroid.  ... 
doi:10.1109/cvpr.2013.259 dblp:conf/cvpr/LucchiLF13 fatcat:k72mmfmet5edll4kgqv5axcyc4

WaveGrad: Estimating Gradients for Waveform Generation [article]

Nanxin Chen, Yu Zhang, Heiga Zen, Ron J. Weiss, Mohammad Norouzi, William Chan
2020 arXiv   pre-print
It starts from a Gaussian white noise signal and iteratively refines the signal via a gradient-based sampler conditioned on the mel-spectrogram.  ...  This paper introduces WaveGrad, a conditional model for waveform generation which estimates gradients of the data density.  ...  (l s−1 , l s ), and then sample from this segment uniformly to give √ᾱ .  ... 
arXiv:2009.00713v2 fatcat:6uceikxky5dfvbmmwytg4utcja

Deeply Learning the Messages in Message Passing Inference [article]

Guosheng Lin, Chunhua Shen, Ian Reid, Anton van den Hengel
2015 arXiv   pre-print
Deep structured output learning shows great promise in tasks like semantic image segmentation.  ...  We apply our method to semantic image segmentation on the PASCAL VOC 2012 dataset.  ...  Learning CNN message estimators is efficient, which does not involve expensive inference steps for gradient calculation.  ... 
arXiv:1506.02108v3 fatcat:hwxtrxoku5hzph6qkto3vlij7m

Probabilistic Joint Image Segmentation and Labeling by Figure-Ground Composition

Adrian Ion, João Carreira, Cristian Sminchisescu
2013 International Journal of Computer Vision  
image interpretations (tilings) composed from those segments, and over their labeling into categories.  ...  The process of drawing samples from the joint distribution can be interpreted as first sampling tilings, followed by sampling labelings conditioned on the choice of a particular tiling. A.  ...  Acknowledgments This work was supported, in part, by CNCS-UEFICSDI, under PCE-2011-3-0438, and CT-ERC-2012-1, and by FCT under PTDC/EEA-CRO/122812/2010.  ... 
doi:10.1007/s11263-013-0663-7 fatcat:bp65aler4jhirgxv6xy2svk6lq

Unsupervised Video Decomposition using Spatio-temporal Iterative Inference [article]

Polina Zablotskaia, Edoardo A. Dominici, Leonid Sigal, Andreas M. Lehrmann
2020 arXiv   pre-print
This is achieved by leveraging 2D-LSTM, temporally conditioned inference and generation within the iterative amortized inference for posterior refinement.  ...  We propose a novel spatio-temporal iterative inference framework that is powerful enough to jointly model complex multi-object representations and explicit temporal dependencies between latent variables  ...  Acknowledgments and Disclosure of Funding This work was funded, in part, by the Vector Institute for AI, Canada CIFAR AI Chair, NSERC CRC and an NSERC DG and Discovery Accelerator Grants.  ... 
arXiv:2006.14727v1 fatcat:pvk3z4jqe5fkzogaaf7o73pz6i

Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs [article]

Michael Gygli, Mohammad Norouzi, Anelia Angelova
2017 arXiv   pre-print
Once the model is trained, we perform inference by gradient descent on the continuous relaxations of the output variables to find outputs with promising scores from the value network.  ...  When applied to image segmentation, the value network takes an image and a segmentation mask as inputs and predicts a scalar estimating the intersection over union between the input and ground truth masks  ...  Our gradient based inference method iteratively refines segmentation masks to maximize the predicted scores of a deep value network.  ... 
arXiv:1703.04363v2 fatcat:5ow4nbg4krbxpbvk5uqhhemzqe
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