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Dense Gaussian Processes for Few-Shot Segmentation [article]

Joakim Johnander, Johan Edstedt, Michael Felsberg, Fahad Shahbaz Khan, Martin Danelljan
2021 arXiv   pre-print
To this end, we propose a few-shot segmentation method based on dense Gaussian process (GP) regression.  ...  Few-shot segmentation is a challenging dense prediction task, which entails segmenting a novel query image given only a small annotated support set.  ...  The computations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC), partially funded by the Swedish Research Council through grant agreement no. 2018-05973.  ... 
arXiv:2110.03674v1 fatcat:7updqeutjnfstdipenektmoamq

Doubly Deformable Aggregation of Covariance Matrices for Few-shot Segmentation [article]

Zhitong Xiong, Haopeng Li, Xiao Xiang Zhu
2022 arXiv   pre-print
For the few-shot segmentation task, the main challenge is how to accurately measure the semantic correspondence between the support and query samples with limited training data.  ...  Specifically, in this work, we first devise a novel hard example mining mechanism to learn covariance kernels for the Gaussian process.  ...  Details of Gaussian Process Gaussian process (GP) is a non-linear and non-parametric Bayesian model for regression and classification [38] .  ... 
arXiv:2208.00306v1 fatcat:nx4nghitc5bq3pdhwhhm54gfnu

Guided Networks for Few-Shot Image Segmentation and Fully Connected CRFs

Kun Zhang, Yuanjie Zheng, Xiaobo Deng, Weikuan Jia, Jian Lian, Xin Chen
2020 Electronics  
Structured output tasks like segmentation are challenging for few-shot learning, due to their being high-dimensional and statistically dependent.  ...  The goal of the few-shot learning method is to learn quickly from a low-data regime.  ...  In addition, they need to optimize for few-shot usage during the inference process.  ... 
doi:10.3390/electronics9091508 fatcat:taoh526ygjddpnfwymmzwar7su

Dense Rigid Reconstruction from Unstructured Discontinuous Video

Karel Lebeda, Simon Hadfield, Richard Bowden
2015 2015 IEEE International Conference on Computer Vision Workshop (ICCVW)  
The technique is robust against distractors in the scene, background clutter and even shot cuts.  ...  We present a novel algorithm for modelling 3D shapes from unstructured, unconstrained discontinuous footage.  ...  Coarse Online Modelling For online target/background segmentation, we train a Gaussian Process (GP) as our coarse, probabilistic model.  ... 
doi:10.1109/iccvw.2015.110 dblp:conf/iccvw/LebedaHB15 fatcat:uoadiaudczckxgvqo7fzk4w3ti

JUMAS @ TRECVID 2010

Bálint Daróczy, Dávid Márk Nemeskey, Róbert Pethes, István Petrás, András A. Benczúr, Daniele Falavigna, Roberto Gretter
2010 TREC Video Retrieval Evaluation  
For Instance Search we submitted a single run where we used the Dutch translation of the queries to retrieve the ASR text. This run reached an AP sum of 0.508 for the 22 topics.  ...  We used the the video processing subsystem developed for TRECVID 2009 tasks [1] . We computed a grayscale HOG descriptor on a dense grid for each image.  ...  For SIX this formula scores videos. For CLEF features, we obtained prediction for shots and aggregated for videos by averaging after skipping the first and last 5% of the video shots.  ... 
dblp:conf/trecvid/DaroczyNPPBFG10 fatcat:t7z4m4bs6jealgetkddhn2lgfy

TokyoTech at TRECVID 2015

Nakamasa Inoue, Hai Dang Tran, Ryosuke Yamamoto, Koichi Shinoda
2015 TREC Video Retrieval Evaluation  
such as optical-flow value similarity for temporal segmentation.  ...  It uses optical-flow features in addition to color or texture features for video segmentation to produce temporally continuous candidate bounding-boxes for object detection.  ...  Our future work will focus on deep learning techniques such as deep convolutional neural networks for event detection.  ... 
dblp:conf/trecvid/InoueTYS15 fatcat:v6yhwbtk5nbalbksyb7dyumn24

Prototype Mixture Models for Few-shot Semantic Segmentation [article]

Boyu Yang, Chang Liu, Bohao Li, Jianbin Jiao, Qixiang Ye
2020 arXiv   pre-print
Few-shot segmentation is challenging because objects within the support and query images could significantly differ in appearance and pose.  ...  Particularly, PMMs improve 5-shot segmentation performance on MS-COCO by up to 5.82\% with only a moderate cost for model size and inference speed.  ...  aggregation [1] , with few-shot segmentation.  ... 
arXiv:2008.03898v2 fatcat:gzejkdc7xbenjpxrhd6oqio6v4

Table of contents

2018 IEEE Transactions on Image Processing  
Ambai 5378 A Unified Approach for Conventional Zero-Shot, Generalized Zero-Shot, and Few-Shot Learning ...................... ...........................................................................  ...  Bronstein 5707 Multiresolution Processing of Images and Video Photo Realistic Image Completion via Dense Correspondence ........................... J.-J. Huang and P. L.  ... 
doi:10.1109/tip.2018.2871548 fatcat:bbsllfph2bcv5lf5wcr4vgedoe

Uncertainty-Aware Semi-Supervised Few Shot Segmentation [article]

Soopil Kim, Philip Chikontwe, Sang Hyun Park
2021 arXiv   pre-print
Few shot segmentation (FSS) aims to learn pixel-level classification of a target object in a query image using only a few annotated support samples.  ...  We employ the uncertainty estimates to exclude predictions with high degrees of uncertainty for pseudo label construction to obtain additional prototypes based on the refined pseudo labels.  ...  Related Works Few Shot Semantic Segmentation Existing few-shot segmentation (FSS) models use the metalearning framework via task-based episodic training of support and query images.  ... 
arXiv:2110.08954v1 fatcat:idnt7mnmnfh2di73yqpo2juww4

Objectness-Aware Few-Shot Semantic Segmentation [article]

Yinan Zhao, Brian Price, Scott Cohen, Danna Gurari
2021 arXiv   pre-print
Few-shot semantic segmentation models aim to segment images after learning from only a few annotated examples.  ...  A key challenge for them is how to avoid overfitting because limited training data is available.  ...  Few-Shot Semantic Segmentation Results: Results for 1-shot and 5-shot segmentation are shown in Table 1 .  ... 
arXiv:2004.02945v3 fatcat:7ltlhinjsfd3vks5demzdlqdma

One Shot Learning for Generic Instance Segmentation in RGBD Videos

Xiao Lin, Josep Casas, Montse Pardàs
2019 Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications  
instance samples via CNNs to generate robust features for instance segmentation.  ...  In practice, a classical generic instance segmentation method is employed to initially detect object instances and build temporal correspondences, whereas instance models are trained based on the few detected  ...  However, this advantage of CNNs becomes an obstacle in the training process when only few annotation is provided.  ... 
doi:10.5220/0007259902330239 dblp:conf/visapp/0003CP19 fatcat:gah4s4mhkrbqtc52dhigzje6jy

Contrastive Enhancement Using Latent Prototype for Few-Shot Segmentation [article]

Xiaoyu Zhao, Xiaoqian Chen, Zhiqiang Gong, Wen Yao, Yunyang Zhang, Xiaohu Zheng
2022 arXiv   pre-print
Few-shot segmentation enables the model to recognize unseen classes with few annotated examples.  ...  Extensive experiments show our approach remarkably improves the performance of state-of-the-art methods for 1-shot and 5-shot segmentation, especially outperforming baseline by 5.9% and 7.3% for 5-shot  ...  Few-Shot Segmentation Few-Shot Segmentation methods are mainly based on metric learning and extend from fewshot image classification methods. [6] early formulated n-way k-shot few-shot semantic segmentation  ... 
arXiv:2203.04095v1 fatcat:s7bdq6cshbcz5mgvio2cofw2we

Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation

Ping Hu, Stan Sclaroff, Kate Saenko
2020 Neural Information Processing Systems  
Zero-shot semantic segmentation (ZSS) aims to classify pixels of novel classes without training examples available.  ...  Specifically, we model the network outputs with Gaussian and Laplacian distributions, with the variances accounting for the observation noise and uncertainty of input samples.  ...  Our work aims to learn reliable models for zero-shot semantic segmentation.  ... 
dblp:conf/nips/HuSS20 fatcat:rn222afzmfdo3fntksbyoky2ry

A Few-Shot Sequential Approach for Object Counting [article]

Negin Sokhandan, Pegah Kamousi, Alejandro Posada, Eniola Alese, Negar Rostamzadeh
2020 arXiv   pre-print
This process is employed on an adapted prototypical-based few-shot approach that uses the extracted features to classify each one either as one of the classes present in the support set images or as background  ...  In this work, we address the problem of few-shot multi-class object counting with point-level annotations.  ...  Few-shot counting, detection and segmentation: the majority of approaches in few-shot learning focus on the problem of object classification.  ... 
arXiv:2007.01899v2 fatcat:q52g6jnbujasxandw3mw4zuc5a

PDFNet: Pointwise Dense Flow Network for Urban-Scene Segmentation [article]

Venkata Satya Sai Ajay Daliparthi
2021 arXiv   pre-print
While regularization techniques come at minimal cost, the collection of labeled data is an expensive and laborious process.  ...  The extensive experiments on Cityscapes and CamVid benchmarks demonstrate that our method significantly outperforms baselines in capturing small classes and in few-data regimes.  ...  1 The encoder connections in the PDFNet are similar to the full dense connectivity pattern introduced in CondenseNet [64].  ... 
arXiv:2109.10083v1 fatcat:4zzppv7iuzelxiqu2i6h5fdiey
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