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Semi-supervised Abstraction-Augmented String Kernel for Multi-level Bio-Relation Extraction [chapter]

Pavel Kuksa, Yanjun Qi, Bing Bai, Ronan Collobert, Jason Weston, Vladimir Pavlovic, Xia Ning
2010 Lecture Notes in Computer Science  
This Abstraction-augmented String Kernel (ASK) allows for better generalization of patterns learned from annotated data and provides a unified framework for solving bRE with multiple degrees of detail.  ...  In this work we introduce a semisupervised approach that can tackle multi-level bRE via string comparisons with mismatches in the string kernel framework.  ...  Related Work Semi-supervised Learning Supervised NLP techniques are restricted by the availability of labeled examples.  ... 
doi:10.1007/978-3-642-15883-4_9 fatcat:n35jjgwqbncj3jhnf6roebh7yy

Special issue on concept detection with big data

Shih-Fu Chang, Thomas S. Huang, Michael S. Lew, Bart Thomee
2015 International Journal of Multimedia Information Retrieval  
One of the grand challenges of machine intelligence and pattern recognition for the past decade has been bridging the semantic gap, that is, determining how to translate the low-level features from images  ...  We received 21 submissions of which 16 were selected for the triple peer-review process.  ...  In the paper, "Large image modality labeling initiative using semi-supervised and optimized clustering" by S. Vajda, D. You, S. Antani and G.  ... 
doi:10.1007/s13735-015-0083-2 fatcat:faywbr2khbhplo7z5wmtoto5ha

The Emerging Trends of Multi-Label Learning [article]

Weiwei Liu, Xiaobo Shen, Haobo Wang, Ivor W. Tsang
2020 arXiv   pre-print
data with limited supervision to build a multi-label classification model becomes valuable for practical applications, etc.  ...  Exabytes of data are generated daily by humans, leading to the growing need for new efforts in dealing with the grand challenges for multi-label learning brought by big data.  ...  Semi-Supervised Multi-Label Classification In semi-supervised MLC (SS-MLC) [117] , the dataset is comprised of two sets: fully labeled data and unlabeled data.  ... 
arXiv:2011.11197v2 fatcat:hu6w4vgnwbcqrinrdfytmmjbjm

Vector space semantics with frequency-driven motifs

Shashank Srivastava, Eduard Hovy
2014 Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)  
In this work, we present a frequencydriven paradigm for robust distributional semantics in terms of semantically cohesive lineal constituents, or motifs.  ...  Traditional models of distributional semantics suffer from computational issues such as data sparsity for individual lexemes and complexities of modeling semantic composition when dealing with structures  ...  Semi-supervised learning: In the semisupervised case, the labels y (k) i are known only for some of the tokens in x (k) .  ... 
doi:10.3115/v1/p14-1060 dblp:conf/acl/SrivastavaH14 fatcat:dsgyalzqyvgyjhrmdzzafvdm3e

Semi-Supervised Convolutional Neural Network for Law Advice Online

Fen Zhao, Penghua Li, Yuanyuan Li, Jie Hou, Yinguo Li
2019 Applied Sciences  
Hence, we present a new semi-supervised model to solve the problem of data annotation.  ...  In addition, to implement the multi-task learning task, we propose the multi-label classification algorithm to assign multiple labels to an instance.  ...  Scheme We propose an SSC framework for multi-label classification, which effectively learns the semantic information of text data.  ... 
doi:10.3390/app9173617 fatcat:oikzq5pxurfflm5hwsax5sv4ya

Bridging Gap between Image Pixels and Semantics via Supervision: A Survey [article]

Jiali Duan, C.-C. Jay Kuo
2022 arXiv   pre-print
The fact that there exists a gap between low-level features and semantic meanings of images, called the semantic gap, is known for decades. Resolution of the semantic gap is a long standing problem.  ...  Most importantly, we claim that the semantic gap is primarily bridged through supervised learning today.  ...  Sparse kernel learning for image annotation.  ... 
arXiv:2107.13757v3 fatcat:dw4c74c3h5bvlmzmxugeh5aela

Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective [article]

Jing Zhang and Wanqing Li and Philip Ogunbona and Dong Xu
2019 arXiv   pre-print
Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes.  ...  up for a possible solution accordingly.  ...  Problem notations: P3.1, supervised domain adaptation (DA); P3.2, Semi-supervised DA; P3.3, Unsupervised DA; P3.4, Supervised online DA; P3.5, Supervised online DA; P3.6, Unsupervised online DA; P3.7,  ... 
arXiv:1705.04396v3 fatcat:iknfmppi5zca7ljovdlwvdwluu

Semi Supervised Deep Quick Instance Detection and Segmentation [article]

Ashish Kumar, L. Behera
2021 arXiv   pre-print
In this paper, we present a semi supervised deep quick learning framework for instance detection and pixel-wise semantic segmentation of images in a dense clutter of items.  ...  There are broadly four key components of the proposed framework semi supervised labeling, occlusion aware clutter synthesis, a customized convolutional neural network head, and instance detection.  ...  (e) Semi supervised labeling for mask, and (f) box labeling. Generated synthetic clutter and color coded labels in short durations, similar to ARC'17.  ... 
arXiv:2101.06405v1 fatcat:tqd3wnp7zrft5j6vriepkfgj7m

A Survey on Machine Learning Techniques for Auto Labeling of Video, Audio, and Text Data [article]

Shikun Zhang, Omid Jafari, Parth Nagarkar
2021 arXiv   pre-print
In this survey paper, we provide a review of previous techniques that focuses on optimized data annotation and labeling for video, audio, and text data.  ...  Therefore, researchers started to pay more attention on auto annotation and labeling.  ...  [83] proposes Semi-supervised Learning by Kernel Density Estimation (SSLKDE).  ... 
arXiv:2109.03784v1 fatcat:uu55zfmtajcvdjekxeaue76izy

Adaptive Graph Guided Embedding for Multi-label Annotation

Lichen Wang, Zhengming Ding, Yun Fu
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
To this end, we propose a novel Adaptive Graph Guided Embedding (AG2E) approach for multi-label annotation in a semi-supervised fashion, which utilizes limited labeled data associating with large-scale  ...  Multi-label annotation is challenging since a large amount of well-labeled training data are required to achieve promising performance.  ...  Different from previous work, we deploy an adaptive graph for semi-supervised multi-label learning.  ... 
doi:10.24963/ijcai.2018/388 dblp:conf/ijcai/WangDF18 fatcat:vwrtxt5lfnbfjoeiq2tfmalk6e

InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization [article]

Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, Jian Tang
2020 arXiv   pre-print
Furthermore, we further propose InfoGraph*, an extension of InfoGraph for semi-supervised scenarios.  ...  As a result, the supervised encoder learns from unlabeled data while preserving the latent semantic space favored by the current supervised task.  ...  However, one of the most difficult obstacles for supervised learning on graphs is that it is often very costly or even impossible to collect annotated labels.  ... 
arXiv:1908.01000v3 fatcat:hty2enrcivgttoafunwv2ajjma

Domain Adaptation for Visual Applications: A Comprehensive Survey [article]

Gabriela Csurka
2017 arXiv   pre-print
Second, we try to address and analyze briefly the state-of-the-art methods for different types of scenarios, first describing the historical shallow methods, addressing both the homogeneous and the heterogeneous  ...  To overcome the burden of annotation, alternative solutions have been proposed in the literature in order to exploit the unlabeled data (referred to as semi-supervised learning), or data and/or models  ...  DA related to traditional ML methods Semi-supervised learning.  ... 
arXiv:1702.05374v2 fatcat:5va4oz4evjfhxgxddflpbb6pxi

Boosting Video Object Segmentation based on Scale Inconsistency [article]

Hengyi Wang, Changjae Oh
2022 arXiv   pre-print
We further present a self-supervised online adaptation, tailored for test-time optimization, that bootstraps the predictions without ground-truth masks based on the scale inconsistency.  ...  We present a refinement framework to boost the performance of pre-trained semi-supervised video object segmentation (VOS) models.  ...  We presented a model-agnostic refinement framework for semi-supervised VOS models.  ... 
arXiv:2205.01197v1 fatcat:7br3gnstffbv3p65qu7odowzhq

Tags2Parts: Discovering Semantic Regions from Shape Tags

Sanjeev Muralikrishnan, Vladimir G. Kim, Siddhartha Chaudhuri
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
We test our method on segmentation benchmarks and show that even with weak supervision of whole shape tags, our method can infer meaningful semantic regions, without ever observing shape segmentations.  ...  As a bonus, our architecture is directly operational under full supervision and performs strongly on standard benchmarks.  ...  There are several avenues for future work enabled by unstructured user annotations in public online 3D repositories.  ... 
doi:10.1109/cvpr.2018.00309 dblp:conf/cvpr/MuralikrishnanK18 fatcat:umn5zvwxerevlocfrijmu67w3u

Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition

Jing Zhang, Wanqing Li, Philip Ogunbona, Dong Xu
2019 ACM Computing Surveys  
Specifically, it categorises the cross-dataset recognition into 17 problems based on a set of carefully chosen data and label attributes.  ...  Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes.  ...  Problem notations: P3.1, supervised domain adaptation (DA); P3.2, Semi-supervised DA; P3.3, Unsupervised DA; P3.4, Supervised online DA; P3.5, Supervised online DA; P3.6, Unsupervised online DA; P3.7,  ... 
doi:10.1145/3291124 fatcat:thjzho3xsnfalprmkquldhwpvm
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