Knowledgeeembedding

Knowledgeeembedding

Knowledge Embedding

News and updates

  • Sept. 26, 2022: Initial webpage rendering!
  • Sept. 28, 2022: Update the document！

Overview

Welcome to the Knowledge Embedding Dataset project!

What is Knowledge Embedding Dataset

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Research Team

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Citations and publications

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References

Knowledge Injection and Visual concept extraction

  • Hu Z, Ma X, Liu Z, et al. Harnessing deep neural networks with logic rules[J]. arXiv preprint arXiv:1603.06318, 2016. paper. code.
  • Ning G, Zhang Z, He Z. Knowledge-guided deep fractal neural networks for human pose estimation[J]. IEEE Transactions on Multimedia, 2017, 20(5): 1246-1259. paper. code.
  • Shen Y, Deng Y, Yang M, et al. Knowledge-aware attentive neural network for ranking question answer pairs[C]//The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 2018: 901-904. paper.
  • Kursuncu U, Gaur M, Sheth A. Knowledge infused learning (k-il): Towards deep incorporation of knowledge in deep learning[J]. arXiv preprint arXiv:1912.00512, 2019. paper.
  • Ge Y, Xiao Y, Xu Z, et al. A peek into the reasoning of neural networks: Interpreting with structural visual concepts[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 2195-2204. paper.
  • Shevchenko V, Teney D, Dick A, et al. Reasoning over vision and language: Exploring the benefits of supplemental knowledge[J]. arXiv preprint arXiv:2101.06013, 2021. paper
  • Sharifzadeh S, Baharlou S M, Tresp V. Classification by attention: Scene graph classification with prior knowledge[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2021, 35(6): 5025-5033. paper

Knowledge Guide

  • Marino K, Salakhutdinov R, Gupta A. The more you know: Using knowledge graphs for image classification[J]. arXiv preprint arXiv:1612.04844, 2016. paper.
  • Marino K, Salakhutdinov R, Gupta A. The more you know: Using knowledge graphs for image classification[J]. arXiv preprint arXiv:1612.04844, 2016. paper.
  • Fang Y, Kuan K, Lin J, et al. Object detection meets knowledge graphs[C]. International Joint Conferences on Artificial Intelligence, 2017. paper.
  • Von Rueden L, Mayer S, Beckh K, et al. Informed Machine Learning–A Taxonomy and Survey of Integrating Knowledge into Learning Systems[J]. arXiv preprint arXiv:1903.12394, 2019. paper. code
  • Kursuncu U, Gaur M, Sheth A. Knowledge infused learning (k-il): Towards deep incorporation of knowledge in deep learning[J]. arXiv preprint arXiv:1912.00512, 2019. paper.
  • Sheth A, Gaur M, Kursuncu U, et al. Shades of knowledge-infused learning for enhancing deep learning[J]. IEEE Internet Computing, 2019, 23(6): 54-63. paper.
  • Chen T, Lin L, Hui X, et al. Knowledge-guided multi-label few-shot learning for general image recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020. paper
  • Zareian A, Karaman S, Chang S F. Bridging knowledge graphs to generate scene graphs[C]//European conference on computer vision. Springer, Cham, 2020: 606-623. paper. code
  • Ge Y, Xiao Y, Xu Z, et al. A peek into the reasoning of neural networks: Interpreting with structural visual concepts[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 2195-2204. paper.
  • Yu F, Tang J, Yin W, et al. Ernie-vil: Knowledge enhanced vision-language representations through scene graphs[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2021, 35(4): 3208-3216. paper.
  • Shevchenko V, Teney D, Dick A, et al. Reasoning over vision and language: Exploring the benefits of supplemental knowledge[J]. arXiv preprint arXiv:2101.06013, 2021. paper.
  • Yu F, Tang J, Yin W, et al. Ernie-vil: Knowledge enhanced vision-language representations through scene graphs[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2021, 35(4): 3208-3216. paper.
  • Sharifzadeh S, Baharlou S M, Tresp V. Classification by attention: Scene graph classification with prior knowledge[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2021, 35(6): 5025-5033. paper
  • Dash, Tirtharaj, et al. “A review of some techniques for inclusion of domain-knowledge into deep neural networks.” Scientific Reports 12.1 (2022): 1-15. paper.

KG-HOI

  • Li Y L, Zhou S, Huang X, et al. Transferable interactiveness knowledge for human-object interaction detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019: 3585-3594. paper. code.
  • Xu B, Wong Y, Li J, et al. Learning to detect human-object interactions with knowledge[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019. paper
  • Zhuo T, Cheng Z, Zhang P, et al. Explainable video action reasoning via prior knowledge and state transitions[C]//Proceedings of the 27th acm international conference on multimedia. 2019: 521-529. paper. code.
  • Kim D J, Sun X, Choi J, et al. Detecting human-object interactions with action co-occurrence priors[C]//European Conference on Computer Vision. Springer, Cham, 2020: 718-736. paper. code.
  • Kim D, Lee G, Jeong J, et al. Tell me what they’re holding: Weakly-supervised object detection with transferable knowledge from human-object interaction[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(07): 11246-11253. paper.
  • Lin X, Zou Q, Xu X, et al. Effects of Motion-Relevant Knowledge From Unlabeled Video to Human-Object Interaction Detection[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021. paper.
  • Hou Z, Yu B, Qiao Y, et al. Affordance transfer learning for human-object interaction detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 495-504. paper. code.
  • Morais R, Le V, Venkatesh S, et al. Learning asynchronous and sparse human-object interaction in videos[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 16041-16050. paper. code.
  • Chen J, Wu X, Hu Y, et al. Spatial-temporal causal inference for partial image-to-video adaptation[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2021, 35(2): 1027-1035. paper. code
  • Yang L, Li K, Zhan X, et al. OakInk: A Large-scale Knowledge Repository for Understanding Hand-Object Interaction[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 20953-20962. paper. code
  • Liu R, Zheng G, Gupta S, et al. Knowledge infused decoding[J]. arXiv preprint arXiv:2204.03084, 2022. paper. code
  • Hua H, Li D, Li R, et al. Towards Explainable Action Recognition by Salient Qualitative Spatial Object Relation Chains[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2022, 36(5): 5710-5718. paper.

MOE

  • Valada A, Dhall A, Burgard W. Convoluted mixture of deep experts for robust semantic segmentation[C]//IEEE/RSJ International conference on intelligent robots and systems (IROS) workshop, state estimation and terrain perception for all terrain mobile robots. 2016, 2. paper. code.
  • Fu H, Gong M, Wang C, et al. MoE-SPNet: A mixture-of-experts scene parsing network[J]. Pattern Recognition, 2018, 84:226-236. paper.
  • Wang X, Yu F, Dunlap L, et al. Deep mixture of experts via shallow embedding[C]//Uncertainty in artificial intelligence. PMLR, 2020: 552-562. paper.
  • Minaee S, Boykov Y Y, Porikli F, et al. Image segmentation using deep learning: A survey[J]. IEEE transactions on pattern analysis and machine intelligence, 2021. paper.
  • Riquelme C, Puigcerver J, Mustafa B, et al. Scaling vision with sparse mixture of experts[J]. Advances in Neural Information Processing Systems, 2021, 34: 8583-8595. paper. code.
  • Fedus W, Dean J, Zoph B. A review of sparse expert models in deep learning[J]. arXiv preprint arXiv:2209.01667, 2022. paper. code
  • Pavlitskaya S, Hubschneider C, Struppek L, et al. Balancing Expert Utilization in Mixture-of-Experts Layers Embedded in CNNs[J]. arXiv preprint arXiv:2204.10598, 2022. paper
  • Fingscheidt T, Gottschalk H, Houben S. Deep Neural Networks and Data for Automated Driving: Robustness, Uncertainty Quantification, and Insights Towards Safety[J]. 2022. paper
  • Ou Y, Yuan Y, Huang X, et al. Patcher: Patch Transformers with Mixture of Experts for Precise Medical Image Segmentation[J]. arXiv preprint arXiv:2206.01741 , 2022. paper. code.

    Sponsors

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Copyright && Maintainer

Copyright (C) 2022 ETVP

Corresponding: Liang Zhang «liangzhang@xidian.edu.cn»

Maintainers: Zhuo Liang