The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Probabilistic framework for solving visual dialog
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Ensembles of DNNs that belong to the mixture models class can be employed to quantify prediction uncertainty.
“The ensembles of DNNs that belong to the mixture models class can be employed to quantify the prediction uncertainty [471].”
From A review of uncertainty quantification in deep learning: Techniques, applications and challenges (2021) · cites this paper as [471]
The Background section (p. 7) describes explicit ensembling as a way to approximate a Bayesian neural network’s predictive posterior, and the paper measures prediction uncertainty. It does not identify those ensembles as belonging to a mixture-model class, so that part of the claim is unsupported. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y., & Ghahramani, Z. (2015) · arXiv · doi:10.48550/arxiv.1506.02142 · Cited by 6,471
Weight Uncertainty in Neural Networks
Blundell, C., Cornebise, J., Kavukcuoglu, K., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1505.05424 · Cited by 1,342
Keeping the neural networks simple by minimizing the description length of the weights
Hinton, G. E., & van Camp, D. (1993) · Proceedings of the sixth annual conference on Computational learning theory - COLT '93 · doi:10.1145/168304.168306 · Cited by 994
Flagged first, then by citations
Research works printed but not linked: 15
- Zhao, 2019, Object detection with deep learning: a review, IEEE Trans. Neural Netw. Learn. Syst., 10.1109/TNNLS.2018.2876865 · DOI · Paper page
- Deng, 2020, Image captioning using densenet network and adaptive attention, Signal Process. Image Commun., 115836, 10.1016/j.image.2020.115836 · DOI
- Wei, 2019, Multi-attention generative adversarial network for image captioning, Neurocomputing
- Ruwa, 2019, Triple attention network for sentimental visual question answering, Comput. Vision Image Understand., 189, 102829, 10.1016/j.cviu.2019.102829 · DOI
- Li, 2020, Visual question answering with attention transfer and a cross-modal gating mechanism, Pattern Recognit. Lett.
- Zhang, 2019, Multi-modal fusion with multi-level attention for visual dialog, Informat. Process. Manag., 102152
- Srivastava, 2014, Dropout: a simple way to prevent neural networks from overfitting, J. Mach. Learn. Res., 15, 1929
- Liu, 2018, Learning structures of interval-based bayesian networks in probabilistic generative model for human complex activity recognition, Pattern Recognit., 81, 545, 10.1016/j.patcog.2018.04.022 · DOI
- Huang, 2015, A probabilistic topic model for clinical risk stratification from electronic health records, J. Biomed. Inform., 58, 28, 10.1016/j.jbi.2015.09.005 · DOI
- Vulić, 2015, Probabilistic topic modeling in multilingual settings: an overview of its methodology and applications, Informat. Process. Manag., 51, 111, 10.1016/j.ipm.2014.08.003 · DOI
- Jiang, 2017, Learning and inference in knowledge-based probabilistic model for medical diagnosis, Knowl. Based Syst., 138, 58, 10.1016/j.knosys.2017.09.030 · DOI
- Bishop, 2006, Pattern recognition and machine learning, J. Electron. Imag., 16, 140
- Wu, 2015, Towards dropout training for convolutional neural networks, Neural Netw., 71, 1, 10.1016/j.neunet.2015.07.007 · DOI
- Fortunato, 2017, Bayesian recurrent neural networks, arXiv preprint arXiv:1704.02798 · arXiv
- Kingma, 2014, Auto-encoding variational bayes, Stat, 1050, 10
Cited by
Sorted by citations
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., et al. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.008 · Cited by 3,111
“[371] presented a probabilistic approach for solving the task of ‘Visual Dialog’.”
Method
Metadata
- Authors
- Patro, B. N., Anupriy, & Namboodiri, V. P.
- Affiliations
- Indian Institute of Technology Kanpur (IN)
- Corresponding author
- Patro, Badri N.
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2021-02
- Volume
- 110
- Pages
- 107586
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2972789567
- Cited by
- 4
- References
- 5
- Citations by year
- 2021: 2
- FWCI
- 0.19
- Type
- journal article
- Language
- en
- Open access
- Licence
- cc-by-nc-nd
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Pattern Recognition (publishedVersion)
- Pure (University of Bath) (publishedVersion, cc-by-nc-nd)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- visual dialogue, probabilistic representation, uncertainty estimation, latent representations, explainable AI, visual language understanding, commonsense knowledge, multimodal deep learning, question answering
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
