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Affordances are a fundamental concept in robotics since they relate available actions for an agent depending on its sensory-motor capabilities and the environment.
1950
Earlier work this paper cites.
J. J. Gibson,
2014
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in
2014
Earlier work this paper cites.
R. Margolin, L. Zelnik-Manor, and A. Tal, “How to evaluate foreground maps?” in
2014
Earlier work this paper cites.
A. Myers, C. L. Teo, C. Fermüller, and Y. Aloimonos, “Affordance detection of tool parts from geometric features,” in
2015
Earlier work this paper cites.
M. P. Naeini, G. Cooper, and M. Hauskrecht, “Obtaining well calibrated probabilities using bayesian binning,” in
2015
Earlier work this paper cites.
L. Jamone, E. Ugur, A. Cangelosi, L. Fadiga, A. Bernardino, J. Piater, and J. Santos-Victor, “Affordances in psychology, neuroscience, and robotics: A survey,”
2016
Earlier work this paper cites.
A. Nguyen, D. Kanoulas, D. G. Caldwell, and N. G. Tsagarakis, “Detecting object affordances with convolutional neural networks,” in
2016
Earlier work this paper cites.
Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in
2016
Earlier work this paper cites.
A. Nguyen, D. Kanoulas, D. G. Caldwell, and N. Tsagarakis, “Object-based affordances detection with convolutional neural networks and dense conditional random fields,” in
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in
2017
Cited alongside, same era.
S. Thermos, G. T. Papadopoulos, P. Daras, and G. Potamianos, “Deep affordance-grounded sensorimotor object recognition,” in
2017
Cited alongside, same era.
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, “On calibration of modern neural networks,” in
2017
Cited alongside, same era.
T.-T. Do, A. Nguyen, and I. Reid, “Affordancenet: An end-to-end deep learning approach for object affordance detection,” in
2018
Cited alongside, same era.
C. N. D. Minh, S. Z. Gilani, S. M. S. Islam, and D. Suter, “Learning affordance segmentation: An investigative study,” in
2020
Later among the works it cites.
F. K. Gustafsson, M. Danelljan, and T. B. Schon, “Evaluating scalable bayesian deep learning methods for robust computer vision,” in
2020
Later among the works it cites.
A. Harakeh, M. Smart, and S. L. Waslander, “Bayesod: A bayesian approach for uncertainty estimation in deep object detectors,” in
2020
Later among the works it cites.
Y. Kwon, J.-H. Won, B. J. Kim, and M. C. Paik, “Uncertainty quantification using bayesian neural networks in classification: Application to biomedical image segmentation,”
2020
Later among the works it cites.
D. Hall, F. Dayoub, J. Skinner, H. Zhang, D. Miller, P. Corke, G. Carneiro, A. Angelova, and N. Sünderhauf, “Probabilistic object detection: Definition and evaluation,” in
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D. Miller, L. Nicholson, F. Dayoub, and N. Sünderhauf, “Dropout sampling for robust object detection in open-set conditions,” in
2018
Cited alongside, same era.
L. Neumann, A. Zisserman, and A. Vedaldi, “Relaxed softmax: Efficient confidence auto-calibration for safe pedestrian detection,” 2018
2018
Cited alongside, same era.
E. Ilg, O. Cicek, S. Galesso, A. Klein, O. Makansi, F. Hutter, and T. Brox, “Uncertainty estimates and multi-hypotheses networks for optical flow,” in
2018
Cited alongside, same era.
D. Morrison, A. Milan, and E. Antonakos, “Uncertainty-aware instance segmentation using dropout sampling,” in
2019
Cited alongside, same era.
D. Miller, F. Dayoub, M. Milford, and N. Sünderhauf, “Evaluating merging strategies for sampling-based uncertainty techniques in object detection,” in
2019
Cited alongside, same era.
2020
Later among the works it cites.
2021
Later among the works it cites.
D. Feng, Z. Wang, Y. Zhou, L. Rosenbaum, F. Timm, K. Dietmayer, M. Tomizuka, and W. Zhan, “Labels are not perfect: Inferring spatial uncertainty in object detection,”
2021
Later among the works it cites.
D. Miller, N. Sünderhauf, M. Milford, and F. Dayoub, “Uncertainty for identifying open-set errors in visual object detection,”
2021
Later among the works it cites.
J. Rodríguez-Puigvert, R. Martínez-Cantín, and J. Civera, “Bayesian deep neural networks for supervised learning of single-view depth,”
2022
Later among the works it cites.