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Found 10 papers, 5 papers with code
D3: Data Diversity Design for Systematic Generalization in Visual Question Answering
We demonstrate that this result is independent of the similarity between the training and testing data and applies to well-known families of neural network architectures for VQA (i. e. monolithic architectures and neural module networks).
Semi-Supervised 3D Hand Shape and Pose Estimation with Label Propagation
To obtain 3D annotations, we are restricted to controlled environments or synthetic datasets, leading us to 3D datasets with less generalizability to real-world scenarios.
CogSense: A Cognitively Inspired Framework for Perception Adaptation
This paper proposes the CogSense system, which is inspired by sense-making cognition and perception in the mammalian brain to perform perception error detection and perception parameter adaptation using probabilistic signal temporal logic.
Few-shot Weakly-Supervised Object Detection via Directional Statistics
When the novel objects are localized, we utilize them to learn a linear appearance model to detect novel classes in new images.
Calibration of Neural Networks using Splines
From this, by approximating the empirical cumulative distribution using a differentiable function via splines, we obtain a recalibration function, which maps the network outputs to actual (calibrated) class assignment probabilities.
Post-hoc Calibration of Neural Networks by g-Layers
Calibration of neural networks is a critical aspect to consider when incorporating machine learning models in real-world decision-making systems where the confidence of decisions are equally important as the decisions themselves.
Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization
Weakly Supervised Object Localization (WSOL) methods only require image level labels as opposed to expensive bounding box annotations required by fully supervised algorithms.
Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks
A common approach is to learn a post-hoc calibration function that transforms the output of the original network into calibrated confidence scores while maintaining the network's accuracy.
Learning to Find Common Objects Across Few Image Collections
We model the selection as an energy minimization problem with unary and pairwise potential functions.
Predicting protein-protein interactions based on rotation of proteins in 3D-space
Therefore, a unique category of prediction approaches has been devised which is based on the protein sequence information.
