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Found 9 papers, 0 papers with code
Spatial Reasoning for Few-Shot Object Detection
Although modern object detectors rely heavily on a significant amount of training data, humans can easily detect novel objects using a few training examples.
GC-TTS: Few-shot Speaker Adaptation with Geometric Constraints
While numerous attempts have been made to the few-shot speaker adaptation system, there is still a gap in terms of speaker similarity to the target speaker depending on the amount of data.
Weakly Supervised Thoracic Disease Localization via Disease Masks
To enable a deep learning-based system to be used in the medical domain as a computer-aided diagnosis system, it is essential to not only classify diseases but also present the locations of the diseases.
Visual Question Answering based on Local-Scene-Aware Referring Expression Generation
We quantitatively and qualitatively evaluated the proposed method on the VQA v2 dataset and compared it with state-of-the-art methods in terms of answer prediction.
Few-Shot Object Detection via Knowledge Transfer
If there are only a few training data and annotations, the object detectors easily overfit and fail to generalize.
Counterfactual Explanation Based on Gradual Construction for Deep Networks
The masking step aims to select an important feature from the input data to be classified as a target class.
Self-Augmentation: Generalizing Deep Networks to Unseen Classes for Few-Shot Learning
Few-shot learning aims to classify unseen classes with a few training examples.
Few-Shot Learning with Geometric Constraints
We assume a network trained for base categories with a large number of training examples, and we aim to add novel categories to it that have only a few, e. g., one or five, training examples.
Interpreting Undesirable Pixels for Image Classification on Black-Box Models
To tackle this issue, in this paper, we propose an explanation method that visualizes undesirable regions to classify an image as a target class.
