Search Results for author:
Found 3 papers, 0 papers with code
Partially-Supervised Novel Object Captioning Leveraging Context from Paired Data
PS-NOC is agnostic to model architecture, and primarily focuses on the training approach that uses existing fully paired image-caption data and the images with only the novel object detection labels (partially paired data).
Data augmentation to improve robustness of image captioning solutions
In this paper, we study the impact of motion blur, a common quality flaw in real world images, on a state-of-the-art two-stage image captioning solution, and notice a degradation in solution performance as blur intensity increases.
B-SCST: Bayesian Self-Critical Sequence Training for Image Captioning
The "baseline" for the policy-gradients in B-SCST is generated by averaging predictive quality metrics (CIDEr-D) of the captions drawn from the distribution obtained using a Bayesian DNN model.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
