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Found 11 papers, 3 papers with code
Image Captioning as an Assistive Technology: Lessons Learned from VizWiz 2020 Challenge
Image captioning has recently demonstrated impressive progress largely owing to the introduction of neural network algorithms trained on curated dataset like MS-COCO.
Alleviating Noisy Data in Image Captioning with Cooperative Distillation
Image captioning systems have made substantial progress, largely due to the availability of curated datasets like Microsoft COCO or Vizwiz that have accurate descriptions of their corresponding images.
Tabular Transformers for Modeling Multivariate Time Series
This results in two architectures for tabular time series: one for learning representations that is analogous to BERT and can be pre-trained end-to-end and used in downstream tasks, and one that is akin to GPT and can be used for generation of realistic synthetic tabular sequences.
Learning Implicit Text Generation via Feature Matching
Generative feature matching network (GFMN) is an approach for training implicit generative models for images by performing moment matching on features from pre-trained neural networks.
Nano-Material Configuration Design with Deep Surrogate Langevin Dynamics
We consider the problem of optimizing by sampling under multiple black-box constraints in nano-material design.
Surrogate-Based Constrained Langevin Sampling With Applications to Optimal Material Configuration Design
We consider the problem of generating configurations that satisfy physical constraints for optimal material nano-pattern design, where multiple (and often conflicting) properties need to be simultaneously satisfied.
Adversarial Semantic Alignment for Improved Image Captions
When evaluated on OOC and MS-COCO benchmarks, we show that SCST-based training has a strong performance in both semantic score and human evaluation, promising to be a valuable new approach for efficient discrete GAN training.
Generative Feature Matching Networks
We propose a non-adversarial feature matching-based approach to train generative models.
Learning Implicit Generative Models by Matching Perceptual Features
Perceptual features (PFs) have been used with great success in tasks such as transfer learning, style transfer, and super-resolution.
Improved Adversarial Image Captioning
In this paper we study image captioning as a conditional GAN training, proposing both a context-aware LSTM captioner and co-attentive discriminator, which enforces semantic alignment between images and captions.
Wasserstein Barycenter Model Ensembling
In this paper we propose to perform model ensembling in a multiclass or a multilabel learning setting using Wasserstein (W.) barycenters.
