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Found 5 papers, 2 papers with code
Convolutional Prompting meets Language Models for Continual Learning
Continual Learning (CL) enables machine learning models to learn from continuously shifting new training data in absence of data from old tasks.
Exemplar-Free Continual Transformer with Convolutions
Although there have been some recent CL approaches for vision transformers, they either store training instances of previous tasks or require a task identifier during test time, which can be limiting.
Few-Shot Visual Question Generation: A Novel Task and Benchmark Datasets
In this paper, we propose a new and challenging Few-Shot Visual Question Generation (FS-VQG) task and provide a comprehensive benchmark to it.
An Unsupervised Normalization Algorithm for Noisy Text: A Case Study for Information Retrieval and Stance Detection
To enable tasks such as search/retrieval and classification over all the available data, we need robust algorithms for text normalization, i. e., for cleaning different kinds of noise in the text.
Information Retrieval
Optical Character Recognition (OCR)
+2
ZSCRGAN: A GAN-based Expectation Maximization Model for Zero-Shot Retrieval of Images from Textual Descriptions
Most existing algorithms for cross-modal Information Retrieval are based on a supervised train-test setup, where a model learns to align the mode of the query (e. g., text) to the mode of the documents (e. g., images) from a given training set.
