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Found 6 papers, 5 papers with code
Uni-NLX: Unifying Textual Explanations for Vision and Vision-Language Tasks
In this work, we propose Uni-NLX, a unified framework that consolidates all NLE tasks into a single and compact multi-task model using a unified training objective of text generation.
Visualizing and Understanding Contrastive Learning
Contrastive learning has revolutionized the field of computer vision, learning rich representations from unlabeled data, which generalize well to diverse vision tasks.
NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language Tasks
Current NLE models explain the decision-making process of a vision or vision-language model (a. k. a., task model), e. g., a VQA model, via a language model (a. k. a., explanation model), e. g., GPT.
Show, Edit and Tell: A Framework for Editing Image Captions
Specifically, our caption-editing model consisting of two sub-modules: (1) EditNet, a language module with an adaptive copy mechanism (Copy-LSTM) and a Selective Copy Memory Attention mechanism (SCMA), and (2) DCNet, an LSTM-based denoising auto-encoder.
HOW IMPORTANT ARE NETWORK WEIGHTS? TO WHAT EXTENT DO THEY NEED AN UPDATE?
We wish to show that starting from the third epoch, freezing weights which have no informative gradient and are less likely to be changed during training, results in a very slight drop in the overall accuracy (and in sometimes better).
Look and Modify: Modification Networks for Image Captioning
Attention-based neural encoder-decoder frameworks have been widely used for image captioning.
