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Learning One-Shot Imitation from Humans without Humans
Alessandro Bonardi, Stephen James, Andrew J. Davison
Videos can be found here: this https URL
https://arxiv.org/abs/1911.01103
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Thanks for your wonderful work, it is very interesting and helpful to my research.
Therefore, I want to apply the other few-shot learning pre-trained weights not only 16-shot as follows:
논문을 소개드리겠습니다. 제목에서 유추가능하듯 Prototype을 조정하여 Few-Shot learning을 …
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## Problem statement
1. Despite the impressive capabilities of large scale language models, the potential to modalities has not been fully demonstrated other than text.
2. Aligning parameters of vi…
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### Model description
Atlas is a retrieval-augmented seq2seq language model comprised of a Contriever retriever and fusion-in-decoder (FID) architecture (which uses T5), introduced in the paper [At…
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Hi, thanks for the great work!
I wonder how can I prompt BLIP2 and InstructBLIP to do few-shot in-context learning, e.g. few-shot VQA. Specifically, I want to have the input like `[Img] [QA1] [Img]…
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I want to add a Cricket Shots Detection Project with Deep Learning methodologies , This Project will be a great addition to the repository.
The project focuses on classification of different cricke…
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## Feature request
After semi-supervised pretraining, can we do light-weighted fine-tunning or few-shot learning instead of classification?
**What is the expected behavior?**
Instead of fine-…