Omkar2118/datasets huggingface dataset:
FewCLUE

Chinese Few-shot Learning Evaluation Benchmark (FewCLUE) is a comprehensive small sample evaluation benchmark in Chinese. It includes nine tasks, ranging from single-sentence and sentence-pair classification tasks to machine reading comprehension tasks.

Introducing paper: FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark
ZS-F-VQA

The ZS-F-VQA dataset  is a new split of the F-VQA dataset for zero-shot problem.
Firstly we obtain the original train/test split of F-VQA dataset and combine them together to filter out the triples whose answers appear in top-500 according to its occurrence frequency.
Next, we randomly divide this set of answers into new training split (a.k.a. seen) $\mathcal{A}_s$ and testing split (a.k.a. unseen) $\mathcal{A}_u$ at the ratio of 1:1. 
With reference to F-VQA standard dataset, the division process is repeated 5 times. 
For each $(i,q,a)$ triplet in original F-VQA dataset, it is divided into training set if $a \in \mathcal{A}_s$. Else it is divided into testing set.
The overlap of answer instance between training and testing set in F-VQA are $2565$ compared to $0$ in ZS-F-VQA.

Introducing paper: Zero-shot Visual Question Answering using Knowledge Graph
IMC PhotoTourism
Full name: Image Matching Challenge Phototourism

Dataset provided by the Image Matching Workshop

https://www.cs.ubc.ca/research/image-matching-challenge/current/