Omkar2118/datasets huggingface dataset:
HUST-LEBW

An eyeblink detection in the wild dataset.

Introducing paper: Towards Real-time Eyeblink Detection in The Wild:Dataset,Theory and Practices
MUSIC-AVQA-R

We introduce the first dataset, ***MUSIC-AVQA-R***, to evaluate the robustness of AVQA models. The construction of this dataset involves two key processes: *rephrasing* and *splitting*. The former involves the rephrasing of questions in the test split of [***MUSIC-AVQA***](https://github.com/GeWu-Lab/MUSIC-AVQA), and the latter is dedicated to the categorization of questions into frequent (head) and rare (tail) subset. 

We followed the previous work in partitioning the dataset into "head" and "tail" categories. Based on the number of answers in the dataset, answers with a count greater than $1.2$ times the mean, denoted as $\mu(a)$, were categorized as "head" while those with counts less than  $1.2\mu(a)$ were categorized as "tail"

Introducing paper: Look, Listen, and Answer: Overcoming Biases for Audio-Visual Question Answering
FortisAVQA

We introduce FortisAVQA, a dataset designed to assess the robustness of AVQA models. Its construction involves two key processes: rephrasing and splitting. Rephrasing modifies questions from the test set of MUSIC-AVQA to enhance linguistic diversity, thereby mitigating the reliance of models on spurious correlations between key question terms and answers. Splitting entails the automatic and reasonable categorization of questions into frequent (head) and rare (tail) subsets, enabling a more comprehensive evaluation of model performance in both in-distribution and out-of-distribution scenarios.
CASTLE Benchmark
Full name: CASTLE Benchmark C@250

The CASTLE Benchmark is a comprehensive dataset and a scoring method for evaluating single or combinations of static analyzers with a focus on security. It consists of a hand-crafted dataset of 250 micro-benchmark programs (almost 11,000 lines of C code), covering 25 common CWEs. We also introduce the novel CASTLE Score metric to enable fair and reliable comparisons, considering factors such as true positive and false positive rates, as well as the tools' ability to find more common issues. This dataset enables a comparison of single tools, as well as the effectiveness of tool combinations.

Introducing paper: CASTLE: Benchmarking Dataset for Static Code Analyzers and LLMs towards CWE Detection