Omkar2118/datasets huggingface dataset:
Uncertainty Quantification for Underwater Object Segmentation

This dataset extends the  [Semantic Segmentation of Underwater Imagery: Dataset and Benchmark](https://paperswithcode.com/paper/semantic-segmentation-of-underwater-imagery), adding an uncertainty evaluation component. 
To facilitate uncertainty analysis, the test set incorporates a comprehensive range of perturbations, inspired by [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://arxiv.org/abs/1903.12261), applied at four intensity levels. These perturbations, which preserve the original ground truth labels, encompass variations in Brightness and Contrast (simulating diverse lighting and object coloration), Gaussian and Shot Noise (reflecting low-light and discrete light properties), and Impulse Noise (resulting from bit errors). Additionally, Defocus, Motion, and Zoom Blurs are included, along with Elastic Transformations, Pixelation from upscaling, and JPEG Compression artifacts. This enhanced dataset enables an in-depth evaluation of model robustness, providing valuable insights into performance under a wide range of challenging, real-world underwater conditions.

Introducing paper: CVAE-SM: A Conditional Variational Autoencoder with Style Modulation for Efficient Uncertainty Quantification
Multi-IF
Full name: Multi-turn and multilingual instruction following

We introduce Multi-IF, a new benchmark designed to assess LLMs' proficiency in following multi-turn and multilingual instructions. Multi-IF, which utilizes a hybrid framework combining LLM and human annotators, expands upon the IFEval by incorporating multi-turn sequences and translating the English prompts into another 7 languages, resulting in a dataset of 4501 multilingual conversations, where each has three turns. Our evaluation of 14 state-of-the-art LLMs on Multi-IF reveals that it presents a significantly more challenging task than existing benchmarks. All the models tested showed a higher rate of failure in executing instructions correctly with each additional turn. For example, o1-preview drops from 0.877 at the first turn to 0.707 at the third turn in terms of average accuracy over all languages. Moreover, languages with non-Latin scripts (Hindi, Russian, and Chinese) generally exhibit higher error rates, suggesting potential limitations in the models’ multilingual capabilities.

Introducing paper: Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Following
SynMirror

SynMirror consists of samples rendered from 3D assets of two widely used 3D object datasets - Objaverse and Amazon Berkeley Objects (ABO) placed in front of a mirror in a virtual blender environment. The total number of rendered samples are $198,204$. Each rendering contains colors, category_id_segmaps, depth, normals and cam_states.

Introducing paper: Reflecting Reality: Enabling Diffusion Models to Produce Faithful Mirror Reflections
ComplexCodeEval

# ComplexCodeEval

ComplexCodeEval is an evaluation benchmark designed to accommodate multiple downstream tasks, accurately reflect different programming environments, and deliberately avoid data leakage issues. This benchmark includes a diverse set of samples from real-world projects, aiming to closely mirror actual development scenarios.

## Overview

ComplexCodeEval consists of:
- **3,897 Java samples** from **1,055 code repositories**
- **7,184 Python samples** from **2,107 code repositories**

### Key Features

1. **Diverse Downstream Tasks**: The benchmark supports multiple downstream tasks to evaluate the performance of different code analysis tools and models.
2. **Accurate Reflection of Programming Environments**: Samples are selected from projects that use popular third-party frameworks and packages.
3. **Avoidance of Data Leakage**: Incorporates multiple timestamps for each sample to prevent data leakage.

Introducing paper: ComplexCodeEval: A Benchmark for Evaluating Large Code Models on More Complex Code