Omkar2118/datasets huggingface dataset:
SegSub
Full name: SegSub: Evaluating Robustness to Knowledge Conflicts and Hallucinations in Vision-Language Models

This research introduces \segsub, a framework for applying targeted image perturbations to investigate VLM resilience against knowledge conflicts. Our analysis reveals distinct vulnerability patterns: while VLMs are robust to parametric conflicts (20% adherence rates), they exhibit significant weaknesses in identifying counterfactual conditions (<30% accuracy) and resolving source conflicts (<1% accuracy). Correlations between contextual richness and hallucination rate (r = -0.368, p = 0.003) reveal the kinds of images that are likely to cause hallucinations. Through targeted fine-tuning on our benchmark dataset, we demonstrate improvements in VLM knowledge conflict detection, establishing a foundation for developing hallucination-resilient multimodal systems in information-sensitive environments.

Introducing paper: SegSub: Evaluating Robustness to Knowledge Conflicts and Hallucinations in Vision-Language Models
Semantic segmentation dataset on earthquake damage

This dataset contains **547** social media images taken in the aftermath of various earthquakes. Each image is paired with a pixel-wise **semantic segmentation mask** that categorizes the scene into three classes:

- **Undamaged structures**  
- **Damaged structures**  
- **Debris**

The segmentation masks were manually labeled and reviewed to support training and evaluation of machine learning models for pixel-level damage severity estimation.

Introducing paper: From Pixels to Damage Severity: Estimating Earthquake Impacts Using Semantic Segmentation of Social Media Images
Vidore
Full name: Visual Document Retrieval Benchmark

It is collection regrouping all datasets constituting the ViDoRe benchmark. It includes the test sets from different academic datasets (ArXiVQA, DocVQA, InfoVQA, TATDQA, TabFQuAD) and from datasets synthetically generated spanning various themes and industrial applications: (Artificial Intelligence, Government Reports, Healthcare Industry, Energy and Shift Project). Further details can be found on the corresponding dataset cards.

Introducing paper: ColPali: Efficient Document Retrieval with Vision Language Models
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Full name: [{AM FAQ's Guidlines}] How Long Does American Airlines Respond to Complaints?

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EGY-BCD

Bi-temporal images in the EGY-BCD dataset are taken from 4 different regions located in Egypt, including New Mansoura, El Galala City, New Cairo, and New Thebes. The figure below shows the building changes in New Mansoura City and New Thebes. Our image data capture time varies from 2017 to 2022. The images feature seasonal changes and different lighting changes in our new dataset, which can help develop effective methods that can mitigate the impact of unrelated changes on real changes.

Dataset paper: [AFDE-Net: Building Change Detection Using Attention-Based Feature Differential Enhancement for Satellite Imagery](https://ieeexplore.ieee.org/document/10145434)
CLCD
Full name: Cropland-CD

The CLCD dataset consists of 600 pairs image of cropland change samples, with 360 pairs for training, 120 pairs for validation and 120 pairs for testing. The bi-temporal images in CLCD were collected by Gaofen-2 in Guangdong Province, China, in 2017 and 2019, respectively, with spatial resolution ranged from 0.5 to 2 m. Each group of samples is composed of two images of 512 × 512 and a corresponding binary label of cropland change.

Dataset paper: [A CNN-Transformer Network With Multiscale Context Aggregation for Fine-Grained Cropland Change Detection](https://ieeexplore.ieee.org/document/9780164)
Benchmark for AMR Metrics based on Overt Objectives

Benchmark for AMR Metrics based on Overt Objectives (Bamboo), the first benchmark to support empirical assessment of graph-based MR similarity metrics. Bamboo maximizes the interpretability of results by defining multiple overt objectives that range from sentence similarity objectives to stress tests that probe a metric’s robustness against meaning-altering and meaning- preserving graph transformations.

Introducing paper: Weisfeiler-Leman in the BAMBOO: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similarity
BRISC
Full name: BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification

BRISC is a high-quality, expert-annotated MRI dataset curated for brain tumor segmentation and classification. It addresses common limitations in existing datasets (e.g., BraTS, Figshare), including class imbalance, narrow tumor focus, and annotation inconsistencies.