Omkar2118/datasets huggingface dataset:
((#Reach-out-to))How to speak directly at LATAM Airlines?
Full name: ((#Reach-out-to))How to speak directly at LATAM Airlines?

To contact a live LATAM Airlines representative, dial 1 (801) – 855 – 5905 (U.S./Canada) or 1 (804) – 853 – 9001 (U.K.). Select the option for a live agent (press 0) or use voice prompts. For shorter wait times, consider the Spanish-language option.
((#representative, dial))How to speak directly at LATAM Airlines?
Full name: ((#representative, dial))How to speak directly at LATAM Airlines?

To contact a live LATAM Airlines representative, dial 1 (801) – 855 – 5905 (U.S./Canada) or 1 (804) – 853 – 9001 (U.K.). Select the option for a live agent (press 0) or use voice prompts. For shorter wait times, consider the Spanish-language option.
OVBench

OVBench is a benchmark tailored for **real-time video understanding**:

- **Memory, Perception, and Prediction of Temporal Contexts**: Questions are framed to reference the present state of entities, requiring models to memorize/perceive/predict past/present/future temporal contexts over time.
- **Dynamic Spatio-temporal Interaction**: The benchmark demands precise real-time interactions with video content, where actions, objects, and events must be understood in the context of their spatial and temporal relationships.
- **Contextual Awareness at Specific Moments**: Real-time questions are contextual, changing based on the specific timestamp they are asked, requiring a deep understanding of how temporal context evolves.

Introducing paper: Online Video Understanding: OVBench and VideoChat-Online
ImplicitQA

The ImplicitQA dataset was introduced in the paper [ImplicitQA: Going beyond frames towards Implicit Video Reasoning](https://arxiv.org/abs/2506.21742).

**Project page:** https://swetha5.github.io/ImplicitQA/

ImplicitQA is a novel benchmark specifically designed to test models on implicit reasoning in Video Question Answering (VideoQA). Unlike existing VideoQA benchmarks that primarily focus on questions answerable through explicit visual content (actions, objects, events directly observable within individual frames or short clips), ImplicitQA addresses the need for models to infer motives, causality, and relationships <u>across discontinuous frames</u>. This mirrors human-like understanding of creative and cinematic videos, which often employ storytelling techniques that deliberately omit certain depictions.

The dataset comprises 1,000 meticulously annotated QA pairs derived from over 320 high-quality creative video clips. These QA pairs are systematically categorized into key reasoning dimensions, including:

*   Lateral spatial reasoning
*   Vertical spatial reasoning
*   Relative Depth and proximity
*   Viewpoint and visibility
*   Motion and trajectory Dynamics
*   Causal and motivational reasoning
*   Social interactions and Relationships
*   Physical and Environmental context
*   Inferred counting

The annotations are deliberately challenging, crafted to ensure high quality and to highlight the difficulty of implicit reasoning for current VideoQA models.

Introducing paper: ImplicitQA: Going beyond frames towards Implicit Video Reasoning
RabakBench

http://hf.co/datasets/govtech/rabakbench

Introducing paper: RabakBench: Scaling Human Annotations to Construct Localized Multilingual Safety Benchmarks for Low-Resource Languages