Omkar2118/datasets huggingface dataset:
UniTalk

We present UniTalk, a novel dataset specifically designed for the task of active speaker detection, emphasizing challenging scenarios to enhance model generalization. Unlike previously established benchmarks such as AVA, which predominantly features old movies and thus exhibits significant domain gaps, UniTalk focuses explicitly on diverse and difficult real-world conditions. These include underrepresented languages, noisy backgrounds, and crowded scenes - such as multiple visible speakers speaking concurrently or in overlapping turns. It contains over 44.5 hours of video with frame-level active speaker annotations across 48,693 speaking identities, and spans a broad range of video types that reflect real-world conditions. Through rigorous evaluation, we show that state-of-the-art models, while achieving nearly perfect scores on AVA, fail to reach saturation on UniTalk, suggesting that the ASD task remains far from solved under realistic conditions. Nevertheless, models trained on UniTalk demonstrate stronger generalization to modern "in-the-wild" datasets like Talkies and ASW, as well as to AVA. UniTalk thus establishes a new benchmark for active speaker detection, providing researchers with a valuable resource for developing and evaluating versatile and resilient models.

Introducing paper: UniTalk: Towards Universal Active Speaker Detection in Real World Scenarios
SuperRS-VQA, HighRS-VQA

We introduce SuperRS-VQA (avg. 8,376×8,376) and HighRS-VQA (avg. 2,000×1,912), the highest-resolution vision-language datasets in RS to date, covering 22 real-world dialogue tasks

Introducing paper: GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K Resolution
Generalized Graph Grammars Dataset (arXiv version)

https://osf.io/btjqw/?view_only=f31eda86e7b04ac886734a26cd2ce43d

Introducing paper: Generalised Graph Grammars for Natural Language Processing
Graph EquiJoin Dataset

https://osf.io/xney5/

Introducing paper: A Logical Model for joining Property Graphs
Streamlining Temporal Formal Verification over Columnar Databases

https://osf.io/6y8cv/
AG-VPReID
Full name: AG-VPReID: A Challenging Large-Scale Benchmark for Aerial-Ground Video-based Person Re-Identification

The largest aerial-ground video dataset with 6,632 identities, 32,321 tracklets, and 9.6 million frames
Multiple platforms combining aerial drones (at various altitudes: 15m, 30m, 80m, 120m), CCTV cameras, and wearable cameras
Rich annotations including clothing changes and 15 soft-biometric attributes
Real-world challenges including extreme viewpoint variations, scale differences, and occlusions
Diverse environments with outdoor campus scenes captured over 20 days

Introducing paper: AG-VPReID: A Challenging Large-Scale Benchmark for Aerial-Ground Video-based Person Re-Identification