Top 5 Computer Vision Video Datasets to Watch in 2025
In 2025, better video datasets are driving computer vision breakthroughs more than just bigger models. We highlight five key datasets—VideoMarathon, Ego-Exo4D, OmniHD-Scenes, Something-Something v2, and HACS—that provide the crucial long-form, egocentric, multimodal, and fine-grained data needed for next-gen AI applications like autonomous driving and advanced video understanding.
The smartest models are trained on smarter data.
You’ve tuned your model. You’ve read the latest CVPR papers. You’ve optimized every parameter. But here’s the truth: even the best architecture will underperform if your training data doesn’t match the task.
In 2025, the frontier of video-based computer vision is being shaped not by bigger models — but by better datasets.
From autonomous driving to multimodal reasoning, video understanding requires data that’s long-form, well-annotated, and grounded in reality. Below, we highlight five cutting-edge datasets that are setting the new standard in computer vision research.
Top 5 Newest & Must-Watch Video Datasets in 2025
- VideoMarathon
A new benchmark for long-form video-language understanding
VideoMarathon is a groundbreaking dataset designed for training and evaluating large-scale video-language models. With approximately 9,700 hours of video and 3.3 million question–answer pairs, it goes far beyond the short-clip datasets that have dominated the field.
Key strengths:
Supports deep temporal reasoning across long video sequences.
Rich QA annotations covering temporal, spatial, object, action, scene, and event understanding.
Powers state-of-the-art video-language models like Hour-LLaVA that can handle long videos.
Why it matters now: As the field moves from clip-based understanding to narrative-level comprehension, VideoMarathon offers the scale and annotation depth needed to support the next generation of intelligent, context-aware video models.
- Ego-Exo4D
A dual-view dataset for skill understanding and embodied intelligence
Ego-Exo4D is a groundbreaking multimodal video dataset capturing human skill learning and execution from both egocentric (first-person) and exocentric (third-person) views. Developed by a large-scale academic consortium with support from Meta AI, the dataset comprises over 1,200 hours of synchronized footage from 740 participants across 123 global locations, encompassing diverse skilled activities like cooking, sports, and mechanical repair.
Key strengths:
Designed for complex embodied intelligence tasks, such as proficiency estimation, skill segmentation, 3D pose estimation, and egocentric–exocentric alignment.
Rich, multimodal data: synchronized egocentric and exocentric video, 3D body pose, gaze tracking, IMU data, multi-channel audio, and expert natural language commentary.
Supports emerging benchmarks in AR/VR, robot imitation learning, and cross-view understanding.
Why it matters now: As AI shifts toward more human-centric, skill-aware systems, Ego-Exo4D provides the multi-perspective, sensor-rich training data needed to model how humans learn, perform, and are perceived. Whether you're building agents that collaborate with people, teach new tasks, or explain human behavior, Ego-Exo4D represents a next-generation foundation for embodied, explainable AI.
- OmniHD-Scenes
A 360° view for next-generation autonomous perception
OmniHD-Scenes is a high-fidelity, multimodal dataset designed for autonomous driving and 3D scene understanding. It was developed under the 2077AI Community, which was launched by Abaka AI as a Founding Contributor to promote the construction of open-source datasets and support cutting-edge AI research with standardized, high-quality data. OmniHD-Scenes offers synchronized data from six high-resolution cameras, LiDAR, and 4D radar, capturing complex driving environments across varied conditions, from daylight to nighttime, and clear skies to rain and fog.
Key strengths:
Covers full 360° perception with multi-sensor fusion: RGB, depth, LiDAR, and radar
Includes dense annotations: 3D bounding boxes, semantic masks, occupancy maps, and motion trajectories
Enables research in sensor fusion, multi-object tracking, and scene reconstruction under real-world complexity
Why it matters now: Autonomous vehicles need more than vision — they need reliable, multimodal understanding of dynamic, uncertain environments. OmniHD-Scenes provides the realistic, richly annotated data necessary to train and evaluate perception systems that can drive safely at scale.
- Something-Something v2
Teaching AI the difference between doing and pretending
Something-Something v2 is a large-scale video dataset focused on fine-grained human actions and intent understanding. It includes over 220,000 short videos, each depicting simple yet subtly distinct interactions — such as “closing a book” versus “pretending to close a book.”
Key strengths:
Encourages contextual understanding and intent-level reasoning, beyond surface-level motion
Each video is paired with textual descriptions, enabling video–language alignment
Widely used as a benchmark for temporal reasoning, action recognition, and multimodal modeling
Why it matters now: To move beyond basic object detection and activity labeling, AI needs to understand what people are doing — and why. Something-Something v2 remains one of the few datasets that explicitly challenges models to reason about subtle, real-world intent.
- HACS – Human Action Clips and Segments
Precise action recognition in the wild
HACS is a large-scale dataset tailored for both action classification and temporal action localization. With over 1.5 million labeled video clips sourced from YouTube, it covers 200+ human action categories, offering both trimmed and untrimmed versions for training and evaluation.
Key strengths:
Combines short clips and long-form videos, enabling robust detection of when actions begin and end
Includes frame-level temporal annotations, allowing fine-grained segmentation
Reflects real-world complexity, with natural variability in backgrounds, motion, and object interactions
Why it matters now: Applications like surveillance, robotics, and sports analytics require precise understanding of when actions occur — not just what they are. HACS offers the temporal resolution and scale needed to build models capable of accurate, real-time action detection in complex environments.Ready to work with better data?
Whether you're building your first video model or scaling a multimodal pipeline, the right dataset makes all the difference.
At Abaka AI, we provide high-quality, fully licensed datasets — from long-form video QA to multimodal driving scenes — built to accelerate modern computer vision workflows.
Explore available collections or request a tailored sample at www.abaka.ai.Dataset Links
- VideoMarathon- Github
- Ego4D- Official Site
- OmniHD-Scenes- Project Site
- Something- Dataset Site
- HACS- Dataset Site
- Something- Dataset Site
- HACS- Dataset Site