The model processes video in a hierarchical manner, using shifted windows to capture local and global dependencies across both spatial and temporal dimensions efficiently.
Provides pre-trained weights on large-scale datasets such as Kinetics-400/600/700 and Something-Something v2, allowing for quick fine-tuning or transfer learning.
The codebase is built entirely in PyTorch, offering flexibility for customization, integration with the PyTorch ecosystem, and ease of debugging.
Supports training and inference at various spatial and temporal resolutions, adaptable to different hardware constraints and application requirements.
Includes evaluation scripts for standard metrics on popular video understanding benchmarks, facilitating reproducible research and performance validation.
Security analysts and surveillance system developers use Video Swin Transformer to automatically detect and classify human activities (e.g., walking, fighting, loitering) in video feeds. By processing footage in real-time or batch mode, it enhances monitoring efficiency, reduces manual review, and can trigger alerts for anomalous behavior, improving public safety and operational oversight.
Social media platforms employ the model to identify inappropriate or violent content in user-uploaded videos. It scans for specific actions or scenes, flagging them for human review or automatic removal. This helps maintain community guidelines, comply with regulations, and create safer online environments at scale.
Sports teams and broadcasters utilize the model to analyze player movements and team tactics from game footage. It can classify actions like passes, shots, or tackles, providing insights into performance metrics. Coaches use these insights for strategy development, player training, and post-game analysis to gain a competitive edge.
Medical researchers and therapists apply Video Swin Transformer to monitor patient movements during physical therapy or daily activities. It can assess exercise correctness, track rehabilitation progress, or detect falls in elderly care settings. This enables remote patient monitoring, personalized treatment plans, and early intervention.
Media companies and video libraries use the model to automatically generate tags or metadata for large video archives based on visual content and actions. This improves content discoverability through keyword search, enables smart recommendations, and streamlines catalog management, saving time and enhancing user experience.
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15Five operates in the people analytics and employee experience space, where platforms aggregate HR and feedback data to give organizations insight into their workforce. These tools typically support engagement surveys, performance or goal tracking, and dashboards that help leaders interpret trends. They are intended to augment HR and management decisions, not to replace professional judgment or context. For specific information about 15Five's metrics, integrations, and privacy safeguards, you should refer to the vendor resources published at https://www.15five.com.
20-20 Technologies is a comprehensive interior design and space planning software platform primarily serving kitchen and bath designers, furniture retailers, and interior design professionals. The company provides specialized tools for creating detailed 3D visualizations, generating accurate quotes, managing projects, and streamlining the entire design-to-sales workflow. Their software enables designers to create photorealistic renderings, produce precise floor plans, and automatically generate material lists and pricing. The platform integrates with manufacturer catalogs, allowing users to access up-to-date product information and specifications. 20-20 Technologies focuses on bridging the gap between design creativity and practical business needs, helping professionals present compelling visual proposals while maintaining accurate costing and project management. The software is particularly strong in the kitchen and bath industry, where precision measurements and material specifications are critical. Users range from independent designers to large retail chains and manufacturing companies seeking to improve their design presentation capabilities and sales processes.
3D Generative Adversarial Network (3D-GAN) is a pioneering research project and framework for generating three-dimensional objects using Generative Adversarial Networks. Developed primarily in academia, it represents a significant advancement in unsupervised learning for 3D data synthesis. The tool learns to create volumetric 3D models from 2D image datasets, enabling the generation of novel, realistic 3D shapes such as furniture, vehicles, and basic structures without explicit 3D supervision. It is used by researchers, computer vision scientists, and developers exploring 3D content creation, synthetic data generation for robotics and autonomous systems, and advancements in geometric deep learning. The project demonstrates how adversarial training can be applied to 3D convolutional networks, producing high-quality voxel-based outputs. It serves as a foundational reference implementation for subsequent work in 3D generative AI, often cited in papers exploring 3D shape completion, single-view reconstruction, and neural scene representation. While not a commercial product with a polished UI, it provides code and models for the research community to build upon.
