Search Results for "semantic"

...A web interface lets you upload images, tune options, and preview generated results, while a backend service orchestrates the model calls and post-processing. The tool focuses on practical developer outputs—semantic markup, reusable components, and readable classes—so the result is a starting point you can refine, not a throwaway demo. It also supports multi-model backends and local-first options to balance cost, speed, and privacy. Teams use it for rapid prototyping, migrating static mockups to codebases, and exploring design alternatives without hand-coding every pixel from scratch.

...It parses subtitles and recommends scene divisions, typically targeting segments of about 25 to 35 seconds. Each scene is planned around one core idea before artwork and annotations are prepared. Semantic drawing order connects visual elements to specific subtitle events rather than relying only on coordinates. A streaming renderer first reveals ink strokes and then progressively adds color. Users can adjust regions, timing, sequence, and subtitle associations in a browser preview before rendering scenes and merging them into a final MP4.

A set of tools (command line and GUI) to provide a complete digital photo workflow for Unixes. EXIF headers are used as the central information repository, so users may change their software at any time without loosing any data.

...It enables the use of recent advances in computer vision to the conventional image editing pipeline. Applications from deep learning such as monocular depth estimation, semantic segmentation, mask generative adversarial networks, image super-resolution, de-noising and coloring have been incorporated with GIMP through Python-based plugins. Additionally, operations on images such as edge detection and color clustering have also been added. GIMP-ML relies on standard Python packages such as numpy, scikit-image, pillow, pytorch, open-cv, scipy. ...

...Each environment includes fully labeled 3D objects, allowing agents to perceive and interact with their surroundings through multiple sensory modalities including RGB images, depth maps, semantic segmentation masks, and top-down maps. The simulator is optimized for high-performance rendering, achieving thousands of frames per second to enable efficient large-scale training of RL agents. House3D has served as the foundation for several influential research projects such as RoomNav (for concept-based navigation) and Embodied Question Answering (EQA).