Search Results for "segmentation"

Segmentation models with pre trained backbones. High-level API (just two lines to create a neural network) 9 models architectures for binary and multi class segmentation (including legendary Unet) 124 available encoders (and 500+ encoders from timm) All encoders have pre-trained weights for faster and better convergence. Popular metrics and losses for training routines.

...It analyzes transcripts and video data to identify the most engaging or “viral” moments, reducing the need for manual editing. The system integrates tools like FFmpeg and AI models to handle segmentation, cropping, and formatting for vertical video platforms. It supports automation workflows that allow creators to produce multiple clips efficiently at scale. The project focuses on content repurposing, helping users adapt long videos into formats suitable for platforms like TikTok and YouTube Shorts. Its modular design allows customization of each processing stage, including selection logic and visual formatting. ...

Av1an is a command-line encoding framework designed to efficiently encode video into the AV1 format using distributed and parallel processing techniques. It acts as an orchestrator that splits video files into chunks, processes them in parallel using encoders like SVT-AV1, AOM, or rav1e, and then merges the results into a final output. The tool is optimized for high-quality encoding while reducing total processing time through multi-core and multi-machine support. It includes scene detection...

Golang HLS Streamer is a Go-based implementation of HTTP Live Streaming (HLS) functionality, designed to handle media segmentation and playlist generation for streaming applications. It provides tools for creating and managing HLS streams, including segmenting video into smaller chunks and generating M3U8 playlists. The project is intended for developers building streaming servers or media delivery systems. It focuses on performance and simplicity, leveraging Go’s concurrency model to handle streaming tasks efficiently. gohls can be integrated into backend services to enable adaptive streaming workflows. ...

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...Unlike most existing methods that perform video matting frame-by-frame as independent images, our method uses a recurrent architecture to exploit temporal information in videos and achieves significant improvements in temporal coherence and matting quality. Furthermore, we propose a novel training strategy that enforces our network on both matting and segmentation objectives. This significantly improves our model's robustness. Our method does not require any auxiliary inputs such as a trimap or a pre-captured background image, so it can be widely applied to existing human matting applications. RVM is specifically designed for robust human video matting.

audio classification; speaker segmentation; speaker clustering; speaker recognition; spoken document retrieval; image retrieval; video retrieval; etc.

Fully automated segmentation of moving objects from a stationary scene using pre-stored scene images. Needs no external parameters or settings; all parameters are derived internally from the pre-stored images. Implemented using OpenCV framework.