msracver/Deep-Feature-Flow

Health v2 · maintenance only

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1315 stars · 292 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official MXNet implementation of Deep Feature Flow (CVPR 2017), an end-to-end framework for video recognition such as object detection and semantic segmentation in videos. It speeds up recognition by running heavy networks on sparse key frames and propagating feature maps to other frames via a lightweight optical flow network.

Use cases

• speed up video object detection with key-frame feature propagation
• semantic segmentation on video frames
• train video recognition models end-to-end with sparse annotations
• reproduce Deep Feature Flow research results
• implement optical-flow-based feature propagation in MXNet

When to choose

• you need a fast, end-to-end trainable video recognition pipeline based on MXNet
• you want to reproduce or extend the CVPR 2017 DFF paper
• your video dataset has sparse frame-level annotations

When to avoid

• you use PyTorch or TensorFlow rather than legacy MXNet
• you need actively maintained, production-ready code
• you need real-time inference on CPU-only hardware

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision video-processing computer-vision deep-learning machine-learning python video-recognition object-detection semantic-segmentation optical-flow mxnet cvpr-2017 research-code video linux gpu

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