Showing 4 open source projects for "context-shredder"

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  • 1
    DETR

    DETR

    End-to-end object detection with transformers

    ...It consists of a set-based global loss, which forces unique predictions via bipartite matching, and a Transformer encoder-decoder architecture. Given a fixed small set of learned object queries, DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel. Due to this parallel nature, DETR is very fast and efficient.
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  • 2
    DetectAndTrack

    DetectAndTrack

    The implementation of an algorithm presented in the CVPR18 paper

    ...Although the repo has been archived and is now read-only, its issue tracker and artifacts remain useful for understanding implementation details and experimental settings. The project sits alongside other Facebook Research vision efforts, offering historical context for the evolution of video pose and tracking techniques. Researchers can still study the algorithms, adapt the pipeline, or port ideas into modern frameworks.
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  • 3
    FastQR

    FastQR

    A Fast QR code detector for arbitrarily acquired images

    Applications of Quick Response (QR) codes enable rich context interaction through creation of links between physical objects and internet resources. In spite of the widespread use of this kind of barcode, applications for visually impaired people and robots are not common because existing decoders assume that the symbol is properly framed during image acquisition. This project implements a two-stage component-based approach to perform accurate detection of QR code symbols in arbitrarily acquired images. ...
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  • 4
    VOC-DPM

    VOC-DPM

    Object detection system using deformable part models (DPMs)

    ...It implements both latent SVM training (where part assignments are treated as latent variables) and weak-label structural SVM (WL-SSVM) for learning from partially labeled data. The code integrates several enhancements: a star-cascade detection algorithm to speed up screening, context rescoring (re-ranking detections using contextual information), and various optimizations like in-memory training (rather than large on-disk data files).
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