視覚マルチタスク学習モデル向けマルチラベルアノテーション
Multi-label Annotation for Visual Multi-Task Learning Models
物体検出・セグメンテーション・キーポイント抽出を同時に学習するマルチタスクモデル向けに、複数種類のアノテーションを統合フォーマットで出力できるラベリングツールと拡張ライブラリを提案し、ロボット知覚の2事例で実証した。
著者: G. Sharma, A. Angleraud, R. Pieters
分類: cs.RO
原文アブストラクト
Deep learning requires large amounts of data, and a well-defined pipeline for labeling and augmentation. Current solutions support numerous computer vision tasks with dedicated annotation types and formats, such as bounding boxes, polygons, and key points. These annotations can be combined into a single data format to benefit approaches such as multi-task models. However, to our knowledge, no available labeling tool supports the export functionality for a combined benchmark format, and no augmentation library supports transformations for the combination of all. In this work, these functionalities are presented, with visual data annotation and augmentation to train a multi-task model (object detection, segmentation, and key point extraction). The tools are demonstrated in two robot perception use cases.
