This repo contains the official PyTorch implementation of our ECCV'2024 paper UniFS: Universal Few-shot Instance Perception with Point Representations.

We evaluate our models on COCO-UniFS benchmark. This benchmark is built upon several existing datasets, including MSCOCO and MISC.

The COCO-UniFS benchmark provides dense annotations for four fundamental few-shot computer vision tasks: object detection, instance segmentation, pose estimation, and object counting. The annotations for object detection and instance segmentation are directly taken from the MSCOCO dataset, which provides bounding box and per-instance segmentation mask annotations for 80 object categories. For pose estimation, we extend the MSCOCO dataset by adding instance-level keypoint annotations for 34 object categories from the MISC dataset. The MISC dataset was originally designed for multi-instance semantic correspondence, and we adapted it to fit the few-shot pose estimation task. The dataset split follows DeFRCN.

• Unzip the downloaded COCO-UniFS data-source to datasets and put it into your project directory:

    ...
    datasets
      | -- coco (trainval2014/*.jpg, val2014/*.jpg, annotations/*.json)
      | -- unifs_cocosplit
    unifS
    tools
    ...
  

This repo is developed based on DCFS, DeFRCN and Detectron2.

UniFS is freely available for free non-commercial use, and may be redistributed under these conditions. For commercial queries, please contact Mr. Sheng Jin (jinsheng13[at]foxmail[dot]com). We will send the detail agreement to you.