Learning to Count Anything: Reference-less Class-agnostic Counting with Weak Supervision

Code

• github.com/activevisionlab/learningtocountanything
  OfficialIn paperpytorch★ 39
• github.com/SinicaGroup/Class-agnostic-Few-shot-Object-Counting
  pytorch★ 118

Abstract

Current class-agnostic counting methods can generalise to unseen classes but usually require reference images to define the type of object to be counted, as well as instance annotations during training. Reference-less class-agnostic counting is an emerging field that identifies counting as, at its core, a repetition-recognition task. Such methods facilitate counting on a changing set composition. We show that a general feature space with global context can enumerate instances in an image without a prior on the object type present. Specifically, we demonstrate that regression from vision transformer features without point-level supervision or reference images is superior to other reference-less methods and is competitive with methods that use reference images. We show this on the current standard few-shot counting dataset FSC-147. We also propose an improved dataset, FSC-133, which removes errors, ambiguities, and repeated images from FSC-147 and demonstrate similar performance on it. To the best of our knowledge, we are the first weakly-supervised reference-less class-agnostic counting method.

Tasks

Benchmark Results

Reproductions