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

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arxiv 2205.10203 v2 pith:7OAJZ3DP submitted 2022-05-20 cs.CV

Michael Hobley , Victor Prisacariu This is my paper · ORCID
classification cs.CV
keywords countingclass-agnosticimagesmethodsreference-lessreferencecurrentdataset
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 12 citations worldwide. Full citation record

  1. SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights

    cs.CV 2025-07 conditional novelty 6.0 of 10

    SFOOD combines existing food datasets with self-collected hyperspectral images to create a six-task benchmark, and its evaluations suggest spectral bands improve sweetness and herbal classification while current model...

  2. ISAC: Training-Free Instance-to-Semantic Attention Control for Multi-Instance Generation

    cs.CV 2025-05 conditional novelty 6.0 of 10

    ISAC improves multi-instance image generation by carving out instance regions from self-attention first and then assigning semantics to those regions.

  3. Single Domain Generalization for Few-Shot Counting via Universal Representation Matching

    cs.CV 2025-05 conditional novelty 6.0 of 10

    URM distills CLIP vision-language representations into learnable prototypes for few-shot counting, improving single-domain generalization on unseen datasets.

  4. Expanding Zero-Shot Object Counting with Rich Prompts

    cs.CV 2025-05 conditional novelty 5.0 of 10

    RichCount improves zero-shot object counting by enriching text prompts with MLLM-generated descriptions and aligning them to CLIP visual features, achieving state-of-the-art mean absolute error on three counting benchmarks.

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