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arXiv Computer Vision

ABACUS: Adapting Unified Foundation Model for Bridging Image Count Understanding and Generation

ABACUS is a 3‑billion‑parameter vision‑language model that simultaneously tackles object counting, crowd counting, referring‑expression counting, and count‑faithful image generation. It introduces density‑aware adaptive zooming with an objectness map, a boundary‑aware count policy trained via GRPO to avoid over‑ or under‑counting at crop edges, and a cycle‑consistent GRPO strategy that scores generated images for count accuracy and aesthetic quality without external critics. The model sets new state‑of‑the‑art performance on seven benchmarks, outperforming both specialized and larger generalist models.

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Jun 22

ABACUS: Adapting Unified Foundation Model for Bridging Image Count Understanding and Generation

ABACUS is a unified vision-language model that handles object counting, crowd counting, referring-expression counting, and count-faithful image generation without any benchmark-specific training required. Our model is built on existing 3B-parameter unified foundation model and is adapted for object localization tasks using three key innovations: density-aware adaptive zooming with objectness maps for spatial grounding; a boundary-aware count policy via GRPO to eliminate crop-boundary errors; and a cycle-consistent GRPO strategy where the understanding branch self-critiques generated outputs, closing the understanding-generation gap without any external annotations.

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Aug 26

Object Counting Across Modalities: Taxonomies, Benchmarks, Applications, and Open Challenges

The paper reviews the evolution of object‑counting techniques from class‑specific density regression to open‑vocabulary, foundation‑model‑backed counters that can handle visual and textual prompts. It highlights that current evaluation relies on a few saturated benchmarks, leading to models exploiting statistical regularities rather than true generalization. The authors propose a five‑axis taxonomy and audit the literature across domains such as microscopy, remote sensing, crowd counting, and agriculture, identifying six structural contradictions and outlining a roadmap for robust, multimodal evaluation protocols.

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Sep 7

Counting Beyond Instances: A Benchmark for Group-Individual Object Counting

The paper introduces Group-Individual Object Counting (GIC), a new task that requires models to count both individual objects and higher‑level semantic groups within the same image. To support this, the authors present BunchCount, a benchmark of 1,330 images with 89,254 individual and 11,065 group annotations that include explicit containment relations. Experiments show that existing counting models excel at individual counting but struggle with group counting, leading the authors to propose a relational counting framework that leverages group‑individual containment to improve group‑level accuracy while preserving individual performance.

By Rui Wang, Junyi Huang, Jiahui Li, Qiao Yu, Yixue Hao, Long Hu, Baoru Huang
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arXiv Computer Vision
Aug 31

NumBench: Diagnosing Counting Failures in Text-to-Image Models

NumBench is a large benchmark of 640,000 text‑to‑image prompts that tests how well models count objects, covering 1,600 categories and counts from 1 to 100. It uses a factorial design to vary composition, spatial guidance, and appearance while balancing counts, and introduces a process model that predicts a near‑quadratic collision deficit at low occupancy. The authors also propose the Confidence‑Weighted Numeric Precision Score for scalable evaluation and find that all tested systems perform poorly above 50 objects, with count range, layout, and composition having the largest effects.

By Sandeep Wadhwa, Mayank Vatsa, Richa Singh, Parrva Chirag Shah, Prakhar Galriya
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