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.
arXiv:2508.16644v5 Announce Type: replace
Abstract: Diffusion models excel at photorealistic synthesis but struggle with object count fidelity, especially in high-density settings. We introduce COUNT...
By Anindya Mondal, Sauradip Nag, Ayan Banerjee, Josep Llados, Xiatian Zhu, Anjan Dutta
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.
By Joana Konadu Owusu, Shivanand Venkanna Sheshappanavar
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
arXiv:2609.37096v1 Announce Type: new
Abstract: Multimodal Large Language Models (MLLMs) consistently struggle with fine-grained visual counting, yet the underlying causes remain poorly understood. I...
By Liwei Che, Yihao Quan, Sen Fang, Hongyi Wang, Ranjay Krishna, Ruixiang Tang, Vladimir Pavlovic
arXiv:2609.23012v1 Announce Type: new
Abstract: Generative vision-language models (VLMs) offer a counting paradigm in which one model produces both a count and a natural-language account of the scene...
By Moshiur Farazi, Bekir Ciftler, Abdulhalim Dandoush, Reda Bendraou