How Many, but How Sure? Class Competition as a Confidence Proxy in Multi-Class Density-Based Counting

Villanelle O'Reilly; Georgios Leontidis; Marc Hanheide

cs.LGOct 16, 2026

How Many, but How Sure? Class Competition as a Confidence Proxy in Multi-Class Density-Based Counting

Authors: Villanelle O'Reilly, Georgios Leontidis, Marc Hanheide

Organizations: Lincoln Centre for Autonomous Systems, University of Lincoln, UK · Applied Vision and Artificial Intelligence Lab, University of Lincoln, UK · UiT The Arctic University of Norway, Norway

Abstract

Multi-class density estimators can count objects in crowded scenes, but their class-wise outputs offer no indication of how reliably density mass has been allocated. We introduce a training-free confidence proxy based on local competition between the strongest class channels. The resulting prominence measure requires only the predicted density maps, with no retraining or access to model internals. We test whether residual competition is diagnostic of cross-class leakage by attenuating contested density at inference time. On DSACA predictions for the Hicks et al. dataset, this reduces overall MAE from 0.920 to 0.846, suggesting that class competition contains useful information about count reliability.

Figures & tables

Figure 1: Qualitative sample from the [ 4 ] dataset, using DSACA [ 8 ] ; weights from GeTNuRi [ 6 ] .

Crowd Counting

Crowd counting estimates how many people appear in images or video, supporting occupancy monitoring, safety planning, and crowd management.

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DualCount: Structurally Consistent Density and Point Modeling for Zero-Shot Object Counting

Xuan Cuong Ngo

University of Arkansas, USA

Object Counting

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Zero-shot object counting aims to estimate the number of objects specified by a text query without category-specific training. Recent approaches primarily rely on density regression or detection-style instance prediction. While effective, density-based models often suffer from spatial ambiguity and background leakage due to weakly regulated mass allocation, leading to fragmented or part-biased representations that increase counting error in complex scenes. In this work, we propose an instance-aware dual-decoder framework that structurally couples density and point representations for zero-shot object counting. Instead of treating density estimation as independent pixel-wise regression, we interpret it as a structured mass allocation problem over a latent set of object instances. Predicted instance centers induce a soft instance-wise decomposition of the density map, upon which we enforce two geometric constraints: (1) per-instance mass conservation, ensuring each object contributes approximately one unit of density mass, and (2) center-of-mass alignment, encouraging each density component to concentrate around its corresponding predicted center. These constraints introduce instance-level geometric consistency and lead to more accurate mass allocation, thereby reducing counting error. Extensive experiments on FSC-147, PUCPR+, and CARPK show that our approach consistently reduces counting error and establishes new state-of-the-art performance in zero-shot object counting.

Abstract

Crowd Counting

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