FocalCount: Towards Class-Count Imbalance in Class-Agnostic Counting
This addresses inaccuracies in object counting for applications requiring class-specific outputs, representing an incremental improvement over prior methods.
The paper tackles the problem of class-count imbalance in class-agnostic object counting, where existing methods inaccurately estimate counts due to dataset biases and uniform loss penalization, and proposes FocalCount with Focal-MSE loss to improve performance, showing superior results in few-shot and zero-shot scenarios across three datasets.
In class-agnostic object counting, the goal is to estimate the total number of object instances in an image without distinguishing between specific categories. Existing methods often predict this count without considering class-specific outputs, leading to inaccuracies when such outputs are required. These inaccuracies stem from two key challenges: 1) the prevalence of single-category images in datasets, which leads models to generalize specific categories as representative of all objects, and 2) the use of mean squared error loss during training, which applies uniform penalization. This uniform penalty disregards errors in less frequent categories, particularly when these errors contribute minimally to the overall loss. To address these issues, we propose {FocalCount}, a novel approach that leverages diverse feature attributes to estimate the number of object categories in an image. This estimate serves as a weighted factor to correct class-count imbalances. Additionally, we introduce {Focal-MSE}, a new loss function that integrates binary cross-entropy to generate stronger error gradients, enhancing the model's sensitivity to errors in underrepresented categories. Our approach significantly improves the model's ability to distinguish between specific classes and general counts, demonstrating superior performance and scalability in both few-shot and zero-shot scenarios across three object counting datasets. The code will be released soon.