# MinMaxCAM: Improving object coverage for CAM-basedWeakly Supervised Object Localization

Source: [MinMaxCAM: Improving object coverage for CAM-basedWeakly Supervised Object Localization](<https://www.sota2.com/research/paper/68fa69d91e209131cea24993>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:07:38.365Z

Authors: Kaili Wang, Jose Oramas, Tinne Tuytelaars

Published: 2021-04-29T14:39:53.000Z

## Abstract

One of the most common problems of weakly supervised object localization is that of inaccurate object coverage. In the context of state-of-the-art methods based on Class Activation Mapping, this is caused either by localization maps which focus, exclusively, on the most discriminative region of the objects of interest or by activations occurring in background regions. To address these two problems, we propose two representation regularization mechanisms: Full Region Regularizationwhich tries to maximize the coverage of the localization map inside the object region, and Common Region Regularization which minimizes the activations occurring in background regions. We evaluate the two regularizations on the ImageNet, CUB-200-2011 and OpenImages-segmentation datasets, and show that the proposed regularizations tackle both problems, outperforming the state-of-the-art by a significant margin.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2104.14375>)

[PDF](<https://arxiv.org/pdf/2104.14375.pdf>)