Multiscale Vision Transformer With Deep Clustering-Guided Refinement for Weakly Supervised Object Localization
This addresses the problem of limited localization accuracy in weakly-supervised object detection for computer vision applications, representing an incremental improvement.
The paper tackles weakly-supervised object localization using only image-level labels to reduce annotation labor, proposing a multiscale vision transformer with deep clustering-guided refinement that achieves improved localization accuracy on the ILSVRC-2012 dataset.
This work addresses the task of weakly-supervised object localization. The goal is to learn object localization using only image-level class labels, which are much easier to obtain compared to bounding box annotations. This task is important because it reduces the need for labor-intensive ground-truth annotations. However, methods for object localization trained using weak supervision often suffer from limited accuracy in localization. To address this challenge and enhance localization accuracy, we propose a multiscale object localization transformer (MOLT). It comprises multiple object localization transformers that extract patch embeddings across various scales. Moreover, we introduce a deep clustering-guided refinement method that further enhances localization accuracy by utilizing separately extracted image segments. These segments are obtained by clustering pixels using convolutional neural networks. Finally, we demonstrate the effectiveness of our proposed method by conducting experiments on the publicly available ILSVRC-2012 dataset.