Self-supervised vision transformers (SSTs) have shown great potential to yield rich localization maps that highlight different objects in an image. However, these maps remain class-agnostic since the model is unsupervised. They often tend to decompose the image into multiple maps containing different objects while being unable to distinguish the object of interest from background noise objects. In this paper, Discriminative Pseudo-label Sampling (DiPS) is introduced to leverage these class-agnostic maps for weakly-supervised object localization (WSOL), where only image-class labels are available. Given multiple attention maps, DiPS relies on a pre-trained classifier to identify the most discriminative regions of each attention map. This ensures that the selected ROIs cover the correct image object while discarding the background ones, and, as such, provides a rich pool of diverse and discriminative proposals to cover different parts of the object. Subsequently, these proposals are used as pseudo-labels to train our new transformer-based WSOL model designed to perform classification and localization tasks. Unlike standard WSOL methods, DiPS optimizes performance in both tasks by using a transformer encoder and a dedicated output head for each task, each trained using dedicated loss functions. To avoid overfitting a single proposal and promote better object coverage, a single proposal is randomly selected among the top ones for a training image at each training step. Experimental results on the challenging CUB, ILSVRC, OpenImages, and TelDrone datasets indicate that our architecture, in combination with our transformer-based proposals, can yield better localization performance than state-of-the-art methods.

翻译：自监督视觉变换器（SSTs）在生成突出图像中不同目标的丰富定位图方面展现出巨大潜力。然而，由于模型非监督特性，这些定位图仍保持类别无关性，往往将图像分解为包含不同目标的多个映射图，却难以区分感兴趣目标与背景噪声。本文提出判别式伪标签采样方法（DiPS），通过利用这些类别无关的定位图实现弱监督目标定位（WSOL），该方法仅需图像级类别标签。给定多张注意力图，DiPS依赖预训练分类器识别每张注意力图中最具判别性的区域。这确保所选感兴趣区域（ROI）覆盖正确的图像目标同时剔除背景区域，从而为目标各部分覆盖提供多样且具判别性的候选池。随后，这些候选区域被用作伪标签训练新型基于变换器的WSOL模型，该模型专为执行分类与定位任务设计。与标准WSOL方法不同，DiPS通过使用变换器编码器及为每个任务配备专用输出头（分别采用专用损失函数训练）来优化双任务性能。为避免过拟合单一候选区域并促进更好的目标覆盖，每个训练步骤中从训练图像的顶级候选区域中随机选择一个。在具有挑战性的CUB、ILSVRC、OpenImages和TelDrone数据集上的实验结果表明，结合基于变换器的候选区域方案，我们的架构能比现有最优方法实现更优的定位性能。