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.

引入了判别伪标签采样（DiPS）来利用无监督模型生成的类别无关地图进行弱监督目标定位（WSOL），并设计了基于 Transformer 的 WSOL 模型，使用 DiPS 生成的伪标签进行分类和定位任务训练，实验结果表明该架构与 Transformer 的建议相结合可以比现有方法获得更好的定位性能。