Weakly Supervised Object Localization (WSOL) allows for training deep
learning models for classification and localization, using only global
class-level labels. The lack of bounding box (bbox) supervision during training
represents a considerable challenge for hyper-parameter search and model
selection. Earlier WSOL works implicitly observed localization performance over
a test set which leads to biased performance evaluation. More recently, a
better WSOL protocol has been proposed, where a validation set with bbox
annotations is held out for model selection. Although it does not rely on the
test set, this protocol is unrealistic since bboxes are not available in
real-world applications, and when available, it is better to use them directly
to fit model weights. Our initial empirical analysis shows that the
localization performance of a model declines significantly when using only
image-class labels for model selection (compared to using bounding-box
annotations). This suggests that adding bounding-box labels is preferable for
selecting the best model for localization. In this paper, we introduce a new
WSOL validation protocol that provides a localization signal without the need
for manual bbox annotations. In particular, we leverage noisy pseudo boxes from
an off-the-shelf ROI proposal generator such as Selective-Search, CLIP, and RPN
pretrained models for model selection. Our experimental results with several
WSOL methods on ILSVRC and CUB-200-2011 datasets show that our noisy boxes
allow selecting models with performance close to those selected using ground
truth boxes, and better than models selected using only image-class labels.

使用无需手动边框注释的模糊伪框，提出一种新的 WSOL 验证协议，该协议可用于模型选择，并通过实验证明其性能接近使用真实边框选择的模型，优于仅使用图像类别标签选择的模型。

弱监督物体定位的真实模型选择

Realistic Model Selection for Weakly Supervised Object Localization

We introduce DiscoBox, a novel framework that jointly learns instance
segmentation and semantic correspondence using bounding box supervision.
Specifically, we propose a self-ensembling framework where instance
segmentation and semantic correspondence are jointly guided by a structured
teacher in addition to the bounding box supervision. The teacher is a
structured energy model incorporating a pairwise potential and a cross-image
potential to model the pairwise pixel relationships both within and across the
boxes. Minimizing the teacher energy simultaneously yields refined object masks
and dense correspondences between intra-class objects, which are taken as
pseudo-labels to supervise the task network and provide positive/negative
correspondence pairs for dense constrastive learning. We show a symbiotic
relationship where the two tasks mutually benefit from each other. Our best
model achieves 37.9% AP on COCO instance segmentation, surpassing prior weakly
supervised methods and is competitive to supervised methods. We also obtain
state of the art weakly supervised results on PASCAL VOC12 and PF-PASCAL with
real-time inference.

使用边界框监督，以结构化 teacher 模型为指导，共同学习实例分割和语义对应的 DiscoBox 框架，同时又能为密集对比学习提供正 / 负的对应对，用以监督网络任务实现物体掩模细化和类内对象间的密集对应，并取得了和有监督方法相当的 37.0% 的 COCO 实例分割性能和最新的 PASCAL VOC12 以及 PF-PASCAL 的最高弱监督结果的实时推理。

DiscoBox: 从框注释到弱监督实例分割与语义对应

DiscoBox: Weakly Supervised Instance Segmentation and Semantic  Correspondence from Box Supervision

Semantic segmentation has achieved huge progress via adopting deep Fully
Convolutional Networks (FCN). However, the performance of FCN based models
severely rely on the amounts of pixel-level annotations which are expensive and
time-consuming. To address this problem, it is a good choice to learn to
segment with weak supervision from bounding boxes. How to make full use of the
class-level and region-level supervisions from bounding boxes is the critical
challenge for the weakly supervised learning task. In this paper, we first
introduce a box-driven class-wise masking model (BCM) to remove irrelevant
regions of each class. Moreover, based on the pixel-level segment proposal
generated from the bounding box supervision, we could calculate the mean
filling rates of each class to serve as an important prior cue, then we propose
a filling rate guided adaptive loss (FR-Loss) to help the model ignore the
wrongly labeled pixels in proposals. Unlike previous methods directly training
models with the fixed individual segment proposals, our method can adjust the
model learning with global statistical information. Thus it can help reduce the
negative impacts from wrongly labeled proposals. We evaluate the proposed
method on the challenging PASCAL VOC 2012 benchmark and compare with other
methods. Extensive experimental results show that the proposed method is
effective and achieves the state-of-the-art results.

本篇论文中，我们提出了一种基于边界框监督下的弱监督下的语义分割方法，通过类别掩码模型、像素级别分割提案、填充率指导调整学习过程等手段，有效地减少错误标记造成的影响，提高了实验数据的表现。