Weakly-supervised semantic segmentation (WSS) ensures high-quality
segmentation with limited data and excels when employed as input seed masks for
large-scale vision models such as Segment Anything. However, WSS faces
challenges related to minor classes since those are overlooked in images with
adjacent multiple classes, a limitation originating from the overfitting of
traditional expansion methods like Random Walk. We first address this by
employing unsupervised and weakly-supervised feature maps instead of
conventional methodologies, allowing for hierarchical mask enhancement. This
method distinctly categorizes higher-level classes and subsequently separates
their associated lower-level classes, ensuring all classes are correctly
restored in the mask without losing minor ones. Our approach, validated through
extensive experimentation, significantly improves WSS across five benchmarks
(VOC: 79.8\%, COCO: 53.9\%, Context: 49.0\%, ADE: 32.9\%, Stuff: 37.4\%),
reducing the gap with fully supervised methods by over 84\% on the VOC
validation set. Code is available at this https URL

采用无监督和弱监督特征图代替传统方法，通过层次化的掩膜增强实现对弱监督语义分割（WSS）的改进，有效解决了 WSS 在处理较小类别时的挑战，大幅提升了 WSS 的性能，并在多个基准数据集上取得了显著的结果改善。

DHR: 基于双特征的层次化重平衡方法在弱监督语义分割中的应用

DHR: Dual Features-Driven Hierarchical Rebalancing in Inter- and  Intra-Class Regions for Weakly-Supervised Semantic Segmentation

Temporal sentence grounding (TSG) aims to locate a specific moment from an
untrimmed video with a given natural language query. Recently, weakly
supervised methods still have a large performance gap compared to fully
supervised ones, while the latter requires laborious timestamp annotations. In
this study, we aim to reduce the annotation cost yet keep competitive
performance for TSG task compared to fully supervised ones. To achieve this
goal, we investigate a recently proposed glance-supervised temporal sentence
grounding task, which requires only single frame annotation (referred to as
glance annotation) for each query. Under this setup, we propose a Dynamic
Gaussian prior based Grounding framework with Glance annotation (D3G), which
consists of a Semantic Alignment Group Contrastive Learning module (SA-GCL) and
a Dynamic Gaussian prior Adjustment module (DGA). Specifically, SA-GCL samples
reliable positive moments from a 2D temporal map via jointly leveraging
Gaussian prior and semantic consistency, which contributes to aligning the
positive sentence-moment pairs in the joint embedding space. Moreover, to
alleviate the annotation bias resulting from glance annotation and model
complex queries consisting of multiple events, we propose the DGA module, which
adjusts the distribution dynamically to approximate the ground truth of target
moments. Extensive experiments on three challenging benchmarks verify the
effectiveness of the proposed D3G. It outperforms the state-of-the-art weakly
supervised methods by a large margin and narrows the performance gap compared
to fully supervised methods. Code is available at
this https URL

通过减少标注成本并保持与全监督方法相竞争的性能，我们提出了一种基于动态高斯先验的望注释视图的时间句子定位框架，该框架在关键词之间进行了深入调研，并通过 Semantic Alignment Group Contrastive Learning 模块和 Dynamic Gaussian prior Adjustment 模块来提高性能。

D3G: 利用高斯先验探索具有注视标注的时间句子定位

D3G: Exploring Gaussian Prior for Temporal Sentence Grounding with  Glance Annotation

Monocular depth estimation has become one of the most studied applications in
computer vision, where the most accurate approaches are based on fully
supervised learning models. However, the acquisition of accurate and large
ground truth data sets to model these fully supervised methods is a major
challenge for the further development of the area. Self-supervised methods
trained with monocular videos constitute one the most promising approaches to
mitigate the challenge mentioned above due to the wide-spread availability of
training data. Consequently, they have been intensively studied, where the main
ideas explored consist of different types of model architectures, loss
functions, and occlusion masks to address non-rigid motion. In this paper, we
propose two new ideas to improve self-supervised monocular trained depth
estimation: 1) self-attention, and 2) discrete disparity prediction. Compared
with the usual localised convolution operation, self-attention can explore a
more general contextual information that allows the inference of similar
disparity values at non-contiguous regions of the image. Discrete disparity
prediction has been shown by fully supervised methods to provide a more robust
and sharper depth estimation than the more common continuous disparity
prediction, besides enabling the estimation of depth uncertainty. We show that
the extension of the state-of-the-art self-supervised monocular trained depth
estimator Monodepth2 with these two ideas allows us to design a model that
produces the best results in the field in KITTI 2015 and Make3D, closing the
gap with respect self-supervised stereo training and fully supervised
approaches.

本研究提出了两种新的方法以提高自监督单目训练深度估计，分别为自我关注和离散视差预测，将这两种方法应用到最先进的单目深度估计模型 Monodepth2 中，超越其他自监督和完全监督方法，获得在 KITTI 2015 和 Make3D 数据集上最好的结果。