Weakly supervised visual recognition using inexact supervision is a critical
yet challenging learning problem. It significantly reduces human labeling costs
and traditionally relies on multi-instance learning and pseudo-labeling. This
paper introduces WeakSAM and solves the weakly-supervised object detection
(WSOD) and segmentation by utilizing the pre-learned world knowledge contained
in a vision foundation model, i.e., the Segment Anything Model (SAM). WeakSAM
addresses two critical limitations in traditional WSOD retraining, i.e., pseudo
ground truth (PGT) incompleteness and noisy PGT instances, through adaptive PGT
generation and Region of Interest (RoI) drop regularization. It also addresses
the SAM's problems of requiring prompts and category unawareness for automatic
object detection and segmentation. Our results indicate that WeakSAM
significantly surpasses previous state-of-the-art methods in WSOD and WSIS
benchmarks with large margins, i.e. average improvements of 7.4% and 8.5%,
respectively. The code is available at https://github.com/hustvl/WeakSAM.

使用弱监督的视觉识别方法，借助预训练的视觉基础模型（SAM）中的世界知识，解决了弱监督对象检测和分割中的限制问题，并在 WSOD 和 WSIS 基准测试上显著超过了之前的最先进方法。

WeakSAM：融合分割与弱监督实例级别识别的方法

WeakSAM: Segment Anything Meets Weakly-supervised Instance-level  Recognition

Time series anomaly detection (TSAD) plays a vital role in various domains
such as healthcare, networks, and industry. Considering labels are crucial for
detection but difficult to obtain, we turn to TSAD with inexact supervision:
only series-level labels are provided during the training phase, while
point-level anomalies are predicted during the testing phase. Previous works
follow a traditional multi-instance learning (MIL) approach, which focuses on
encouraging high anomaly scores at individual time steps. However, time series
anomalies are not only limited to individual point anomalies, they can also be
collective anomalies, typically exhibiting abnormal patterns over subsequences.
To address the challenge of collective anomalies, in this paper, we propose a
tree-based MIL framework (TreeMIL). We first adopt an N-ary tree structure to
divide the entire series into multiple nodes, where nodes at different levels
represent subsequences with different lengths. Then, the subsequence features
are extracted to determine the presence of collective anomalies. Finally, we
calculate point-level anomaly scores by aggregating features from nodes at
different levels. Experiments conducted on seven public datasets and eight
baselines demonstrate that TreeMIL achieves an average 32.3% improvement in F1-
score compared to previous state-of-the-art methods. The code is available at
this https URL

这篇研究提出了一种基于树的多示例学习框架（TreeMIL），用于解决时间序列异常检测中的集体异常问题，并在七个公共数据集和八种基线方法上进行实验证明，相较于先前最先进的方法，TreeMIL 的 F1 得分平均提高了 32.3%。