Human-Object Interaction (HOI) detection aims to localize human-object pairs
and comprehend their interactions. Recently, two-stage transformer-based
methods have demonstrated competitive performance. However, these methods
frequently focus on object appearance features and ignore global contextual
information. Besides, vision-language model CLIP which effectively aligns
visual and text embeddings has shown great potential in zero-shot HOI
detection. Based on the former facts, We introduce a novel HOI detector named
ISA-HOI, which extensively leverages knowledge from CLIP, aligning interactive
semantics between visual and textual features. We first extract global context
of image and local features of object to Improve interaction Features in images
(IF). On the other hand, we propose a Verb Semantic Improvement (VSI) module to
enhance textual features of verb labels via cross-modal fusion. Ultimately, our
method achieves competitive results on the HICO-DET and V-COCO benchmarks with
much fewer training epochs, and outperforms the state-of-the-art under
zero-shot settings.

ISA-HOI 是一种新颖的 HOI 探测器，它广泛利用了 CLIP 的知识，通过对视觉和文本特征之间的交互语义进行对齐，提取图像的全局上下文和物体的局部特征来改善图像中的交互特征，并且通过跨模态融合提高动词标签的文本特征，最终在较少的训练时期内在 HICO-DET 和 V-COCO 基准上取得了竞争性的结果，并在零样本设置下超越了最先进的方法。

探索基于交互语义对齐的高效视觉语言模型 HOI 检测方法

Exploring Interactive Semantic Alignment for Efficient HOI Detection  with Vision-language Model

Recent years have witnessed an upsurge of interest in the problem of anomaly
detection on attributed networks due to its importance in both research and
practice. Although various approaches have been proposed to solve this problem,
two major limitations exist: (1) unsupervised approaches usually work much less
efficiently due to the lack of supervisory signal, and (2) existing anomaly
detection methods only use local contextual information to detect anomalous
nodes, e.g., one- or two-hop information, but ignore the global contextual
information. Since anomalous nodes differ from normal nodes in structures and
attributes, it is intuitive that the distance between anomalous nodes and their
neighbors should be larger than that between normal nodes and their neighbors
if we remove the edges connecting anomalous and normal nodes. Thus, hop counts
based on both global and local contextual information can be served as the
indicators of anomaly. Motivated by this intuition, we propose a hop-count
based model (HCM) to detect anomalies by modeling both local and global
contextual information. To make better use of hop counts for anomaly
identification, we propose to use hop counts prediction as a self-supervised
task. We design two anomaly scores based on the hop counts prediction via HCM
model to identify anomalies. Besides, we employ Bayesian learning to train HCM
model for capturing uncertainty in learned parameters and avoiding overfitting.
Extensive experiments on real-world attributed networks demonstrate that our
proposed model is effective in anomaly detection.

本文提出采用基于跳数的模型识别异常，结合了局部和全局信息，并利用贝叶斯学习进行参数学习和缓解过拟合问题，实验表明该方法有效。

基于跳数的属性网络自监督异常检测

Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks

Recent work has made significant progress in improving spatial resolution for
pixelwise labeling with Fully Convolutional Network (FCN) framework by
employing Dilated/Atrous convolution, utilizing multi-scale features and
refining boundaries. In this paper, we explore the impact of global contextual
information in semantic segmentation by introducing the Context Encoding
Module, which captures the semantic context of scenes and selectively
highlights class-dependent featuremaps. The proposed Context Encoding Module
significantly improves semantic segmentation results with only marginal extra
computation cost over FCN. Our approach has achieved new state-of-the-art
results 51.7% mIoU on PASCAL-Context, 85.9% mIoU on PASCAL VOC 2012. Our single
model achieves a final score of 0.5567 on ADE20K test set, which surpass the
winning entry of COCO-Place Challenge in 2017. In addition, we also explore how
the Context Encoding Module can improve the feature representation of
relatively shallow networks for the image classification on CIFAR-10 dataset.
Our 14 layer network has achieved an error rate of 3.45%, which is comparable
with state-of-the-art approaches with over 10 times more layers. The source
code for the complete system are publicly available.

本文提出了一种 Context Encoding Module 方法，在原有 FCN 框架的基础上加入了全局场景语义信息来优化语义分割结果，在 PASCAL-Context、PASCAL VOC 2012 和 ADE20K 数据集上取得了最新最优的成果，并且通过在 CIFAR-10 数据集上进行对比实验表明该方法可以用来改善相对浅层网络的特征表示效果。