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Found 11 papers, 8 papers with code
Do LLMs Understand Visual Anomalies? Uncovering LLM Capabilities in Zero-shot Anomaly Detection
However, existing approaches depend on static anomaly prompts that are prone to cross-semantic ambiguity, and prioritize global image-level representations over crucial local pixel-level image-to-text alignment that is necessary for accurate anomaly localization.
HILL: Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text Classification
Existing self-supervised methods in natural language processing (NLP), especially hierarchical text classification (HTC), mainly focus on self-supervised contrastive learning, extremely relying on human-designed augmentation rules to generate contrastive samples, which can potentially corrupt or distort the original information.
The Impact of Customer Online Satisfaction on Stock Returns: Evidence from the E-commerce Reviews in China
This paper investigates the significance of consumer opinions in relation to value in China's A-share market.
HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification
Hierarchical text classification (HTC) is a challenging subtask of multi-label classification as the labels form a complex hierarchical structure.
SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning
In contrastive learning, the choice of ``view'' controls the information that the representation captures and influences the performance of the model.
3D-QueryIS: A Query-based Framework for 3D Instance Segmentation
Previous top-performing methods for 3D instance segmentation often maintain inter-task dependencies and the tendency towards a lack of robustness.
Structural Entropy Guided Graph Hierarchical Pooling
In addition to SEP, we further design two classification models, SEP-G and SEP-N for graph classification and node classification, respectively.
A Simple yet Effective Method for Graph Classification
Inspired by structural entropy on graphs, we transform the data sample from graphs to coding trees, which is a simpler but essential structure for graph data.
Hierarchical information matters: Text classification via tree based graph neural network
Inspired by the structural entropy, we construct the coding tree of the graph by minimizing the structural entropy and propose HINT, which aims to make full use of the hierarchical information contained in the text for the task of text classification.
Structural Optimization Makes Graph Classification Simpler and Better
We then present an implementation of the scheme in a tree kernel and a convolutional network to perform graph classification.
Price graphs: Utilizing the structural information of financial time series for stock prediction
Then, structural information, referring to associations among temporal points and the node weights, is extracted from the mapped graphs to resolve the problems regarding long-range dependencies and the chaotic property.
