20 accepted papers
The task of stochastic human motion prediction has attracted significant attention in recent years due to its wide-ranging applications in robotics, animation, and human-computer interaction. While diffusion models have demonstrated promising progress in this domain, they remain hindered by two crit
Short text classification has gained significant attention in the information age due to its prevalence and real-world applications. Recent advancements in graph learning combined with contrastive learning have shown promising results in addressing the challenges of semantic sparsity and limited lab…
In knowledge distillation, the performance of teacher models often serves as an upper limit for student models. For a long time, deeper and more accurate supervised learning algorithms have been the first choice for teacher models in image classification tasks where unsupervised models typically und…
Short text classification, as a research subtopic in natural language processing, is more challenging due to its semantic sparsity and insufficient labeled samples in practical scenarios. We propose a novel model named MI-DELIGHT for short text classification in this work. Specifically, it first per…
Recent advances in oracle bone inscriptions (OBI) classification have explored various strategies such as zero-shot learning, augmentation, and complex convolution architectures. These strategies ultimately represent samples as global feature vectors in various forms but often fail to effectively ca…
Graph few-shot learning has attracted increasing attention due to its ability to rapidly adapt models to new tasks with only limited labeled nodes. Despite the remarkable progress made by existing graph few-shot learning methods, several key limitations remain. First, most current approaches rely on…
The ancestor of Chinese character – the ancient characters from about 1300 BC to 200 BC are not fixed in their writing glyphs. At the same or different points in time, one character can possess multiple glyphs that are different in shapes or radicals. Nearly half of ancient glyphs have not been deci…
Text classification occupies an important role in natural language processing and has many applications in real life. Short text classification, as one of its subtopics, has attracted increasing interest from researchers since it is more challenging due to its semantic sparsity and insufficient labe…
Natural Language Inference (NLI) is a crucial task in natural language processing that involves determining the relationship between two sentences, typically referred to as the premise and the hypothesis. However, traditional NLI models solely rely on the semantic information inherent in independent…
Cross-domain text classification aims to transfer models from label-rich source domains to label-poor target domains, giving it a wide range of practical applications. Many approaches promote cross-domain generalization by capturing domaininvariant features. However, these methods rely on unlabeled…
Part-prototype Networks (ProtoPNets) are concept-based classifiers designed to achieve the same performance as black-box models without compromising transparency. ProtoPNets compute predictions based on similarity to class-specific part-prototypes learned to recognize parts of training examples, mak…
Ontology serves as the foundation for the underlying representation of knowledge. In order to achieve the sharing of knowledge across languages, ontologies that are typically only represented in English must be translated into different languages. Building a domain-specific translation system is nec…
Many models that leverage knowledge graphs (KGs) have recently demonstrated remarkable success in question answering (QA) tasks. In the real world, many facts contained in KGs are time-constrained thus temporal KGQA has received increasing attention. Despite the fruitful efforts of previous models i…
Interactive argument pair identification is an emerging research task for argument mining, aiming to identify whether two arguments are interactively related. It is pointed out that the context of the argument is essential to improve identification performance. However, current context-based methods…
Federated Learning (FL) is an emerging privacy-aware machine learning technique that applies successfully to the collaborative learning of global models for Human Activity Recognition (HAR). As of now, the applications of FL for HAR assume that the data associated with diverse individuals follow the…
Modern Chinese characters evolved from 3,000 years ago. Up to now, tens of thousands of glyphs of ancient characters have been discovered, which must be deciphered by experts to interpret unearthed documents. Experts usually need to compare each ancient character to be examined with similar known on…
Text classification is a fundamental task with broad applications in natural language processing. Recently, graph neural networks (GNNs) have attracted much attention due to their powerful representation ability. However, most existing methods for text classification based on GNNs consider only one-…
We tackle sequential learning under label noise in applications where a human supervisor can be queried to relabel suspicious examples. Existing approaches are flawed, in that they only relabel incoming examples that look "suspicious" to the model. As a consequence, those mislabeled examples that el…
We set out to uncover the unique grammatical properties of an important yet so far under-researched type of natural language text: that of short labels typically found within structured datasets. We show that such labels obey a specific type of abbreviated grammar that we call the Language of Data,…
The ability to learn from human supervision is fundamental for personal assistants and other interactive applications of AI. Two central challenges for deploying interactive learners in the wild are the unreliable nature of the supervision and the varying complexity of the prediction task. We addres…