10 accepted papers
Developing a universal graph model capable of generalizing across diverse graph domains has consistently been a key objective in graph learning. Recently, many studies have focused on achieving in-context learning (ICL) on graphs, which can generalize to novel tasks without the need for fine-tuning,
Integrating large language models (LLMs) into text-to-speech (TTS) systems has improved speech expressiveness, yet controllable emotional expression remains challenging. Existing approaches primarily rely on external conditioning or global activation steering, offering limited insight into how emoti…
Nowadays, research of Text Classification (TC) based on graph neural networks (GNNs) is on the rise. Both inductive methods and transductive methods have made significant progress. For transductive methods, the semantic interaction between texts plays a crucial role in the learning of effective text…
Sentiment analysis of historical literature provides valuable insights for humanities research, yet remains challenging due to scarce annotations and limited generalization of models trained on modern texts. Prior work has primarily focused on two directions: using sentiment lexicons or leveraging l
Insect pests greatly affect the growth and harvest of crops, and accurate identification of insect species is very important to the agricultural industry. The insect dataset has the problem that the target is too small to locate, detect, and identify. To solve this problem, We designed a modified YO…
Point-based models have achieved impressive success in point cloud processing, but current methods mainly address issues such as disorder, sparsity, and rotation of point clouds. In addition to these characteristics, we also found that for the point set P (X<inf xmlns:mml="http://www.w3.org/1998/Mat…
In the field of computer vision, object detection is a prominent and challenging task. Despite the favorable performance of deep learning-based object detection techniques on clear images, it fails in inclement weather conditions like snow because of image degradation. Recent efforts have explored u…
Video snow removal is an important task in computer vision, as the snowflakes in videos reduce visibility and negatively affect the performance of outdoor visual systems. However, due to the complexity of real snowy scenarios, it is difficult to apply existing supervised learning-based methods to pr…
A point cloud is a geometric data structure that is a set of coordinates p(x, y, z) of points, with additional normal n(n <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</inf> , n <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http:/…
Although Convolutional Neural Networks (CNN) have made good progress in image restoration, the intrinsic equivalence and locality of convolutions still constrain further improvements in image quality. Recent vision transformer and selfattention have achieved promising results on various computer vis…