5 citations · 14 across the 5 of their papers we have counts for
5 papers
MInD: Improving Multimodal Sentiment Analysis via Multimodal Information Disentanglement
Weichen Dai, Xingyu Li, Zeyu Wang +4
Learning effective joint representations has been a central task in multi-modal sentiment analysis. Previous works addressing this task focus on exploring sophisticated fusion tech…
Tree-of-Mixed-Thought: Combining Fast and Slow Thinking for Multi-hop Visual Reasoning
Pengbo Hu, Ji Qi, Xingyu Li +5
There emerges a promising trend of using large language models (LLMs) to generate code-like plans for complex inference tasks such as visual reasoning. This paradigm, known as LLM-…
Boosting Multi-modal Model Performance with Adaptive Gradient Modulation
Hong Li, Xingyu Li, Pengbo Hu +3
While the field of multi-modal learning keeps growing fast, the deficiency of the standard joint training paradigm has become clear through recent studies. They attribute the sub-o…
Cross-view Geo-localization via Learning Disentangled Geometric Layout Correspondence
Xiaohan Zhang, Xingyu Li, Waqas Sultani +2
Cross-view geo-localization aims to estimate the location of a query ground image by matching it to a reference geo-tagged aerial images database. As an extremely challenging task,…
DDDM: a Brain-Inspired Framework for Robust Classification
Xiyuan Chen, Xingyu Li, Yi Zhou +1
Despite their outstanding performance in a broad spectrum of real-world tasks, deep artificial neural networks are sensitive to input noises, particularly adversarial perturbations…