9 citations · 20 across the 7 of their papers we have counts for
7 papers
Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding
Deyi Ji, Lanyun Zhu, Siqi Gao +4
The ubiquity and value of tables as semi-structured data across various domains necessitate advanced methods for understanding their complexity and vast amounts of information. Des…
SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More
Tianrun Chen, Ankang Lu, Lanyun Zhu +7
The advent of large models, also known as foundation models, has significantly transformed the AI research landscape, with models like Segment Anything (SAM) achieving notable succ…
PPTFormer: Pseudo Multi-Perspective Transformer for UAV Segmentation
Deyi Ji, Wenwei Jin, Hongtao Lu +1
The ascension of Unmanned Aerial Vehicles (UAVs) in various fields necessitates effective UAV image segmentation, which faces challenges due to the dynamic perspectives of UAV-capt…
xLSTM-UNet can be an Effective 2D & 3D Medical Image Segmentation Backbone with Vision-LSTM (ViL) better than its Mamba Counterpart
Tianrun Chen, Chaotao Ding, Lanyun Zhu +5
Convolutional Neural Networks (CNNs) and Vision Transformers (ViT) have been pivotal in biomedical image segmentation, yet their ability to manage long-range dependencies remains c…
Discrete Latent Perspective Learning for Segmentation and Detection
Deyi Ji, Feng Zhao, Lanyun Zhu +3
In this paper, we address the challenge of Perspective-Invariant Learning in machine learning and computer vision, which involves enabling a network to understand images from varyi…
IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decoding
Lanyun Zhu, Deyi Ji, Tianrun Chen +3
Despite achieving rapid developments and with widespread applications, Large Vision-Language Models (LVLMs) confront a serious challenge of being prone to generating hallucinations…