activity
20212024
most citedPSG: Prompt-based Sequence Generation for Acronym Extraction

2 citations · 3 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2024

Agriculture-Vision Challenge 2024 -- The Runner-Up Solution for Agricultural Pattern Recognition via Class Balancing and Model Ensemble

Wang Liu, Zhiyu Wang, Puhong Duan +2

The Agriculture-Vision Challenge at CVPR 2024 aims at leveraging semantic segmentation models to produce pixel level semantic segmentation labels within regions of interest for mul…

eess.IV20241 cited

Modeling the Label Distributions for Weakly-Supervised Semantic Segmentation

Linshan Wu, Zhun Zhong, Jiayi Ma +4

Weakly-Supervised Semantic Segmentation (WSSS) aims to train segmentation models by weak labels, which is receiving significant attention due to its low annotation cost. Existing a…

cs.CV2024

GeReA: Question-Aware Prompt Captions for Knowledge-based Visual Question Answering

Ziyu Ma, Shutao Li, Bin Sun +3

Knowledge-based visual question answering (VQA) requires world knowledge beyond the image for accurate answer. Recently, instead of extra knowledge bases, a large language model (L…

eess.IV2023

Hyperspectral Image Fusion via Logarithmic Low-rank Tensor Ring Decomposition

Jun Zhang, Lipeng Zhu, Chao Wang +1

Integrating a low-spatial-resolution hyperspectral image (LR-HSI) with a high-spatial-resolution multispectral image (HR-MSI) is recognized as a valid method for acquiring HR-HSI.…

cs.CV2023

LOGO-Former: Local-Global Spatio-Temporal Transformer for Dynamic Facial Expression Recognition

Fuyan Ma, Bin Sun, Shutao Li

Previous methods for dynamic facial expression recognition (DFER) in the wild are mainly based on Convolutional Neural Networks (CNNs), whose local operations ignore the long-range…

cs.CL2022

Scene-Aware Prompt for Multi-modal Dialogue Understanding and Generation

Bin Li, Yixuan Weng, Ziyu Ma +2

This paper introduces the schemes of Team LingJing's experiments in NLPCC-2022-Shared-Task-4 Multi-modal Dialogue Understanding and Generation (MDUG). The MDUG task can be divided…