most citedSwiMDiff: Scene-wide Matching Contrastive Learning with Diffusion Constraint for Remote Sensing Image

1 citations · 4 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2024

DiffCLIP: Few-shot Language-driven Multimodal Classifier

Jiaqing Zhang, Mingxiang Cao, Xue Yang +2

Visual language models like Contrastive Language-Image Pretraining (CLIP) have shown impressive performance in analyzing natural images with language information. However, these mo…

cs.LG20241 cited

Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation

Tingjia Shen, Hao Wang, Jiaqing Zhang +5

Cross-Domain Sequential Recommendation (CDSR) aims to mine and transfer users' sequential preferences across different domains to alleviate the long-standing cold-start issue. Trad…

cs.CV20241 cited

Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior

Yidan Liu, Weiying Xie, Kai Jiang +3

The majority of existing hyperspectral anomaly detection (HAD) methods use the low-rank representation (LRR) model to separate the background and anomaly components, where the anom…

cs.CV20241 cited

Multimodal Informative ViT: Information Aggregation and Distribution for Hyperspectral and LiDAR Classification

Jiaqing Zhang, Jie Lei, Weiying Xie +3

In multimodal land cover classification (MLCC), a common challenge is the redundancy in data distribution, where irrelevant information from multiple modalities can hinder the effe…

cs.CV20241 cited

SwiMDiff: Scene-wide Matching Contrastive Learning with Diffusion Constraint for Remote Sensing Image

Jiayuan Tian, Jie Lei, Jiaqing Zhang +2

With recent advancements in aerospace technology, the volume of unlabeled remote sensing image (RSI) data has increased dramatically. Effectively leveraging this data through self-…

cs.CV2024

Distribution-aware Interactive Attention Network and Large-scale Cloud Recognition Benchmark on FY-4A Satellite Image

Jiaqing Zhang, Jie Lei, Weiying Xie +3

Accurate cloud recognition and warning are crucial for various applications, including in-flight support, weather forecasting, and climate research. However, recent deep learning a…