6 citations · 6 across the 6 of their papers we have counts for
7 papers
ULME: Predictive Coupling for Mutually Reinforcing Embedding and Language Modeling
Yang Liu, Zhiqiang Yuan, Shu-Dong Huang +5
Text embedding and text generation are two of the most useful capabilities of modern large language models, yet they are typically provided by separate specialized models (an embed…
Intra-Modal Neighbors Never Lie: Rectifying Inter-Modal Noisy Correspondence via Graph-Based Intra-Modal Reasoning
Yang Liu, Wentao Feng, Shu-Dong Huang +2
Large-scale web-harvested datasets have fueled the progress of cross-modal retrieval but inevitably suffer from noisy correspondence, which severely degrades model generalization.…
EBAD-Gaussian: Event-driven Bundle Adjusted Deblur Gaussian Splatting
Yufei Deng, Yuanjian Wang, Rong Xiao +6
While 3D Gaussian Splatting (3D-GS) achieves photorealistic novel view synthesis, its performance degrades with motion blur. In scenarios with rapid motion or low-light conditions,…
Asymmetric Visual Semantic Embedding Framework for Efficient Vision-Language Alignment
Yang Liu, Mengyuan Liu, Shudong Huang +1
Learning visual semantic similarity is a critical challenge in bridging the gap between images and texts. However, there exist inherent variations between vision and language data,…
Aligning Information Capacity Between Vision and Language via Dense-to-Sparse Feature Distillation for Image-Text Matching
Yang Liu, Wentao Feng, Zhuoyao Liu +2
Enabling Visual Semantic Models to effectively handle multi-view description matching has been a longstanding challenge. Existing methods typically learn a set of embeddings to fin…
Multi-view Granular-ball Contrastive Clustering
Peng Su, Shudong Huang, Weihong Ma +2
Previous multi-view contrastive learning methods typically operate at two scales: instance-level and cluster-level. Instance-level approaches construct positive and negative pairs…
DrugLLM: Open Large Language Model for Few-shot Molecule Generation
Xianggen Liu, Yan Guo, Haoran Li +4
Large Language Models (LLMs) have made great strides in areas such as language processing and computer vision. Despite the emergence of diverse techniques to improve few-shot learn…