12 citations · 13 across the 6 of their papers we have counts for
7 papers
Focus on What Really Matters in Low-Altitude Governance: A Management-Centric Multi-Modal Benchmark with Implicitly Coordinated Vision-Language Reasoning Framework
Hao Chang, Zhihui Wang, Lingxiang Wu +5
Low-altitude vision systems are becoming a critical infrastructure for smart city governance. However, existing object-centric perception paradigms and loosely coupled vision-langu…
PixCLIP: Achieving Fine-grained Visual Language Understanding via Any-granularity Pixel-Text Alignment Learning
Yicheng Xiao, Yu Chen, Haoxuan Ma +5
While the Contrastive Language-Image Pretraining(CLIP) model has achieved remarkable success in a variety of downstream vison language understanding tasks, enhancing its capability…
OpenS2S: Advancing Fully Open-Source End-to-End Empathetic Large Speech Language Model
Chen Wang, Tianyu Peng, Wen Yang +8
Empathetic interaction is a cornerstone of human-machine communication, due to the need for understanding speech enriched with paralinguistic cues and generating emotional and expr…
Enhancing Text-to-SQL Capabilities of Large Language Models via Domain Database Knowledge Injection
Xingyu Ma, Xin Tian, Lingxiang Wu +3
Text-to-SQL is a subtask in semantic parsing that has seen rapid progress with the evolution of Large Language Models (LLMs). However, LLMs face challenges due to hallucination iss…
AnyDesign: Versatile Area Fashion Editing via Mask-Free Diffusion
Yunfang Niu, Dong Yi, Lingxiang Wu +2
Fashion image editing aims to modify a person's appearance based on a given instruction. Existing methods require auxiliary tools like segmenters and keypoint extractors, lacking a…
Recurrent Context Compression: Efficiently Expanding the Context Window of LLM
Chensen Huang, Guibo Zhu, Xuepeng Wang +5
To extend the context length of Transformer-based large language models (LLMs) and improve comprehension capabilities, we often face limitations due to computational resources and…
Enhancing Chain of Thought Prompting in Large Language Models via Reasoning Patterns
Yufeng Zhang, Xuepeng Wang, Lingxiang Wu +1
Chain of Thought (CoT) prompting can encourage language models to engage in multi-step logical reasoning. The quality of the provided demonstrations significantly influences the su…