15 citations · 35 across the 6 of their papers we have counts for
6 papers
Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models
Haoran Wei, Lingyu Kong, Jinyue Chen +6
Modern Large Vision-Language Models (LVLMs) enjoy the same vision vocabulary -- CLIP, which can cover most common vision tasks. However, for some special vision task that needs den…
Merlin:Empowering Multimodal LLMs with Foresight Minds
En Yu, Liang Zhao, Yana Wei +8
Humans possess the remarkable ability to foresee the future to a certain extent based on present observations, a skill we term as foresight minds. However, this capability remains…
DreamLLM: Synergistic Multimodal Comprehension and Creation
Runpei Dong, Chunrui Han, Yuang Peng +11
This paper presents DreamLLM, a learning framework that first achieves versatile Multimodal Large Language Models (MLLMs) empowered with frequently overlooked synergy between multi…
ChatSpot: Bootstrapping Multimodal LLMs via Precise Referring Instruction Tuning
Liang Zhao, En Yu, Zheng Ge +8
Human-AI interactivity is a critical aspect that reflects the usability of multimodal large language models (MLLMs). However, existing end-to-end MLLMs only allow users to interact…
Logit Normalization for Long-tail Object Detection
Liang Zhao, Yao Teng, Limin Wang
Real-world data exhibiting skewed distributions pose a serious challenge to existing object detectors. Moreover, the samplers in detectors lead to shifted training label distributi…
Task-specific Inconsistency Alignment for Domain Adaptive Object Detection
Liang Zhao, Limin Wang
Detectors trained with massive labeled data often exhibit dramatic performance degradation in some particular scenarios with data distribution gap. To alleviate this problem of dom…