29 citations · 116 across the 52 of their papers we have counts for
8 papers · 2 filters
HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data
Qifan Yu, Juncheng Li, Longhui Wei +6
Multi-modal Large Language Models (MLLMs) tuned on machine-generated instruction-following data have demonstrated remarkable performance in various multi-modal understanding and ge…
De-fine: Decomposing and Refining Visual Programs with Auto-Feedback
Minghe Gao, Juncheng Li, Hao Fei +7
Visual programming, a modular and generalizable paradigm, integrates different modules and Python operators to solve various vision-language tasks. Unlike end-to-end models that ne…
Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative Instructions
Juncheng Li, Kaihang Pan, Zhiqi Ge +7
Recent advancements in Multimodal Large Language Models (MLLMs) have been utilizing Visual Prompt Generators (VPGs) to convert visual features into tokens that LLMs can recognize.…
Interactive Data Synthesis for Systematic Vision Adaptation via LLMs-AIGCs Collaboration
Qifan Yu, Juncheng Li, Wentao Ye +2
Recent text-to-image generation models have shown promising results in generating high-fidelity photo-realistic images. In parallel, the problem of data scarcity has brought a grow…
InstructVid2Vid: Controllable Video Editing with Natural Language Instructions
Bosheng Qin, Juncheng Li, Siliang Tang +2
We introduce InstructVid2Vid, an end-to-end diffusion-based methodology for video editing guided by human language instructions. Our approach empowers video manipulation guided by…
Visually-Prompted Language Model for Fine-Grained Scene Graph Generation in an Open World
Qifan Yu, Juncheng Li, Yu Wu +3
Scene Graph Generation (SGG) aims to extract <subject, predicate, object> relationships in images for vision understanding. Although recent works have made steady progress on SGG,…
Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models
Juncheng Li, Minghe Gao, Longhui Wei +7
Prompt tuning, a recently emerging paradigm, enables the powerful vision-language pre-training models to adapt to downstream tasks in a parameter -- and data -- efficient way, by l…
Variational Cross-Graph Reasoning and Adaptive Structured Semantics Learning for Compositional Temporal Grounding
Juncheng Li, Siliang Tang, Linchao Zhu +5
Temporal grounding is the task of locating a specific segment from an untrimmed video according to a query sentence. This task has achieved significant momentum in the computer vis…