5 citations · 14 across the 15 of their papers we have counts for
13 papers · 1 filter
WiseEdit: Benchmarking Cognition- and Creativity-Informed Image Editing
Kaihang Pan, Weile Chen, Haiyi Qiu +6
Recent image editing models boast next-level intelligent capabilities, facilitating cognition- and creativity-informed image editing. Yet, existing benchmarks provide too narrow a…
What Limits Virtual Agent Application? OmniBench: A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities
Wendong Bu, Yang Wu, Qifan Yu +10
As multimodal large language models (MLLMs) advance, MLLM-based virtual agents have demonstrated remarkable performance. However, existing benchmarks face significant limitations,…
SOYO: A Tuning-Free Approach for Video Style Morphing via Style-Adaptive Interpolation in Diffusion Models
Haoyu Zheng, Qifan Yu, Binghe Yu +5
Diffusion models have achieved remarkable progress in image and video stylization. However, most existing methods focus on single-style transfer, while video stylization involving…
Boosting Virtual Agent Learning and Reasoning: A Step-Wise, Multi-Dimensional, and Generalist Reward Model with Benchmark
Bingchen Miao, Yang Wu, Minghe Gao +7
The development of Generalist Virtual Agents (GVAs) has shown significant promise in autonomous task execution. However, current training paradigms face critical limitations, inclu…
Mastering Collaborative Multi-modal Data Selection: A Focus on Informativeness, Uniqueness, and Representativeness
Qifan Yu, Zhebei Shen, Zhongqi Yue +7
Instruction tuning fine-tunes pre-trained Multi-modal Large Language Models (MLLMs) to handle real-world tasks. However, the rapid expansion of visual instruction datasets introduc…
STEP: Enhancing Video-LLMs' Compositional Reasoning by Spatio-Temporal Graph-guided Self-Training
Haiyi Qiu, Minghe Gao, Long Qian +7
Video Large Language Models (Video-LLMs) have recently shown strong performance in basic video understanding tasks, such as captioning and coarse-grained question answering, but st…
AnyEdit: Mastering Unified High-Quality Image Editing for Any Idea
Qifan Yu, Wei Chow, Zhongqi Yue +7
Instruction-based image editing aims to modify specific image elements with natural language instructions. However, current models in this domain often struggle to accurately execu…
Unified Generative and Discriminative Training for Multi-modal Large Language Models
Wei Chow, Juncheng Li, Qifan Yu +7
In recent times, Vision-Language Models (VLMs) have been trained under two predominant paradigms. Generative training has enabled Multimodal Large Language Models (MLLMs) to tackle…
Towards Unified Multimodal Editing with Enhanced Knowledge Collaboration
Kaihang Pan, Zhaoyu Fan, Juncheng Li +6
The swift advancement in Multimodal LLMs (MLLMs) also presents significant challenges for effective knowledge editing. Current methods, including intrinsic knowledge editing and ex…
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…
Dancing Avatar: Pose and Text-Guided Human Motion Videos Synthesis with Image Diffusion Model
Bosheng Qin, Wentao Ye, Qifan Yu +2
The rising demand for creating lifelike avatars in the digital realm has led to an increased need for generating high-quality human videos guided by textual descriptions and poses.…
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…
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,…