19 citations · 40 across the 18 of their papers we have counts for
18 papers
Imagine Before You Draw: Visual Prompt Engineering for Image Generation
Liyu Jia, Fengda Zhang, Jiachun Pan +7
Incorporating visual semantic representations as an intermediate step before image generation can reduce the modeling difficulty between text and images, thereby improving generati…
Modeling Cross-vision Synergy for Unified Large Vision Model
Shengqiong Wu, Lanhu Wu, Mingyang Bao +5
Recent advances in large vision models (LVMs) have shifted from modality-specific designs toward unified architectures that jointly process images, videos, and 3D data. However, ex…
WEAVE: Unleashing and Benchmarking the In-context Interleaved Comprehension and Generation
Wei Chow, Jiachun Pan, Yongyuan Liang +10
Recent advances in unified multimodal models (UMMs) have enabled impressive progress in visual comprehension and generation. However, existing datasets and benchmarks focus primari…
Selftok: Discrete Visual Tokens of Autoregression, by Diffusion, and for Reasoning
Bohan Wang, Zhongqi Yue, Fengda Zhang +15
We completely discard the conventional spatial prior in image representation and introduce a novel discrete visual tokenizer: Self-consistency Tokenizer (Selftok). At its design co…
On Path to Multimodal Generalist: General-Level and General-Bench
Hao Fei, Yuan Zhou, Juncheng Li +29
The Multimodal Large Language Model (MLLM) is currently experiencing rapid growth, driven by the advanced capabilities of LLMs. Unlike earlier specialists, existing MLLMs are evolv…
Generative Multimodal Pretraining with Discrete Diffusion Timestep Tokens
Kaihang Pan, Wang Lin, Zhongqi Yue +6
Recent endeavors in Multimodal Large Language Models (MLLMs) aim to unify visual comprehension and generation by combining LLM and diffusion models, the state-of-the-art in each ta…
VistaDPO: Video Hierarchical Spatial-Temporal Direct Preference Optimization for Large Video Models
Haojian Huang, Haodong Chen, Shengqiong Wu +5
Large Video Models (LVMs) built upon Large Language Models (LLMs) have shown promise in video understanding but often suffer from misalignment with human intuition and video halluc…
Learning 4D Panoptic Scene Graph Generation from Rich 2D Visual Scene
Shengqiong Wu, Hao Fei, Jingkang Yang +4
The latest emerged 4D Panoptic Scene Graph (4D-PSG) provides an advanced-ever representation for comprehensively modeling the dynamic 4D visual real world. Unfortunately, current p…
Video-of-Thought: Step-by-Step Video Reasoning from Perception to Cognition
Hao Fei, Shengqiong Wu, Wei Ji +4
Existing research of video understanding still struggles to achieve in-depth comprehension and reasoning in complex videos, primarily due to the under-exploration of two key bottle…
Vitron: A Unified Pixel-level Vision LLM for Understanding, Generating, Segmenting, Editing
Hao Fei, Shengqiong Wu, Hanwang Zhang +2
Recent developments of vision large language models (LLMs) have seen remarkable progress, yet still encounter challenges towards multimodal generalists, such as coarse-grained inst…
HumanEdit: A High-Quality Human-Rewarded Dataset for Instruction-based Image Editing
Jinbin Bai, Wei Chow, Ling Yang +4
We present HumanEdit, a high-quality, human-rewarded dataset specifically designed for instruction-guided image editing, enabling precise and diverse image manipulations through op…
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…
Towards Semantic Equivalence of Tokenization in Multimodal LLM
Shengqiong Wu, Hao Fei, Xiangtai Li +4
Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in processing vision-language tasks. One of the crux of MLLMs lies in vision tokenization, which…
Auto-Encoding Morph-Tokens for Multimodal LLM
Kaihang Pan, Siliang Tang, Juncheng Li +6
For multimodal LLMs, the synergy of visual comprehension (textual output) and generation (visual output) presents an ongoing challenge. This is due to a conflicting objective: for…
Discriminative Probing and Tuning for Text-to-Image Generation
Leigang Qu, Wenjie Wang, Yongqi Li +3
Despite advancements in text-to-image generation (T2I), prior methods often face text-image misalignment problems such as relation confusion in generated images. Existing solutions…
Dysen-VDM: Empowering Dynamics-aware Text-to-Video Diffusion with LLMs
Hao Fei, Shengqiong Wu, Wei Ji +2
Text-to-video (T2V) synthesis has gained increasing attention in the community, in which the recently emerged diffusion models (DMs) have promisingly shown stronger performance tha…