1 citations · 1 across the 12 of their papers we have counts for
12 papers
VLNVerse: A Benchmark for Vision-Language Navigation with Versatile, Embodied, Realistic Simulation and Evaluation
Sihao Lin, Zerui Li, Xunyi Zhao +10
Despite remarkable progress in Vision-Language Navigation (VLN), existing benchmarks remain confined to fixed, small-scale datasets with naive physical simulation. These shortcomin…
NeuroPath: Neurobiology-Inspired Path Tracking and Reflection for Semantically Coherent Retrieval
Junchen Li, Rongzheng Wang, Yihong Huang +3
Retrieval-augmented generation (RAG) greatly enhances large language models (LLMs) performance in knowledge-intensive tasks. However, naive RAG methods struggle with multi-hop ques…
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…
Expanding the Action Space of LLMs to Reason Beyond Language
Zhongqi Yue, Weishi Wang, Yundaichuan Zhan +3
Large Language Models (LLMs) are powerful reasoners in natural language, but their actions are typically confined to outputting vocabulary tokens. As a result, interactions with ex…
TAlignDiff: Automatic Tooth Alignment assisted by Diffusion-based Transformation Learning
Yunbi Liu, Enqi Tang, Shiyu Li +5
Orthodontic treatment hinges on tooth alignment, which significantly affects occlusal function, facial aesthetics, and patients' quality of life. Current deep learning approaches p…
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…
RepText: Rendering Visual Text via Replicating
Haofan Wang, Yujia Xu, Yimeng Li +5
Although contemporary text-to-image generation models have achieved remarkable breakthroughs in producing visually appealing images, their capacity to generate precise and flexible…
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…
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…
SILMM: Self-Improving Large Multimodal Models for Compositional Text-to-Image Generation
Leigang Qu, Haochuan Li, Wenjie Wang +4
Large Multimodal Models (LMMs) have demonstrated impressive capabilities in multimodal understanding and generation, pushing forward advancements in text-to-image generation. Howev…
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…